Journal articles: 'Kalman filter based tracking loop'

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Klokov, Andrey, Motayam Kanouj, and Aleksandr Mironchev. "A Novel Carrier Tracking Approach for GPS Signals Based on Gauss–Hermite Kalman Filter." Electronics 11, no. 14 (July 15, 2022): 2215. http://dx.doi.org/10.3390/electronics11142215.

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In a conventional GPS receiver, the carrier tracking system is the key stage that keeps the receiver locked to the radio navigation parameters (RNPs) of the received signal. The most commonly used approaches to the tracking system are phase lock loop (PLL), frequency lock loop (FLL), and FLL-assisted PLL. The main limitation of the above approaches is that their performance deteriorates when working with weak signals and in harsh environments. In recent years, Kalman filter (KF)-based tracking loop architectures have gained increased attention due to their robust and better performance compared with conventional architectures. In this paper, a novel Gauss–Hermite Kalman filtering-based carrier tracking algorithm is proposed for static and moving receivers with weak GPS signals. The performance of the proposed algorithm is compared with two other approaches: extended Kalman filter (EKF) and unscented Kalman filter (UKF). Simulations were conducted using a software-defined GPS simulator and software device radio (SDR) modules. A comparative analysis of the tracking methods demonstrated that the proposed tracking method shows a better performance and improves the tracking sensitivity and capability under weak signal conditions as compared with EKF- and UKF-based tracking methods. In addition, the results show that the proposed approach improves the Doppler frequency measurement accuracy under dynamic operation conditions.

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Luo, Yu, Yong-qing Wang, Hai-kun Luo, Yuan-xing Ma, and Si-liang Wu. "Study on Vector Tracking Loop Based on Extended Kalman Filter." Journal of Electronics & Information Technology 35, no. 6 (February 17, 2014): 1400–1405. http://dx.doi.org/10.3724/sp.j.1146.2012.00828.

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Cheng, Yan, Qing Chang, Hao Wang, and Xianxu Li. "A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals." Sensors 19, no. 6 (March 19, 2019): 1369. http://dx.doi.org/10.3390/s19061369.

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For global navigation satellite system receivers, Kalman filter (KF)-based tracking loops show remarkable advantages in terms of tracking sensitivity and robustness compared with conventional tracking loops. However, to improve the tracking sensitivity further, increasing the coherent integration time is necessary, but it is typically limited by the navigation data bit sign transition. Moreover, for standard KF-based tracking receivers, the KF parameters are initialized by the acquired results. However, especially under weak signal conditions, the acquired results have frequency errors that are too large for KF-based tracking to converge rapidly to a steady state. To solve these problems, a two-stage KF-based tracking architecture is proposed to track weaker signals and achieve faster convergence. In the first stage, coarse tracking refines the acquired results and achieves bit synchronization. Then, in the second stage, fine tracking initializes the KF-based tracking by using the coarse tracking results and extends the coherent integration time without the bit sign transition limitation. This architecture not only utilizes the self-tuning technique of the KF to improve the tracking sensitivity, but also adopts the two-stage to reduce the convergence time of the KF-based tracking. Simulation results demonstrate that the proposed method outperforms conventional tracking techniques in terms of tracking sensitivity. Furthermore, the proposed method is compared with the standard KF-based tracking approach, proving that the proposed method converges more rapidly.

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Ding, Ji Cheng, Lin Zhao, Jia Liu, and Shuai He Gao. "An Array Nonlinear Kalman Tracker for Indoor GPS Signal." Applied Mechanics and Materials 44-47 (December 2010): 3864–68. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3864.

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To implement indoor GPS signal tracking in standalone mode when the tracking loop is unlocked and data bit edge is unknown, the paper develops a modified Viterbi Algorithm (MVA) based on dynamic programming, and it was applied for GPS bit synchronization. Besides, two combination carrier tracking schemes based on Central Difference Kalman Filter (CDKF) and MVA module were designed for indoor GPS signal. The testing results indicate that the methods can successful detect bit edge position with high detection probability whether or not the tracking loop is locked. The co-operational tracking scheme is still able to perform when the signal quality deteriorate.

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Zou, Xiaojun, Baowang Lian, and Zesheng Dan. "Vector Tracking Algorithm Based on Adaptive Cubature Kalman Filter." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 6 (December 2018): 1108–15. http://dx.doi.org/10.1051/jnwpu/20183661108.

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In the vector tracking loop, there is a great error in the output of discriminator owing to the disturbance of noise. Cubature Kalman filter is proposed to replace the discriminator to process I/Q data and generate code phase error and the carrier frequency error in this paper. The present algorithm not only can avoid the nonlinear problem of discriminator, but also can reduce the bad effect of noise. Moreover, using cubature Kalman filter to deal with the nonlinear I/Q data is beneficial to preserve the accuracy of data processing. Because noise is unknown or time-varying, the filter should have the ability to respond to the changes of environmental noise. The innovation of measurements is used to estimate the covariance matrix of measurement noise in real time. Finally, a comparison is carried out between the present algorithm and the vector tracking algorithm based on discriminator. The test results show that the code phase error and the carrier frequency error are smaller, and the accuracy of navigation solution is also higher.

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Zhao, Sihao, Mingquan Lu, and Zhenming Feng. "Implementation and Performance Assessment of a Vector Tracking Method Based on a Software GPS Receiver." Journal of Navigation 64, S1 (October 14, 2011): S151—S161. http://dx.doi.org/10.1017/s0373463311000440.

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A number of methods have been developed to enhance the robustness of Global Positioning System (GPS) receivers when there are a limited number of visible satellites. Vector tracking is one of them. It utilizes information from all channels to aid the processing of individual channels to generate receiver positions and velocities. This paper analyzes relationships among code phase, carrier frequency, and receiver position and velocity, and presents a vector loop-tracking algorithm using an Extended Kalman filter implemented in a Matlab-based GPS software receiver. Simulated GPS signals are generated to test the proposed vector tracking method. The results show that when some of the satellites are blocked, the vector tracking loop provides better carrier frequency tracking results for the blocked signals and produces more accurate navigation solutions compared with traditional scalar tracking loops.

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Yuan, Cao, Ma Lianchuan, and Weigang Ma. "Mobile Target Tracking Based on Hybrid Open-Loop Monocular Vision Motion Control Strategy." Discrete Dynamics in Nature and Society 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/690576.

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This paper proposes a new real-time target tracking method based on the open-loop monocular vision motion control. It uses the particle filter technique to predict the moving target’s position in an image. Due to the properties of the particle filter, the method can effectively master the motion behaviors of the linear and nonlinear. In addition, the method uses the simple mathematical operation to transfer the image information in the mobile target to its real coordinate information. Therefore, it requires few operating resources. Moreover, the method adopts the monocular vision approach, which is a single camera, to achieve its objective by using few hardware resources. Firstly, the method evaluates the next time’s position and size of the target in an image. Later, the real position of the objective corresponding to the obtained information is predicted. At last, the mobile robot should be controlled in the center of the camera’s vision. The paper conducts the tracking test to the L-type and the S-type and compares with the Kalman filtering method. The experimental results show that the method achieves a better tracking effect in the L-shape experiment, and its effect is superior to the Kalman filter technique in the L-type or S-type tracking experiment.

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Chen, Shaohua, and Yang Gao. "Improvement of Carrier Phase Tracking Based on a Joint Vector Architecture." International Journal of Aerospace Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/9682875.

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Carrier phase measurements are essential to high precision positioning. Usually, the carrier phase measurements are generated from the phase lock loop in a conventional Global Navigation Satellite System (GNSS) receiver. However there is a dilemma problem to the design of the loop parameters in a conventional tracking loop. To address this problem and improve the carrier phase tracking sensitivity, a carrier phase tracking method based on a joint vector architecture is proposed. The joint vector architecture contains a common loop based on extended Kalman filter to track the common dynamics of the different channels and the individual loops for each channel to track the satellite specific dynamics. The transfer function model of the proposed architecture is derived. The proposed method and the conventional scalar carrier phase tracking are tested with a high quality simulator. The test results indicate that carrier phase measurements of satellites start to show cycle slips using the proposed method when carrier noise ratio is equal to and below 15 dB-Hz instead of 21 dB-Hz with using the conventional phase tracking loop. Since the joint vector based tracking loops jointly process the signals of all available satellites, the potential interchannel influence between different satellites is also investigated.

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Li, Na, Shufang Zhang, and Yi Jiang. "High Dynamic Weak Signal Tracking Algorithm of a Beidou Vector Receiver Based on an Adaptive Square Root Cubature Kalman Filter." Sensors 21, no. 20 (October 9, 2021): 6707. http://dx.doi.org/10.3390/s21206707.

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Compared with a scalar tracking receiver, the Beidou vector tracking receiver has the advantages of smaller tracking errors, fast loss-of-lock reacquisition, and high stability. However, in extremely challenging conditions, such as highly dynamic and weak signals, the loop will exhibit a high degree of nonlinearity, and observations with gross errors and large deviations will reduce the positioning accuracy and stability. In view of this situation, based on the concepts of cubature Kalman filtering and square root filtering, a square root cubature Kalman filtering (SRCKF) algorithm is given. Then, combining this algorithm with the concept of covariance matching based on an innovation sequence, an adaptive square root cubature Kalman filter (ASRCKF) algorithm is proposed. The algorithm was verified, and the tracking performance of the vector locking loop (VLL) realized by the algorithm was compared with the SRCKF VLL and the ASRCKF scalar locking loop (SLL). The simulation results show that, regardless of whether in a highly dynamic weak signal environment or in a general situation where the signal-to-noise ratio is higher than the tracking threshold, the tracking accuracy and stability of the ASRCKF VLL are higher than those of the SRCKF VLL and the ASRCKF SLL, the three-dimensional position error of the ASRCKF VLL does not exceed 36 m, and the three-dimensional velocity error does not exceed 3.5 m/s.

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Luo, Y., C. Yu, J. Li, and N. El-Sheimy. "PERFORMANCE OF GNSS CARRIER-TRACKING LOOP BASED ON KALMAN FILTER IN A CHALLENGING ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1687–93. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1687-2019.

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Abstract. The global navigation satellite system (GNSS) recently plays an extremely important role in positioning, navigation, and timing (PNT) applications for the modernized automations and mechanizations, e.g., unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), military aircrafts, etc. Nevertheless, GNSS signals are very vulnerable to the influence of various interferences when they are received on Earth, and the reason why it happens is that the long line-of-sight (LOS) distance between the satellite and the receiver user dramatically reduces the power strength after the signal reaches at the ground. The weak GNSS signal is hard to be handled with traditional phase lock loop (PLL), especially in a dynamic environment. Again, the trade-off among the coherent integration time of tracking loop, received signal power strength, and signal or user receiver dynamics is still a tough and remained problem to be solved. The Kalman filter (KF) is always a promising tool to efficiently decrease the random noise for the tracking process. In our work, we evaluate the performances of the tracking loop modelled with both standard KF and extended Kalman filter (EKF). An adaptive algorithm for the covariance matrix of the process noise is contained in our system to increase the tracking ability in a weak and dynamic environment. Besides, a noise channel is also contained to automatically adjust the priori measurement covariance for the KF tracking loop model. Simulation results demonstrate the performance with the proposed technique.

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Li, Na, Shufang Zhang, and Yi Jiang. "GPS Receiver VFLL-Assisted PLL High Dynamic Weak Signal Tracking Based on a Maximum Likelihood Estimation." Applied Sciences 12, no. 24 (December 15, 2022): 12907. http://dx.doi.org/10.3390/app122412907.

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This paper proposes a GPS receiver vector frequency-locked loop-assisted phase-locked loop (VFAPLL) structure based on the maximum likelihood estimation (MLE) method for highly dynamic weak-signal scenarios. In this structure, the loop structure does not include a frequency discriminator, and the signal is directly input to the navigation filter after down-conversion, coherent integration, and other processing to avoid nonlinear noise error. Due to the high dimension and nonlinearity of the cost function of the MLE algorithm, the Levenberg Marquardt (LM) algorithm is used to optimize it. The proposed VFAPLL is compared with the VFAPLL implemented based on the extended Kalman filter (EKF) algorithm and the frequency locked loop assisted phase locked loop (FAPLL) implemented based on MLE. Through simulation verification, it was shown that the VFAPLL (MLE) has higher tracking accuracy, lower loss-of-lock threshold, and better robustness to the input signal than the other two loops.

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HUANG He, 黄鹤, 张会生 ZHANG Hui-sheng, 黄莺 HUANG Ying, 许家栋 XU Jia-dong, and 徐剑 XU Jian. "A Loop-Template Matching Algorithm for Target Tracking Based on Kalman Filter." ACTA PHOTONICA SINICA 39, no. 2 (2010): 346–49. http://dx.doi.org/10.3788/gzxb20103902.0346.

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Han, Zhifeng, and Zheng Fang. "A filter algorithm for receiver tracking loops assisted by inertial information." Journal of Navigation 75, no. 2 (December 14, 2021): 496–506. http://dx.doi.org/10.1017/s0373463321000916.

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AbstractIn traditional satellite navigation receivers, the parameters of tracking loop such as loop bandwidth and integration time are usually set in the design of the receivers according to different scenarios. The signal tracking performance is limited in traditional receivers. In addition, when the tracking ability of weak signals is improved by extending the integration time, negative effect of residual frequency error becomes more and more serious with extension of the integration time. To solve these problems, this paper presents out research on receiver tracking algorithms and proposes an optimised tracking algorithm with inertial information. The receiver loop filter is designed based on Kalman filter, reducing the phase jitter caused by thermal noise in the weak signal environment and improving the signal tracking sensitivity. To confirm the feasibility of the proposed algorithm, simulation tests are conducted.

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Tang, Xinhua, Gianluca Falco, Emanuela Falletti, and Letizia Lo Presti. "Theoretical analysis and tuning criteria of the Kalman filter-based tracking loop." GPS Solutions 19, no. 3 (September 23, 2014): 489–503. http://dx.doi.org/10.1007/s10291-014-0408-2.

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Nandi, Amit Kumar, Palash Poddar, Randhir Kumar, and Sameer Kumar Devarakonda. "A REAL-TIME AUTONOMOUS FACE-TRACKING SYSTEM BASED ON A 2-DOF ARTICULATED MANIPULATOR PLATFORM USING EXTENDED KALMAN FILTER." Acta Mechanica Malaysia 4, no. 2 (September 9, 2021): 40–43. http://dx.doi.org/10.26480/amm.02.2021.40.43.

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There is an increasing demand for autonomous tracking applications in the industrial context which ranges from driver monitoring in semi-autonomous vehicles to human-robot interaction (HRI) to facilitate situational awareness in collaborative robots. In order to address the same, a system to track the human face in real-time has been developed and the system is capable of moves accordingly so that the face always remains in the range of visibility of the autonomous system. The system consists of open-source hardware and software to design the Tracking Algorithms which utilizes the Extended Kalman Filters (EKF) at its core. In addition to the basic model, this paper uses a hybrid model, implemented using both Extended Kalman Filters and Viola Jones in conjunction with Iterative Learning Control (ILC) intelligent tuning of PID loop. Performance evaluation of the system has been done in Solidworks and MATLAB. The proposed model with two different control methodologies along with the modified Extended Kalman Filter and Viola Jones Based algorithm has a shorter delay time and produced stable responses over traditional viola jones wavelets based approach.

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Cortés, Iñigo, Johannes Rossouw van der Merwe, Elena Simona Lohan, Jari Nurmi, and Wolfgang Felber. "Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter." Sensors 22, no. 2 (January 6, 2022): 420. http://dx.doi.org/10.3390/s22020420.

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This paper evaluates the performance of robust adaptive tracking techniques with the direct-state Kalman filter (DSKF) used in modern digital global navigation satellite system (GNSS) receivers. Under the assumption of a well-known Gaussian distributed model of the states and the measurements, the DSKF adapts its coefficients optimally to achieve the minimum mean square error (MMSE). In time-varying scenarios, the measurements’ distribution changes over time due to noise, signal dynamics, multipath, and non-line-of-sight effects. These kinds of scenarios make difficult the search for a suitable measurement and process noise model, leading to a sub-optimal solution of the DSKF. The loop-bandwidth control algorithm (LBCA) can adapt the DSKF according to the time-varying scenario and improve its performance significantly. This study introduces two methods to adapt the DSKF using the LBCA: The LBCA-based DSKF and the LBCA-based lookup table (LUT)-DSKF. The former method adapts the steady-state process noise variance based on the LBCA’s loop bandwidth update. In contrast, the latter directly relates the loop bandwidth with the steady-state Kalman gains. The presented techniques are compared with the well-known state-of-the-art carrier-to-noise density ratio (C/N0)-based DSKF. These adaptive tracking techniques are implemented in an open software interface GNSS hardware receiver. For each implementation, the receiver’s tracking performance and the system performance are evaluated in simulated scenarios with different dynamics and noise cases. Results confirm that the LBCA can be successfully applied to adapt the DSKF. The LBCA-based LUT-DSKF exhibits superior static and dynamic system performance compared to other adaptive tracking techniques using the DSKF while achieving the lowest complexity.

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Deng, Xiwen, Zhongliang Deng, Jingrong Liu, and Zhichao Zhang. "A Novel Carrier Loop Based on Coarse-to-Fine Weighted Adaptive Kalman Filter for Weak Communication-Positioning Integrated Signal." Sensors 22, no. 11 (May 27, 2022): 4068. http://dx.doi.org/10.3390/s22114068.

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We propose a communication-navigation integrated signal (CPIS), which is superimposed on the communication signal with power that does not affect the communication service, and realizes high-precision indoor positioning in a mobile communication network. Due to the occlusion of indoor obstacles and the power limitation of the positioning signal, existing carrier loop algorithms have large tracking errors in weak signal environments, which limits the positioning performance of the receiver in a complex environment. The carrier loop based on Kalman filtering (KF) has a good performance in respect of weak signals. However, the carrier frequency error of acquisition under weak signals is large, and the KF loop cannot converge quickly. Moreover, the KF algorithm based on fixed noise covariance increases or diverges in filtering error in complex environments. In this paper, a coarse-to-fine weighted adaptive Kalman filter (WAKF)-based carrier loop algorithm is proposed to solve the above problems of the receiver. In the coarse tracking stage, acquisition error reduction and bit synchronization are realized, and then a carrier loop based on Sage–Husa adaptive filtering is entered. Considering the shortcomings of the filter divergence caused by the negative covariance matrix of Sage–Husa in the filter update process, the weighted factor is given and UD decomposition is introduced to suppress the filtering divergence and improve the filtering accuracy. The simulation and actual environment test results show that the tracking sensitivity of the proposed algorithm is better than that based on the Sage–Husa adaptive filtering algorithm. In addition, compared with the weighted Sage–Husa AKF algorithm, the coarse-to-fine WAKF-based carrier loop algorithm converges faster.

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Zhu, Xuefen, Fei Shen, Jianfeng Chen, Yang Yang, Dongrui Yang, and Xiyuan Chen. "Combined Tracking Strategy Based on Unscented Kalman Filter for Global Positioning System L2C CM/CL Signal." Defence Science Journal 65, no. 5 (September 11, 2015): 395. http://dx.doi.org/10.14429/dsj.65.8725.

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In a global positioning system receiver, the tracking algorithm plays a dominant role since the code delay and Doppler frequency shift need to be accurately estimated as well as their variation over time need to be continuously updated. Combine unscented Kalman filter (UKF) with CM/CL signal to improve the signal tracking precision is proposed. It allow weighting assignment between CM code and CL code incoming signal, masked by a mass of noise, and to describe a UKF tracking loop aiming at decreasing numerical errors. UKF here involves state and measuring equations which calculate absolute offsets to adjust initial code and carrier phase then dramatically decrease the tracking error. In particular, the algorithm is implemented in both open space and jammed environment to highlight the advantages of tracking approach, by comparing single code and combined code, UKF and EKF tracking loop. It proves that signal tracking based on UKF, with low energy dissipation as well as high precision, is particularly appealing for a software receiver implementation.

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Xiaoyun, Li, and Du Wei. "Reconfigurable Fault Tolerant Control for Spacecraft Based on Modified IMM Algorithm." MATEC Web of Conferences 160 (2018): 05009. http://dx.doi.org/10.1051/matecconf/201816005009.

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This paper proposes an integrated fault detection, diagnosis, and reconfigurable control method for attitude tracking of a spacecraft. A novel IMM algorithm, based on the unscented Kalman filter and an index related to the closed-loop system performance, is presented to detect and diagnose the faults. To achieve steady attitude tracking, the sliding mode variable structure controller is designed. When a fault is detected and isolated, the controller structure is reconfigured to compensate the faulty system to maintain the system performance. A simulation example evaluating the attitude tracking process is employed, which demonstrates the efficiency of the proposed approach.

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Ding, Jicheng, Guoli Zhang, and Lin Zhao. "Urban and Indoor Weak Signal Tracking Using an Array Tracker with MVA and Nonlinear Filtering." Journal of Applied Mathematics 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/107156.

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We focus on the need for weak GPS signal tracking technique at a receiver powered on in urban or indoor environment; the tracking loop is unlocked and data bit edge position is unknown. A modified Viterbi algorithm (MVA) based on dynamic programming is developed and it is applied to GPS bit synchronization to improve bit edge position detection probability. Meanwhile, two combination carrier tracking schemes based on central difference Kalman filter (CDKF) and MVA module are designed for tracking very weak GPS signal. The testing results indicate that the methods can successfully detect bit edge position with high detection probability whether or not the tracking loop is locked. The tested combination tracking scheme is still able to work well when the signal quality deteriorates to 20 dB-Hz without additional large store space.

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Tian, Tian, Jian-ping An, and Ai-hua Wang. "Non-data-aided Extended Kalman Filter Based Carrier Tracking Loop in High Dynamic Environment." Journal of Electronics & Information Technology 35, no. 1 (February 17, 2014): 63–67. http://dx.doi.org/10.3724/sp.j.1146.2012.00012.

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Wang, Yang, Rong Yang, and Y. Jade Morton. "Kalman Filter-Based Robust Closed-Loop Carrier Tracking of Airborne GNSS Radio-Occultation Signals." IEEE Transactions on Aerospace and Electronic Systems 56, no. 5 (October 2020): 3384–93. http://dx.doi.org/10.1109/taes.2020.2972248.

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Wang, Xin Rong, Min Tao, and Wen Jian Geng. "Application of the Kalman Filter in the Rate Gyroscopes of Ship-Borne Servo System." Advanced Materials Research 765-767 (September 2013): 2613–16. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2613.

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The gyro stabilization loop is very important part in the Ship-borne Servo system to keep stable tracking. Based on the controlling structure of the gyro stabilization loop of Ship-borne Servo system, the theory of stabilizing the ships shaking is introduced and the signal of Gyroscopes is analyzed. The Kalman filter used in the gyro stabilization loop is put forward based on the famous Singer Model. The simulation on the real measured data is carried on. The result of simulation shows that this method can highly decrease random error in the Gyroscopes output and the ability of isolating the ships shaking can be improved more.

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A. Al-Isawi, Malik M., Adnan J. Attiya, and Julius O. ADOGHE. "UAV Control Based on Dual LQR and Fuzzy-PID Controller." Al-Khwarizmi Engineering Journal 16, no. 3 (September 1, 2020): 43–53. http://dx.doi.org/10.22153/kej.2020.08.001.

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This paper presents the design of a longitudinal controller for an autonomous unmanned aerial vehicle (UAV). This paper proposed the dual loop (inner-outer loop) control based on the intelligent algorithm. The inner feedback loop controller is a Linear Quadratic Regulator (LQR) to provide robust (adaptive) stability. In contrast, the outer loop controller is based on Fuzzy-PID (Proportional, Integral, and Derivative) algorithm to provide reference signal tracking. The proposed dual controller is to control the position (altitude) and velocity (airspeed) of an aircraft. An adaptive Unscented Kalman Filter (AUKF) is employed to track the reference signal and is decreased the Gaussian noise. The mathematical model of aircraft has been (Cessna 172) presented. The stability and robustness of the system have been verified in a simulation experiment.

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Onnen, D., G. C. Larsen, W. H. Lio, J. Y. Liew, M. Kühn, and V. Petrović. "Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering." Journal of Physics: Conference Series 2265, no. 2 (May 1, 2022): 022024. http://dx.doi.org/10.1088/1742-6596/2265/2/022024.

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Abstract In a wind farm setting, the location of the wake to which a downwind turbine is exposed is of high interest. It can be used for closed-loop active wake control, ultimately leading to fatigue load reduction and higher power generation. This work proposes a method for dynamic tracking of the meandering wake centre. The rotor takes the role of a sensor, with its blades sampling through the incoming wind field. The measurement of the flapwise blade root bending moment and its decomposition into the non-rotating yaw and tilt moments is used. The latter are linked to the lateral and vertical wake location via a parametric model, tuned with training data from aeroelastic simulations. The implementation of an Extended Kalman Filter (EKF) adds robustness to the tracking and allows to include physical knowledge of the wake meandering to the estimation. For this, the governing equations of the dynamic wake meandering model (DWM) are used to describe the meandering motion as a random walk process. The performance, possibilities and limitations of the tracking method in various inflow conditions are shown and discussed. Generally the wake tracking works satisfying, with estimation errors below 10% of the rotor diameter under moderate turbulence intensity. The Extended Kalman Filter formulation provides the confidence in the tracked wake position. The work shows how this can be effectively used for wake impingement detection.

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Borovitsky, D. S., A. E. Zhesterev, V. P. Ipatov, and R. M. Mamchur. "SATELLITE ALTIMETER DATA FILTERING AND SMOOTHING IN THE COURSE OF GROUND-BASED RETRACKING." Journal of the Russian Universities. Radioelectronics, no. 2 (April 24, 2019): 13–21. http://dx.doi.org/10.32603/1993-8985-2019-22-2-13-21.

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Introduction. Satellite radar altimeter is an essential part of the Earth remote sensing space missions. Satellite altimeter on-board delay-lock loop, by a widely shared concept, is operationally just a tool of a reliable retaining of received echo-signal within the tracking window, while “fine” altimetric parameter (orbit height, significant wave height, scattering cross section per unit of a probed surface, etc.) measuring is committed to the ground-based retracking of data. In particular, in the course of retracking altimeter data are being filtered and/or smoothed.Objective. The paper subject is study of retracking algorithms of altimeter data transmitted from the space vehicle to the ground segment.Methods and materials. It is known that data filtering already presents on-board the space vehicle and is implemented in delay-lock loop based on the α–β-filter. However, at the stage of ground-based retracking it seems more appropriate to use the Kalman filter, which possesses a number of theoretical optimal features and is efficient as for utilization of the available computational resource.Results and conclusions. In the paper implementation of filtering and smoothing via Kalman algorithm is described. On the ground of computer simulation data it is stated that Kalman filtering and smoothing make estimate accuracy two and more times higher depending on significant wave height.

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Kanouj, M. M., A. V. Klokov, G. N. Parvatov, and A. I. Potekaev. "The modification of Kalman filterbased carrier tracking loop for GPS signals in dynamic conditions and high noise levels." Izvestiya vysshikh uchebnykh zavedenii. Fizika 64, no. 1 (2021): 134–44. http://dx.doi.org/10.17223/00213411/64/1/134.

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This paper discusses the possibility of creating an automated positioning system, including various search objects by means of engineering intelligence, based on the methodology of tracking GPS signals. The traditional tracking methodology is analyzed and a more efficient one is proposed based on a modification of the Kalman filter for environments with low signal-to-noise ratio and in high user dynamic conditions. To achieve tracking of the GPS signals, the data is processed using MATLAB program. A comparative analysis showed that the proposed tracking method improves the tracking performance by 7 dB compared to the traditional tracking and overcomes bit synchronization losses. In addition, the proposed method improves the accuracy of Doppler frequency measurements under dynamic conditions.

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Tu, Zhiyong, Yidong Lou, Wenfei Guo, Weiwei Song, and Yusheng Wang. "Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments." Remote Sensing 13, no. 10 (May 20, 2021): 2000. http://dx.doi.org/10.3390/rs13102000.

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This paper designs a cascading vector tracking loop based on the Unscented Kalman Filter (UKF) for high dynamic environment. Constant improvement in dynamic performance is an enormous challenge to the traditional receiver. Due to the doppler effect, the satellite signals received by these vehicles contain fast changing doppler frequency shifts and the first and second derivatives of doppler frequency, which will directly cause a negative impact on the receiver’s stable tracking of the signals. In order to guarantee the dynamic performance and the tracking accuracy, this paper designs a vector carrier structure to estimate the doppler component of a signal. Firstly, after the coherence integral, the IQ values are reorganized into new observations. Secondly, the phase error and frequency of the carrier are estimated through the pre-filter. Then, the pseudorange and carrier frequency are used as the observations of the main filter to estimate the motion state of the aircraft. Finally, the current state is fed back to the carrier Numerical Controlled Oscillator (NCO) as a complete closed loop. In the whole structure, the cascading vector loop replaces the original carrier tracking loop, and the stable signal tracking of code loop is guaranteed by carrier assisted pseudo-code method. In this paper, with the high dynamic signals generated by the GNSS signal simulator, this designed algorithm is validated by a software receiver. The results show that this loop has a wider dynamic tracking range and lower tracking error than the second-order frequency locked loop assisted third-order phase locked loop in high dynamic circumstances. When the acceleration of carrier is 100 g, the convergence time of vector structure is about 100 ms, and the carrier phase error is lower than 0.6 mm.

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Zeng, Xiaohua, Tong Liu, Dafeng Song, Nannan Yang, and Haoyong Cui. "Extended Kalman filter–based and model predictive control–based dynamic coordinated control strategy for power-split hybrid electric bus." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 6 (December 23, 2019): 1623–33. http://dx.doi.org/10.1177/0954407019891750.

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The power-split hybrid electric vehicle achieves excellent fuel economy because both the engine speed and the torque of this system are decoupled from the road load. However, for a power-split hybrid electric vehicle with multiple power sources, the inconsistency of the response characteristic of each power source seriously affects the stability control of the power system and riding comfort, so the coordinated control of the power system is particularly important. This article proposed a dynamic coordinated control strategy. First, extended Kalman filter is applied to realize robust online estimation of the engine dynamics. Then, an extended Kalman filter–based and model predictive control–based dynamic coordinated control strategy is designed to achieve accurate reference tracking in hybrid electric mode. Considering the real-time performance for the online application of the dynamic coordinated control strategy, a fast model predictive control solver is formed based on a reasonable assumption. Offline simulation results show that accurate reference tracking is achieved in hybrid electric mode. Hardware-in-the-loop simulation is also conducted to validate the real-time performance of the proposed dynamic coordinated control strategy. This study is expected to improve the performance and robustness of the dynamic coordinated control strategy in hybrid electric mode while reducing the calibration load.

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Li, Qunsheng, and Yan Zhao. "An Innovative High-Precision Scheme for a GPS/MEMS-SINS Ultra-Tight Integrated System." Sensors 19, no. 10 (May 17, 2019): 2291. http://dx.doi.org/10.3390/s19102291.

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The Doppler-assisted error provided by a low-precision microelectromechanical system (MEMS) strapdown inertial navigation system (SINS) increases rapidly. Therefore, the bandwidth of the tracking loop for a global positioning system (GPS)/MEMS-SINS ultra-tight integration system is too narrow to track Doppler shift. GPS measurement error is correlated with the MEMS-SINS velocity error when the Doppler-assisted error exists, leading to tracking loop lock loss. The estimated precision of the integrated Kalman filter (IKF) also decreases. Even the integrated system becomes unstable. To solve this problem, an innovative GPS/MEMS-SINS ultra-tight integration scheme based on using high-precision carrier phase measurements as the IKF measurements is proposed in this study. By assisting the tracking loop with time-differenced carrier phase (TDCP) velocity, the carrier loop noise bandwidth and code correlator spacing are reduced. The tracking accuracies of the carrier and code are increased. The navigation accuracy of GPS/MEMS-SINS ultra-tight integration is further improved.

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Qin, Peng, Tao Zhao, Nian Liu, Zhen Mei, and Wen Yan. "Predefined-Time Fuzzy Neural Network Control for Omnidirectional Mobile Robot." Processes 11, no. 1 (December 22, 2022): 23. http://dx.doi.org/10.3390/pr11010023.

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In this paper, a fuzzy neural network based predefined-time trajectory tracking control method is proposed for the tracking problem of omnidirectional mobile robots (FM-OMR) with uncertainties. Considering the requirement of tracking error convergence time, a position tracking controller based on predefined-time stability is proposed. Compared with the traditional position tracking control method, the minimum upper bound of the convergence time can be explicitly set. In order to obtain more accurate angular velocity tracking, the inner loop controller combines Type 1 fuzzy neural network (T1FNN) to estimate the uncertainty. In addition, considering the problem of feedback channel noise, a Kalman filter combining velocity and position information is proposed. Finally, the simulation results verify the effectiveness of this method.

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Liu, G., and L. Wang. "JOINT TRACKING GPS AND LEO SIGNALS WITH ADAPTIVE VECTOR TRACKING LOOP IN CHALLENGING ENVIRONMENTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-3/W1-2022 (April 22, 2022): 119–24. http://dx.doi.org/10.5194/isprs-archives-xlvi-3-w1-2022-119-2022.

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Abstract. Navigation from LEO satellites own many merits and attracts increasing popularity recently. In addition to increasing the signal availability, the low signal strength loss and fast satellite geometry change from LEO satellite are particularly appealing in challenging environments. Recently, a few researchers attempt to navigate with non-cooperative signals from LEO satellites with pure phase lock loop (PLL) or frequency lock loop (FLL), while a more practical solution to utilizing LEO navigation is joint positioning with the existing GNSS signals which has not been seriously studied. In this study, we proposed a joint GPS and LEO navigation signal tracking strategy that employs a vector tracking loop (VTL) with fully considering the high dynamic characteristics of the LEO signals. In order to solve the high dynamics problem, the second-order deviation parameters were considered in the extended Kalman filter, which is more adaptive to the non-linear variation of the signal acceleration. In addition, a carrier-to-noise ratio (C/N0) based observation noise determination strategy is employed to adapt different observation conditions. The proposed method was verified with different simulation data and the results indicate the adaptive vector tracking loop is capable of tracking GPS and LEO signals simultaneously and robustly. The benefit is particularly in the weak signal scenarios. The experiment results also reveal that the joint vector tracking loop improves positioning accuracy in GNSS challenging environments.

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Koksal, N., M. Jalalmaab, and B. Fidan. "Adaptive Linear Quadratic Attitude Tracking Control of a Quadrotor UAV Based on IMU Sensor Data Fusion." Sensors 19, no. 1 (December 22, 2018): 46. http://dx.doi.org/10.3390/s19010046.

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In this paper, an infinite-horizon adaptive linear quadratic tracking (ALQT) control scheme is designed for optimal attitude tracking of a quadrotor unmanned aerial vehicle (UAV). The proposed control scheme is experimentally validated in the presence of real-world uncertainties in quadrotor system parameters and sensor measurement. The designed control scheme guarantees asymptotic stability of the close-loop system with the help of complete controllability of the attitude dynamics in applying optimal control signals. To achieve robustness against parametric uncertainties, the optimal tracking solution is combined with an online least squares based parameter identification scheme to estimate the instantaneous inertia of the quadrotor. Sensor measurement noises are also taken into account for the on-board Inertia Measurement Unit (IMU) sensors. To improve controller performance in the presence of sensor measurement noises, two sensor fusion techniques are employed, one based on Kalman filtering and the other based on complementary filtering. The ALQT controller performance is compared for the use of these two sensor fusion techniques, and it is concluded that the Kalman filter based approach provides less mean-square estimation error, better attitude estimation, and better attitude control performance.

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WIRA, PATRICE, and JEAN-PHILIPPE URBAN. "PREDICTING UNKNOWN MOTION FOR MODEL INDEPENDENT VISUAL SERVOING." International Journal of Computational Intelligence and Applications 01, no. 03 (September 2001): 287–302. http://dx.doi.org/10.1142/s1469026801000135.

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Prediction in real-time image sequences is a key-feature for visual servoing applications. It is used to compensate for the time-delay introduced by the image feature extraction process in the visual feedback loop. In order to track targets in a three-dimensional space in real-time with a robot arm, the target's movement and the robot end-effector's next position are predicted from the previous movements. A modular prediction architecture is presented, which is based on the Kalman filtering principle. The Kalman filter is an optimal stochastic estimation technique which needs an accurate system model and which is particularly sensitive to noise. The performances of this filter diminish with nonlinear systems and with time-varying environments. Therefore, we propose an adaptive Kalman filter using the modular framework of mixture of experts regulated by a gating network. The proposed filter has an adaptive state model to represent the system around its current state as close as possible. Different realizations of these state model adaptive Kalman filters are organized according to the divide-and-conquer principle: they all participate to the global estimation and a neural network mediates their different outputs in an unsupervised manner and tunes their parameters. The performances of the proposed approach are evaluated in terms of precision, capability to estimate and compensate abrupt changes in targets trajectories, as well as to adapt to time-variant parameters. The experiments prove that, without the use of models (e.g. the camera model, kinematic robot model, and system parameters) and without any prior knowledge about the targets movements, the predictions allow to compensate for the time-delay and to reduce the tracking error.

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Tang, Xinhua, Gianluca Falco, Emanuela Falletti, and Letizia Lo Presti. "Complexity reduction of the Kalman filter-based tracking loops in GNSS receivers." GPS Solutions 21, no. 2 (August 4, 2016): 685–99. http://dx.doi.org/10.1007/s10291-016-0557-6.

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Shi, Lei, Shurong Yuan, and Bo Yao. "Unconventionally Designed Tracking Loop Adaptable to Plasma Sheath Channel for Hypersonic Vehicles." Sensors 21, no. 1 (December 22, 2020): 21. http://dx.doi.org/10.3390/s21010021.

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An aircraft that moves through the atmosphere at hypersonic speed is covered by plasma sheath, which causes random and fast time-varying amplitude attenuation and phase fluctuation in received signals. This paper comprehensively analyzes the mechanism of the amplitude attenuation effects on a traditional phase-locked loop (PLL), which is always ignored in traditional scenarios (such as satellite telemetry and vehicle communication). Simulation results and theoretical analysis showed that traditional PLL does not work reliably for signal carrier tracking with the severe time-varying amplitude attenuation of the plasma sheath channel. In this paper, an unconventionally designed Kalman filter (KF) tracking loop that is aware of phase dynamics and amplitude attenuation fluctuation for hypersonic vehicles is proposed. To introduce time-varying amplitude attenuation into the proposed KF-based tracking loop, the amplitude attenuation is first modeled with an autoregressive model. The statistical characteristics of the amplitude and phase fluctuation are then incorporated into the state equation and observation equation. Simulation results indicate that the proposed tracking loop is stable when the signal-to-noise ratio is −10 dB with the Ka band, even in the most severe flight environment for hypersonic vehicles.

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Ping, Xianyao, Shuo Cheng, Wei Yue, Yongchang Du, Xiangyu Wang, and Liang Li. "Adaptive estimations of tyre–road friction coefficient and body’s sideslip angle based on strong tracking and interactive multiple model theories." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 14 (July 30, 2020): 3224–38. http://dx.doi.org/10.1177/0954407020941410.

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Vehicle dynamic states and parameters, such as the tyre–road friction coefficient and body’s sideslip angle especially, are crucial for vehicle dynamics control with close-loop feedback laws. Autonomous vehicles also have strict demands on real-time knowledge of those information to make reliable decisions. With consideration of the cost saving, some estimation methods employing high-resolution vision and position devices are not for the production vehicles. Meanwhile, the bad adaptability of traditional Kalman filters to variable system structure restricts their practical applications. This paper introduces a cost-efficient estimation scheme using on-board sensors. Improved Strong Tracking Unscented Kalman filter is constructed to estimate the friction coefficient with fast convergence rate on time-variant road surfaces. On the basis of previous step, an estimator based on interactive multiple model is built to tolerant biased noise covariance matrices and observe body’s sideslip angle. After the vehicle modelling errors are considered, a Self-Correction Data Fusion algorithm is developed to integrate results of the estimator and direct integral method with error correction theory. Some simulations and experiments are also implemented, and their results verify the high accuracy and good robustness of the cooperative estimation scheme.

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Tang, Xinhua, Xin Chen, Zhonghai Pei, and Peng Wang. "The Explicit Tuning Investigation and Validation of a Full Kalman Filter-Based Tracking Loop in GNSS Receivers." IEEE Access 7 (2019): 111487–98. http://dx.doi.org/10.1109/access.2019.2931729.

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WON, JONG-HOON, DOMINIK DÖTTERBÖCK, and BERND EISSFELLER. "Performance Comparison of Different Forms of Kalman Filter Approaches for a Vector-Based GNSS Signal Tracking Loop." Navigation 57, no. 3 (September 2010): 185–99. http://dx.doi.org/10.1002/j.2161-4296.2010.tb01777.x.

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Zhou, Hongcheng, Dezhi Xu, Daobo Wang, and Le Ge. "Adaptive Fault-Tolerant Tracking Control of Nonaffine Nonlinear Systems with Actuator Failure." Abstract and Applied Analysis 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/283403.

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This paper proposes an adaptive fault-tolerant control scheme for nonaffine nonlinear systems. A model approximation method which is a solution that bridges the gap between affine and nonaffine control systems is developed firstly. A joint estimation approach is based on unscented Kalman filter, in which both failure parameters and states are simultaneously estimated by means of the argument state vector composed of the unknown faults and states. Then, stability analysis is given for the closed-loop system. Finally, the proposed approach is verified using a three-degree-of-freedom simulation of a typical fighter aircraft and the significantly improved system response demonstrates the practical potential of the theoretic results obtained.

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Zhong, Lina, Jianye Liu, Rongbing Li, and Rong Wang. "Approach for Detecting Soft Faults in GPS/INS Integrated Navigation based on LS-SVM and AIME." Journal of Navigation 70, no. 3 (February 2, 2017): 561–79. http://dx.doi.org/10.1017/s037346331600076x.

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In life-critical applications, the real-time detection of faults is very important in Global Positioning System/Inertial Navigation System (GPS/INS) integrated navigation systems. A new fault detection method for soft fault detection is developed in this paper with the purpose of improving real-time performance. In general, the innovation information obtained from a Kalman filter is used for test statistic calculations in Autonomous Integrity Monitored Extrapolation (AIME). However, the innovation of the Kalman filter is degraded by error tracking and closed-loop correction effects, leading to time delays in soft fault detection. Therefore, the key issue of improving real-time performance is providing accurate innovation to AIME. In this paper, the proposed algorithm incorporates Least Squares-Support Vector Machine (LS-SVM) regression theory into AIME. Because the LS-SVM has a good regression and prediction performance, the proposed method provides replaced innovation obtained from the LS-SVM driven by real-time observation data. Based on the replaced innovation, the test statistics can follow fault amplitudes more accurately; finally, the real-time performance of soft fault detection can be improved. Theoretical analysis and physical simulations demonstrate that the proposed method can effectively improve the detection instantaneity.

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Liu, Di, Qingyuan Xia, Changhui Jiang, Chaochen Wang, and Yuming Bo. "A LSTM-RNN-Assisted Vector Tracking Loop for Signal Outage Bridging." International Journal of Aerospace Engineering 2020 (August 12, 2020): 1–11. http://dx.doi.org/10.1155/2020/2975489.

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Global Navigation Satellite System (GNSS) has been the most popular tool for providing positioning, navigation, and timing (PNT) information. Some methods have been developed for enhancing the GNSS performance in signal challenging environments (urban canyon, dense foliage, signal blockage, multipath, and none-line-of-sight signals). Vector Tracking Loop (VTL) was recognized as the most promising and prospective one among these technologies, since VTL realized mutual aiding between channels. However, momentary signal blockage from part of the tracking channels affected the VTL operation and the navigation solution estimation. Moreover, insufficient available satellites employed would lead to the navigation solution errors diverging quickly over time. Short-time or temporary signal blockage was common in urban areas. Aiming to improve the VTL performance during the signal outage, in this paper, the deep learning method was employed for assisting the VTL navigation solution estimation; more specifically, a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) was employed to aid the VTL navigation filter (navigation filter was usually a Kalman filter). LSTM-RNN obtained excellent performance in time-series data processing; therefore, in this paper, the LSTM-RNN was employed to predict the navigation filter innovative sequence values during the signal outage, and then, the predicted innovative values were employed to aid the navigation filter for navigation solution estimation. The LSTM-RNN was well trained while the signal was normal, and the past innovative sequence was employed as the input of the LSTM-RNN. A simulation was designed and conducted based on an open-source Matlab GNSS software receiver; a dynamic trajectory with several temporary signal outages was designed for testing the proposed method. Compared with the conventional VTL, the LSTM-RNN-assisted VTL could keep the horizontal positioning errors within 50 meters during a signal outage. Also, conventional Support Vector Machine (SVM) and radial basis function neural network (RBF-NN) were compared with the LSTM-RNN method; LSTM-RNN-assisted VTL could maintain the positioning errors less than 20 meters during the outages, which demonstrated LSTM-RNN was superior to the SVM and RBF-NN in these applications.

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Zhang, Chi, Fuwu Yan, Changqing Du, and Giorgio Rizzoni. "An Improved Model-Based Self-Adaptive Filter for Online State-of-Charge Estimation of Li-Ion Batteries." Applied Sciences 8, no. 11 (October 28, 2018): 2084. http://dx.doi.org/10.3390/app8112084.

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Accurate battery modeling is essential for the state-of-charge (SOC) estimation of electric vehicles, especially when vehicles are operated in dynamic processes. Temperature is a significant factor for battery characteristics, especially for the hysteresis phenomenon. Lack of existing literatures on the consideration of temperature influence in hysteresis voltage can result in errors in SOC estimation. Therefore, this study gives an insight to the equivalent circuit modeling, considering the hysteresis and temperature effects. A modified one-state hysteresis equivalent circuit model was proposed for battery modeling. The characterization of hysteresis voltage versus SOC at various temperatures was acquired by experimental tests to form a static look-up table. In addition, a strong tracking filter (STF) was applied for SOC estimation. Numerical simulations and experimental tests were performed in commercial 18650 type Li(Ni1/3Co1/3Mn1/3)O2 battery. The results were systematically compared with extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results of comparison showed the following: (1) the modified model has more voltage tracking capability than the original model; and (2) the modified model with STF algorithm has better accuracy, robustness against initial SOC error, voltage measurement drift, and convergence behavior than EKF and UKF.

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Tang, Xiaoqing, Yuke Li, Xiaoming Liu, Dan Liu, Zhuo Chen, and Tatsuo Arai. "Vision-Based Automated Control of Magnetic Microrobots." Micromachines 13, no. 2 (February 21, 2022): 337. http://dx.doi.org/10.3390/mi13020337.

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Magnetic microrobots are vital tools for targeted therapy, drug delivery, and micromanipulation on cells in the biomedical field. In this paper, we report an automated control and path planning method of magnetic microrobots based on computer vision. Spherical microrobots can be driven in the rotating magnetic field generated by electromagnetic coils. Under microscopic visual navigation, robust target tracking is achieved using PID–based closed–loop control combined with the Kalman filter, and intelligent obstacle avoidance control can be achieved based on the dynamic window algorithm (DWA) implementation strategy. To improve the performance of magnetic microrobots in trajectory tracking and movement in complicated environments, the magnetic microrobot motion in the flow field at different velocities and different distribution obstacles was investigated. The experimental results showed that the vision-based controller had an excellent performance in a complex environment and that magnetic microrobots could be controlled to move to the target position smoothly and accurately. We envision that the proposed method is a promising opportunity for targeted drug delivery in biological research.

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Zhao, Xin, Jianli Li, Xunliang Yan, and Shaowen Ji. "Robust Adaptive Cubature Kalman Filter and Its Application to Ultra-Tightly Coupled SINS/GPS Navigation System." Sensors 18, no. 7 (July 20, 2018): 2352. http://dx.doi.org/10.3390/s18072352.

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In this paper, we propose a robust adaptive cubature Kalman filter (CKF) to deal with the problem of an inaccurately known system model and noise statistics. In order to overcome the kinematic model error, we introduce an adaptive factor to adjust the covariance matrix of state prediction, and process the influence introduced by dynamic disturbance error. Aiming at overcoming the abnormality error, we propose the robust estimation theory to adjust the CKF algorithm online. The proposed adaptive CKF can detect the degree of gross error and subsequently process it, so the influence produced by the abnormality error can be solved. The paper also studies a typical application system for the proposed method, which is the ultra-tightly coupled navigation system of a hypersonic vehicle. Highly dynamical scene experimental results show that the proposed method can effectively process errors aroused by the abnormality data and inaccurate model, and has better tracking performance than UKF and CKF tracking methods. Simultaneously, the proposed method is superior to the tracing method based on a single-modulating loop in the tracking performance. Thus, the stable and high-precision tracking for GPS satellite signals are preferably achieved and the applicability of the system is promoted under the circumstance of high dynamics and weak signals. The effectiveness of the proposed method is verified by a highly dynamical scene experiment.

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Fan, Wenhui, Hongwen He, and Bing Lu. "Online Active Set-Based Longitudinal and Lateral Model Predictive Tracking Control of Electric Autonomous Driving." Applied Sciences 11, no. 19 (October 5, 2021): 9259. http://dx.doi.org/10.3390/app11199259.

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Autonomous driving is a breakthrough technology in the automobile and transportation fields. The characteristics of planned trajectories and tracking accuracy affect the development of autonomous driving technology. To improve the measurement accuracy of the vehicle state and realise the online application of predictive control algorithm, an online active set-based longitudinal and lateral model predictive tracking control method of autonomous driving is proposed for electric vehicles. Integrated with the vehicle inertial measurement unit (IMU) and global positioning system (GPS) information, a vehicle state estimator is designed based on an extended Kalman filter. Based on the 3-degree-of-freedom vehicle dynamics model and the curvilinear road coordinate system, the longitudinal and lateral errors dimensionality reduction is carried out. A fast-rolling optimisation algorithm for longitudinal and lateral tracking control of autonomous vehicles is designed and implemented based on convex optimisation, online active set theory and QP solver. Finally, the performance of the proposed tracking control method is verified in the reconstructed curve road scene based on real GPS data. The hardware-in-the-loop simulation results show that the proposed MPC controller has apparent advantages compared with the PID-based controller.

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Che, Fei, Yifeng Niu, Jie Li, and Lizhen Wu. "Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance." Applied Sciences 10, no. 11 (May 27, 2020): 3709. http://dx.doi.org/10.3390/app10113709.

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Cooperative standoff tracking of moving targets is an important application of fixed-wing unmanned aerial vehicles (UAVs). To cope with the problem of long convergence time and unstable tracking in cooperative target tracking, traditional Lyapunov vector field guidance (LVFG) is modified. The guidance parameter c is discussed, and the gradient descent method is utilized to develop the optimal guidance parameter search algorithm. As for tracking moving targets, an interacting multiple model-based unscented Kalman filter (IMM-UKF) estimator is built for predicting the target state, and the result is used for correcting the guidance law. Meanwhile, a speed-based controller is developed for faster convergence to the desired intervehicle phase, and the stability of the controller is proved using the Lyapunov stability theory. Numerical simulation results indicate the proposed guidance converges faster to the standoff circle without intersecting the orbit. The state estimator reduces the estimate error and the intervehicle phase converges faster to the desired phase than the traditional control method. Furthermore, extensive hardware-in-the-loop simulations are carried out to verify the feasibility of the algorithm.

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Wang, Fei-Xue, Qian Peng, Xin-Liang Zang, and Qi-Fan Xue. "Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation." Applied Sciences 11, no. 24 (December 20, 2021): 12137. http://dx.doi.org/10.3390/app112412137.

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Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.

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Wang, Xueyun, Jingjuan Zhang, Wei Wang, and Pengyu Gao. "An Innovative Architecture of UTC GPS/INS System with Improved Performance under Severe Jamming." Discrete Dynamics in Nature and Society 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/185618.

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Ultratightly coupled (UTC) architecture is believed to be the best architecture for Global Positioning System (GPS) and Inertial Navigation System (INS) integration system due to the advanced data fusion strategy and effective mutual assistance between the subsystems. However the performance of UTC GPS/INS system will be degraded by severe jamming interference, especially when low-grade inertial measurement unit (IMU) is used. To solve this problem an innovative architecture of UTC GPS/INS system is proposed. Since GPS receiver’s antijamming ability is closely related to tracking loop bandwidth, adaptive tracking loop bandwidth based on the fuzzy logics is proposed to enhance antijamming ability for GPS receiver. The bandwidth will be adapted through a fuzzy logic controller according to the calculated carrier to noise intensity ratio(C/N0). Moreover, fuzzy adaptive integration Kalman filter (IKF) is developed to improve estimation accuracy of IKF when measurement noises change. A simulation platform is established to evaluate the innovative architecture and results demonstrate that the proposed scheme improves navigation performance significantly under severe jamming conditions.

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Nosheen, Tayyaba, Ahsan Ali, Muhammad Umar Chaudhry, Dmitry Nazarenko, Inam ul Hasan Shaikh, Vadim Bolshev, Muhammad Munwar Iqbal, Sohail Khalid, and Vladimir Panchenko. "A Fractional Order Controller for Sensorless Speed Control of an Induction Motor." Energies 16, no. 4 (February 14, 2023): 1901. http://dx.doi.org/10.3390/en16041901.

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Agriculture activities are completely dependent upon energy production worldwide. This research presents sensorless speed control of a three-phase induction motor aided with an extended Kalman filter (EKF). Although a proportional integral (PI) controller can ensure tracking of the rotor speed, a considerable magnitude of ripples is present in the torque generated by a motor. Adding a simple derivative to have a proportional integral derivative (PID) action can cause a further increase in ripple magnitude, as it allows the addition of high-frequency noise in the system. Therefore, a fractional-order-based PID control is presented. The proposed control scheme is applied in a closed loop with the system, and simulation results are compared with the PID controller. It is evident from the results that the fractional order control not only ensures 20 times faster tracking, but ripple magnitude in torque was also reduced by a factor of 50% compared to that while using PID and ensures the effectiveness of the proposed strategy.

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Journal articles on the topic 'Kalman filter based tracking loop'

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