Journal articles: 'GENETIC PARTICLE FILTER'

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Wang, Er Shen, Tao Pang, and Zhi Xian Zhang. "Accuracy Improvement of GPS Positioning Based on GA-Aided Particle Filter." Applied Mechanics and Materials 719-720 (January 2015): 737–43. http://dx.doi.org/10.4028/www.scientific.net/amm.719-720.737.

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Aiming at the weight degeneracy phenomena in particle filter algorithm, a resampling method improving the diversity based on GA-aided particle filter was presented. Taking the advantage of genetic algorithm ( GA ) in selection ,crossover and inheritance to make up for the shortcoming of resampling. Genetic operation on particles in real number domain is adapted to reduce the complex of the genetic algorithm. And the evolutionary idea of genetic algorithm was combined with particle filter, by using selection, and mutation to improve the weight degeneracy and diversity of particle filter. This GA-aided particle filter was applied in the established GPS system nonlinear dynamic state space model. The experimental results based on the collected real GPS data is compared with the tradition particle filter, and compared with the effective number of particles and particle distribution. The experimental results indicated that the GA-aided particle filter can increase the number of particle, and effectively solve the particle degradation phenomena, the estimation accuracy of GA-aided particle filter is better than that of particle filter (PF).

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Liu, Zhigang, Jin Shang, and Xufen Hua. "Smart City Moving Target Tracking Algorithm Based on Quantum Genetic and Particle Filter." Wireless Communications and Mobile Computing 2020 (June 20, 2020): 1–9. http://dx.doi.org/10.1155/2020/8865298.

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In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

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You, Yuanhong, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu. "A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates." Hydrology and Earth System Sciences 27, no. 15 (August 9, 2023): 2919–33. http://dx.doi.org/10.5194/hess-27-2919-2023.

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Abstract. Accurate snowpack simulations are critical for regional hydrological predictions, snow avalanche prevention, water resource management, and agricultural production, particularly during the snow ablation period. Data assimilation methodologies are increasingly being applied for operational purposes to reduce the uncertainty in snowpack simulations and to enhance their predictive capabilities. This study aims to investigate the feasibility of using a genetic particle filter (GPF) as a snow data assimilation scheme designed to assimilate ground-based snow depth (SD) measurements across different snow climates. We employed the default parameterization scheme combination within the Noah-MP (with multi-parameterization) model as the model operator in the snow data assimilation system to evolve snow variables and evaluated the assimilation performance of the GPF using observational data from sites with different snow climates. We also explored the impact of measurement frequency and particle number on the filter updating of the snowpack state at different sites and the results of generic resampling methods compared to the genetic algorithm used in the resampling process. Our results demonstrate that a GPF can be used as a snow data assimilation scheme to assimilate ground-based measurements and obtain satisfactory assimilation performance across different snow climates. We found that particle number is not crucial for the filter's performance, and 100 particles are sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the filter-updating performance, and dense ground-based snow observational data always dominate the accuracy of assimilation results. Compared to generic resampling methods, the genetic algorithm used to resample particles can significantly enhance the diversity of particles and prevent particle degeneration and impoverishment. Finally, we concluded that the GPF is a suitable candidate approach for snow data assimilation and is appropriate for different snow climates.

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Li, Tao, and Qi Yuan Sun. "A Visual Tracking Based on Particle Filter of Multi-Algorithm Fusion." Applied Mechanics and Materials 513-517 (February 2014): 2893–96. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2893.

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A novel visual tracking algorithm based on particle filter with multi-algorithm fusion is proposed. Mean shift is employed to make particles distribute more reasonably in order to maintain tracking accuracy by using fewer particles, and the genetic evolution ideas is introduced to increase the diversity of samples by applying selection, crossover and mutation operator to achieve particles resampling. The experiments show that the tracking performance of the proposed method, compared with Mean Shift Embedded Particle Filter (MSEPF), is significantly improved.

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Huo, Lina. "Intelligent Recognition Method of Vehicle Path with Time Window Based on Genetic Algorithm." Security and Communication Networks 2021 (August 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/3614291.

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Based on particle filter and improved cuckoo genetic algorithm, an algorithm for intelligent vehicle path recognition with a time window is designed. Particle filter (PF) is an influential visual tracking tool; it relies on the Monte Carlo Chain framework and Bayesian probability, which are essential for intelligent monitoring systems. The algorithm first uses particle filters for visual tracking and then obtains the current operating environment of the vehicle, then performs cluster analysis on customer locations, and finally performs path recognition in each area. The algorithm not only introduces particle filters, which are advanced visual tracking, but also improves the cuckoo search algorithm; when the bird’s egg is found by the bird’s nest owner, it needs to randomly change the position of the entire bird’s nest, which speeds up the search speed of the optimal delivery route. Analyze and compare the hybrid intelligent algorithm and the cuckoo search algorithm. Finally, the international standard test set Benchmark Problems is used for testing. The experimental outcomes indicated that the new hybrid intelligent approach is an effective algorithm for handling vehicle routing tasks with time windows.

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Yang, Jin, Xuerong Cui, Juan Li, Shibao Li, Jianhang Liu, and Haihua Chen. "Particle filter algorithm optimized by genetic algorithm combined with particle swarm optimization." Procedia Computer Science 187 (2021): 206–11. http://dx.doi.org/10.1016/j.procs.2021.04.052.

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Mechri, Rihab, Catherine Ottlé, Olivier Pannekoucke, and Abdelaziz Kallel. "Genetic particle filter application to land surface temperature downscaling." Journal of Geophysical Research: Atmospheres 119, no. 5 (March 6, 2014): 2131–46. http://dx.doi.org/10.1002/2013jd020354.

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Chen, Xiyuan, Chong Shen, and Yuefang Zhao. "Study on GPS/INS System Using Novel Filtering Methods for Vessel Attitude Determination." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/678943.

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Any vehicle such as vessel has three attitude parameters, which are mostly defined as pitch, roll, and heading from true north. In hydrographic surveying, determination of these parameters by using GPS or INS technologies is essential for the requirements of vehicle measurements. Recently, integration of GPS/INS by using data fusion algorithm became more and more popular. Therefore, the data fusion algorithm plays an important role in vehicle attitude determination. To improve attitude determination accuracy and efficiency, two improved data fusion algorithms are presented, which are extended Kalman particle filter (EKPF) and genetic particle filter (GPF). EKPF algorithm combines particle filter (PF) with the extended Kalman filter (EKF) to avoid sample impoverishment during the resampling process. GPF is based on genetic algorithm and PF; several genetic operators such as selection, crossover, and mutation are adopted to optimize the resampling process of PF, which can not only reduce the particle impoverishment but also improve the computation efficiency. The performances of the system based on the two proposed algorithms are analyzed and compared with traditional KF. Simulation results show that, comprehensively considering the determination accuracy and consumption cost, the performance of the proposed GPF is better than EKPF and traditional KF.

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Sun, Meng, Yunjia Wang, Shenglei Xu, Hongji Cao, and Minghao Si. "Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter." Applied Sciences 10, no. 2 (January 17, 2020): 668. http://dx.doi.org/10.3390/app10020668.

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This paper proposes a fusion indoor positioning method that integrates the pedestrian dead-reckoning (PDR) and geomagnetic positioning by using the genetic-particle filter (GPF) algorithm. In the PDR module, the Mahony complementary filter (MCF) algorithm is adopted to estimate the heading angles. To improve geomagnetic positioning accuracy and geomagnetic fingerprint specificity, the geomagnetic multi-features positioning algorithm is devised and five geomagnetic features are extracted as the single-point fingerprint by transforming the magnetic field data into the geographic coordinate system (GCS). Then, an optimization mechanism is designed by using gene mutation and the method of reconstructing a particle set to ameliorate the particle degradation problem in the GPF algorithm, which is used for fusion positioning. Several experiments are conducted to evaluate the performance of the proposed methods. The experiment results show that the average positioning error of the proposed method is 1.72 m and the root mean square error (RMSE) is 1.89 m. The positioning precision and stability are improved compared with the PDR method, geomagnetic positioning, and the fusion-positioning method based on the classic particle filter (PF).

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Bi, Jun, Wei Guan, and Long-Tao Qi. "A genetic resampling particle filter for freeway traffic-state estimation." Chinese Physics B 21, no. 6 (June 2012): 068901. http://dx.doi.org/10.1088/1674-1056/21/6/068901.

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Jamal, Alaa, and Raphael Linker. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model." Agriculture 10, no. 12 (December 7, 2020): 606. http://dx.doi.org/10.3390/agriculture10120606.

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Particle filter has received increasing attention in data assimilation for estimating model states and parameters in cases of non-linear and non-Gaussian dynamic processes. Various modifications of the original particle filter have been suggested in the literature, including integrating particle filter with Markov Chain Monte Carlo (PF-MCMC) and, later, using genetic algorithm evolutionary operators as part of the state updating process. In this work, a modified genetic-based PF-MCMC approach for estimating the states and parameters simultaneously and without assuming Gaussian distribution for priors is presented. The method was tested on two simulation examples on the basis of the crop model AquaCrop-OS. In the first example, the method was compared to a PF-MCMC method in which states and parameters are updated sequentially and genetic operators are used only for state adjustments. The influence of ensemble size, measurement noise, and mutation and crossover parameters were also investigated. Accurate and stable estimations of the model states were obtained in all cases. Parameter estimation was more challenging than state estimation and not all parameters converged to their true value, especially when the parameter value had little influence on the measured variables. Overall, the proposed method showed more accurate and consistent parameter estimation than the PF-MCMC with sequential estimation, which showed highly conservative behavior. The superiority of the proposed method was more pronounced when the ensemble included a large number of particles and the measurement noise was low.

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Yadav, Suman, Richa Yadav, Ashwni Kumar, and Manjeet Kumar. "Design of Optimal Two-Dimensional FIR Filters with Quadrantally Symmetric Properties Using Vortex Search Algorithm." Journal of Circuits, Systems and Computers 29, no. 10 (December 16, 2019): 2050155. http://dx.doi.org/10.1142/s0218126620501558.

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This research paper presents a new evolutionary technique named vortex search optimization (VSO) to design digital 2D finite impulse response (FIR) filter for improved performance both in pass-band and stop-band regions. Optimum filter coefficients are calculated by minimizing the deviation of actual frequency response from specified or desired response. Efficiency of the designed filter is measured by several parameters, such as maximum pass-band ripple, maximum stop-band ripple, mean attenuation in stop band and time taken, to execute the code. Analysis of the performance of designed filter is correlated with various different algorithms like real coded genetic algorithm, particle swarm optimization, genetic search algorithm and hybrid particle swarm optimization gravitational algorithm. Comparative study shows significant reduction in pass-band error, stop-band error and execution time.

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Zhou, Ning, Lawrence Lau, Ruibin Bai, and Terry Moore. "A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking." Remote Sensing 13, no. 1 (January 2, 2021): 132. http://dx.doi.org/10.3390/rs13010132.

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In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems. Resampling is generally required to address the inherent particle degeneracy problem in the particle filter. However, traditional resampling methods cause the problem of particle impoverishment. This problem degrades positioning accuracy and robustness and sometimes may even result in filtering divergence and tracking failure. In order to mitigate the particle impoverishment and improve positioning accuracy, this paper proposes an improved genetic optimization based resampling method. This resampling method optimizes the distribution of resampled particles by the five operators, i.e., selection, roughening, classification, crossover, and mutation. The proposed resampling method is then integrated into the particle filtering framework to form a genetic optimization resampling based particle filtering (GORPF) algorithm. The performance of the GORPF algorithm is tested by a one-dimensional tracking simulation and a three-dimensional indoor tracking experiment. Both test results show that with the aid of the proposed resampling method, the GORPF has better robustness against particle impoverishment and achieves better positioning accuracy than several existing target tracking algorithms. Moreover, the GORPF algorithm owns an affordable computation load for real-time applications.

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Liu, Xingtao, Chaoyi Zheng, Ji Wu, Jinhao Meng, Daniel-Ioan Stroe, and Jiajia Chen. "An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries." Energies 13, no. 2 (January 18, 2020): 478. http://dx.doi.org/10.3390/en13020478.

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In this paper, an improved method for estimating the state of charge (SOC) of lithium-ion batteries is proposed, which is developed from the particle filter (PF). An improved genetic particle filter (GPF), owing to the advantages of the PF and genetic algorithm, is proposed to overcome the disadvantage of the traditional particle filter: lacking the diversity of particles. Firstly, the relationship between SOC and open-circuit voltage (OCV) is identified on the low-current OCV test. Secondly, a first-order resistor and capacitance (RC) model is established, then, the least-squares algorithm is used to identify the model parameters via the incremental current test. Thirdly, GPF and the improved GPF (IGPF) are proposed to solve the problems of the PF. The method based on the IGPF is proposed to estimate the state of power (SOP). Finally, IGPF, GPF, and PF are employed to estimate the SOC on the federal urban driving schedule (FUDS). The results show that compared with traditional PF, the errors of the IGPF are 20% lower, and compared with GPF, the maximum error of the IGPF has declined 1.6% SOC. The SOC that is estimated by the IGPF is applied to estimate the SOP for battery, considering the restrictions from the peak SOC, the voltage, and the instruction manual. The result shows that the method based on the IGPF can successfully estimate SOP.

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Liu, Hongqiang, Lei Yu, Chenwei Ruan, and Zhongliang Zhou. "Tracking Air-to-Air Missile Using Proportional Navigation Model with Genetic Algorithm Particle Filter." Mathematical Problems in Engineering 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3921608.

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The purpose of this paper is to track the air-to-air missile. Here we put forward the PN-GAPF (Proportional Navigation motion model and Genetic Algorithm Particle Filter) method to solve the problem. The main jobs we have done can be listed as follows: firstly, we establish the missile state space model named as the Proportional Navigation (PN) motion model to simulate the real motion of the air-to-air missile; secondly, the PN-EKF and PN-PF methods are proposed to track the missile, through combining PN motion model with EKF and PF; thirdly, in order to solve the particle degeneracy and diversity loss, we introduce the intercross and variation in GA to the particles resampling step and then the PN-GAPF method is put forward. The simulation results show that the PN motion model is better than the CV and CA motion models for tracking the air-to-air missile and that the PN-GAPF method is more efficient than the PN-EKF and PN-PF.

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Zhang, Wan-li, and Xiao-ying Yang. "A New Particle Filter Target Tracking Algorithm Based on Genetic Algorithm." Open Automation and Control Systems Journal 7, no. 1 (June 26, 2015): 521–24. http://dx.doi.org/10.2174/1874444301507010521.

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XIE, Weicheng, Junxu WEI, Zhichao CHEN, and Tianqian LI. "Particle Filter Target Tracking Algorithm Based on Dynamic Niche Genetic Algorithm." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E100.A, no. 6 (2017): 1325–32. http://dx.doi.org/10.1587/transfun.e100.a.1325.

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Qiu, Zhenbing, and Huaming Qian. "Adaptive genetic particle filter and its application to attitude estimation system." Digital Signal Processing 81 (October 2018): 163–72. http://dx.doi.org/10.1016/j.dsp.2018.06.015.

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Gautam, Divya, Kavita Khare, and Bhavana P. Shrivastava. "A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising." Applied Sciences 13, no. 12 (June 11, 2023): 7032. http://dx.doi.org/10.3390/app13127032.

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Medical image denoising is a crucial pre-processing task in the medical field to ensure accurate analysis of anomalies or sicknesses in the human body. Digital filters are popular for reducing undesired noise as they provide reliability, high accuracy, and reduced sensitivity to component tolerances compared to analog filters. However, conventional digital filter design approaches lack efficiency in achieving global optimization robustness. To overcome these incapabilities, this paper adopted bio-inspired optimization algorithms to offer viable digital filter designing tools because of their simple implementation and requirement of a few parameters to control their convergence. This research article explores a hybrid strategy that combines a novel guided decimation box filter (GDBF) with a hybrid cuckoo particle swarm optimization (HCPSO) algorithm to design a denoising filter for medical images. It is the first time a decimation box filter has been used for denoising, leading to novelty. The HCPSO algorithm is applied to obtain the filter parameters optimally. Medical images mostly suffer from four types of noises. The performance of the proposed filter is analyzed for these types of noise. To highlight the importance of parameter selection, the results of the proposed method are compared with other recently utilized bio-inspired genetic algorithms, such as PSO (particle swarm optimization), CS (cuckoo search), and FF (firefly). The superiority (potency) of the proposed method has been established by calculating the improvement in quality parameters such as the peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). The proposed filter achieved the highest PSNR (~35.7 dB), SSIM (~0.95), and FSIM (~0.92) and proved its numerical and visual quality efficacy over state-of-the-art models.

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Cao, Lei, Shu Guang Liu, Xian Hua Zeng, Pan He, and Yue Yuan. "Passenger Flow Prediction Based on Particle Filter Optimization." Applied Mechanics and Materials 373-375 (August 2013): 1256–60. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1256.

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The article aims at making scientific and accurate prediction of the current flow of passagers based on its characteristics, which is nonlinear and is influenced by various factors. Clustering is used to make classification of IC card data.Then ARMA prediction model is installed and the model parameter is worked out through matrix method and revised by the method of particle filtering. Resampling is also conducted through genetic optimization. The data of rail transit of Chongqing City of June, 2012 is used to make verification.The result shows that the prediction of optimize parameters through the method of particle filtering is close to the real values. The MAE is 0.8154,MAPE is 1.755,so this method in our article can make the prediction of passenger flow volume more accurately.

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Gellert, Karol, and Erik Schlögl. "Parameter Learning and Change Detection Using a Particle Filter with Accelerated Adaptation." Risks 9, no. 12 (December 16, 2021): 228. http://dx.doi.org/10.3390/risks9120228.

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This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in online sequential filtering does not match the model posterior filtered based on data up to a current point in time. The examples considered encompass parameter regime shifts and stochastic volatility. The filter adapts to regime shifts extremely rapidly and delivers a clear heuristic for distinguishing between regime shifts and stochastic volatility, even though the model dynamics assumed by the filter exhibit neither of those features.

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Muñoz, Javier, Blanca López, Fernando Quevedo, Concepción A. Monje, Santiago Garrido, and Luis E. Moreno. "Coverage Strategy for Target Location in Marine Environments Using Fixed-Wing UAVs." Drones 5, no. 4 (October 17, 2021): 120. http://dx.doi.org/10.3390/drones5040120.

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In this paper, we propose a coverage method for the search of lost targets or debris on the ocean surface. The OSCAR data set is used to determine the marine currents and the differential evolution genetic filter is used to optimize the sweep direction of the lawnmower coverage and get the sweep angle for the maximum probability of containment. The position of the target is determined by a particle filter, where the particles are moved by the ocean currents and the final probabilistic distribution is obtained by fitting the particle positions to a Gaussian probability distribution. The differential evolution algorithm is then used to optimize the sweep direction that covers the highest probability of containment cells before the less probable ones. The algorithm is tested with a variety of parameters of the differential evolution algorithm and compared to other popular optimization algorithms.

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Pan, Haipeng, Chengte Chen, and Minming Gu. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration." Energies 14, no. 16 (August 15, 2021): 5000. http://dx.doi.org/10.3390/en14165000.

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Accurately estimating the state of health (SOH) of a lithium-ion battery is significant for electronic devices. To solve the nonlinear degradation problem of lithium-ion batteries (LIB) caused by capacity regeneration, this paper proposes a new LIB degradation model and improved particle filter algorithm for LIB SOH estimation. Firstly, the degradation process of LIB is divided into the normal degradation stage and the capacity regeneration stage. A multi-stage prediction model (MPM) based on the calendar time of the LIB is proposed. Furthermore, the genetic algorithm is embedded into the standard particle filter to increase the diversity of particles and improve prediction accuracy. Finally, the method is verified with the LIB dataset provided by the NASA Ames Prognostics Center of Excellence. The experimental results show that the method proposed in this paper can effectively improve the accuracy of capacity prediction.

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Song, Jin Bao, Long Ye, Qin Zhang, and Jian Ping Chai. "Video Redirection Parameters Optimization Based on Hierarchical Genetic Optimization Strategy." Applied Mechanics and Materials 668-669 (October 2014): 1152–56. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1152.

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In the process of particle filter, it can produce the problem of particle degeneration, and the amount of computation is very big. In order to solve these two problems, this paper presents a method for video redirection parameters optimization based on hierarchical genetic optimization strategy. This paper adopts hierarchical mutation operator and local crossover operator. The result shows that the hierarchical genetic optimization algorithm which is used in video tracking has a very good effect.

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Song, Honghu, Zhen Wu, Hui Zhang, Junli Li, and Rui Qiu. "A simulation optimization design of the filter stack spectrometer for laser-plasma interaction experiment." Journal of Instrumentation 18, no. 03 (March 1, 2023): P03012. http://dx.doi.org/10.1088/1748-0221/18/03/p03012.

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Abstract Filter stack spectrometers are widely employed in laser facilities for the spectrum measurement of bremsstrahlung photons. However, this method suffers from large uncertainty of unfolding due to its intrinsic limit resolution. For this, an optimization study on filter stack spectrometer is conducted. This procedure is implemented by a hybrid particle swarm optimization and genetic algorithm (PSO-GA). Monte-Carlo particle transport code Fluka is used for the simulation of the response matrix. Gravel algorithm, based on the least-square method, is used for the unfolding. For mono-energetic photons, this optimized filter stack spectrometer design provides a better energy resolution. For continuous distribution, this optimized filter stack spectrometer design yields a narrower unfolding solution space in the presence of measurement error.

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Liu, Ruian, and Yiming Sun. "Classification And Optimization Analysis Method of Digital Filter." Highlights in Science, Engineering and Technology 61 (July 30, 2023): 159–71. http://dx.doi.org/10.54097/hset.v61i.10288.

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The significance of digital filters in the field of digital signal processing has been increasingly emphasized due to the swift advancements in science, technology, and computing in recent years. At the same time, the optimization of digital filters has attracted the attention of many researchers. This paper describes in detail the classification and optimization methods of digital filters, and analyzes the advantages and disadvantages of several optimization algorithms. Among them, the parameters of the Genetic Algorithm are relatively complex. How to reduce the difficulty of calculation and ensure the performance of the filter is the direction of current research. Particle Swarm Algorithm works very well in the design process and parameter configuration, but it is still difficult to apply. This is also the direction of future research. Whether from the perspective of scientific research or practical application, the optimization of digital filters has a lot of room for development.

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Wang, Yanni, and Junni Zhou. "The Analysis of Target Trajectory Based on Genetic Evolutionary and Particle Filter." TELKOMNIKA (Telecommunication Computing Electronics and Control) 14, no. 3A (September 1, 2016): 297. http://dx.doi.org/10.12928/telkomnika.v14i3a.4421.

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Boudjelaba, Kamal, Frédéric Ros, and Djamel Chikouche. "Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design." Circuits, Systems, and Signal Processing 33, no. 10 (April 29, 2014): 3195–222. http://dx.doi.org/10.1007/s00034-014-9800-y.

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Ababneh, Jehad I., and Mohammad H. Bataineh. "Linear phase FIR filter design using particle swarm optimization and genetic algorithms." Digital Signal Processing 18, no. 4 (July 2008): 657–68. http://dx.doi.org/10.1016/j.dsp.2007.05.011.

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Adamu, Zainab Muhammad, Emmanuel Gbenga Dada, and Stephen Bassi Joseph. "Moth Flame Optimization Algorithm for Optimal FIR Filter Design." International Journal of Intelligent Systems and Applications 13, no. 5 (October 8, 2021): 24–34. http://dx.doi.org/10.5815/ijisa.2021.05.03.

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This paper presents the application of Moth Flame optimization (MFO) algorithm to determine the best impulse response coefficients of FIR low pass, high pass, band pass and band stop filters. MFO was inspired by observing the navigation strategy of moths in nature called transverse orientation composed of three mathematical sub-models. The performance of the proposed technique was compared to those of other well-known high performing optimization techniques like techniques like Particle Swarm Optimization (PSO), Novel Particle Swarm Optimization (NPSO), Improved Novel Particle Swarm Optimization (INPSO), Genetic Algorithm (GA), Parks and McClellan (PM) Algorithm. The performances of the MFO based designed optimized FIR filters have proved to be superior as compared to those obtained by PSO, NPSO, INPSO, GA, and PM Algorithm. Simulation results indicated that the maximum stop band ripples 0.057326, transition width 0.079 and fitness value 1.3682 obtained by MFO is better than that of PSO, NPSO, INPSO, GA, and PM Algorithms. The value of stop band ripples indicated the ripples or fluctuations obtained at the range which signals are attenuated is very low. The reduced value of transition width is the rate at which a signal changes from either stop band to pass band of a filter or vice versa is very good. Also, small fitness value in an indication that the values of the control variable of MFO are very near to its optimum solutions. The proposed design technique in this work generates excellent solution with high computational efficiency. This shows that MFO algorithm is an outstanding technique for FIR filter design.

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Liu, Fang, Jie Ma, and Weixing Su. "Unscented Particle Filter for SOC Estimation Algorithm Based on a Dynamic Parameter Identification." Mathematical Problems in Engineering 2019 (April 22, 2019): 1–14. http://dx.doi.org/10.1155/2019/7452079.

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In order to solve the problem that the model-based State of Charge (SOC) estimation method is too dependent on the model parameters in the SOC estimation of electric vehicles, an improved genetic algorithm is proposed in this paper. The method has the advantages of being able to quickly determine the search range, reducing the probability of falling into local optimum, and having high recognition accuracy. Then we can realize online dynamic identification of power battery model parameters and improve the accuracy of model parameter identification. In addition, considering the complex application environment and operating conditions of electric vehicles, an SOC estimation method based on improved genetic algorithm and unscented particle filter (improved GA-UPF) is proposed. And we compare the improved GA-UPF algorithm with the least square unscented particle filter (LS-UPF) and improved GA unscented Kalman filter (improved GA-UKF) algorithm. The comparison results show that the improved GA-UPF algorithm proposed in this paper has higher estimation accuracy and better stability. It also reflects the practicability and accuracy of the improved GA parameter identification algorithm proposed in this paper.

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Polukhin, Pavel V. "Application of genetic algorithms to optimize solution of filtering and prediction problems in dynamic program testing systems." Yugra State University Bulletin 18, no. 4 (January 14, 2023): 120–32. http://dx.doi.org/10.18822/byusu202204120-132.

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Subject of research: probabilistic time models of testing created to form complex stochastic connections between individual test elements and developed to detect certain groups of the web applications program errors. The purpose of research: substantiate the possibility of using genetic algorithms in the process of solving probabilistic testing problems based on a multi-particle filter and evaluate their effectiveness. The study provides fundamental methods to improve the accuracy of the posterior distribution of probabilistic testing models and the total number of matched with evidences samples. Methods and objects of research: object of the research is to solve filtering and smoothing problems for a probabilistic test model based on a multi-particle filter. Methods and algorithms based on the Monte Carlo method are presented, allowing, in combination with genetic algorithms, to increase the accuracy of obtaining posterior estimates of samples. This approach allows you to narrow the range of samples, as well as increase their consistency. The formation of each next sample will be carried out taking into account the previous ones through the use of crossover and mutation operations. The main results of research: as a result, the validity of the proposed approaches to solving filtration and prediction problems in the process of implementing testing procedures based on multi-particle filter algorithms and genetic algorithms was proved. The given practical results prove the constructiveness and scientific validity of the proposed methods and algorithms for solving web applications testing problems.

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Jamal, Alaa, and Raphael Linker. "Covariance-Based Selection of Parameters for Particle Filter Data Assimilation in Soil Hydrology." Water 14, no. 22 (November 9, 2022): 3606. http://dx.doi.org/10.3390/w14223606.

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Real-time in situ measurements are increasingly being used to improve the estimations of simulation models via data assimilation techniques such as particle filter. However, models that describe complex processes such as water flow contain a large number of parameters while the data available are typically very limited. In such situations, applying particle filter to a large, fixed set of parameters chosen a priori can lead to unstable behavior, i.e., inconsistent adjustment of some of the parameters that have only limited impact on the states that are being measured. To prevent this, in this study correlation-based variable selection is embedded in the particle filter, so that at each step only a subset of the most influential parameters is adjusted. The particle filter used in this study includes genetic algorithm operators and Monte Carlo Markov Chain for alleviating filter degeneracy and sample impoverishment. The proposed method was applied to a water flow model (Hydrus-1D) in which soil water content at various depths and soil hydraulic parameters were updated. Two case studies are presented. Overall, the proposed method yielded parameters and states estimates that were more accurate and more consistent than those obtained when adjusting all the parameters. Furthermore, the results show that the higher the influence of a parameter on the model output under the current conditions, the better the estimation of this parameter is.

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Henderson, Donna, Sha (Joe) Zhu, Christopher B. Cole, and Gerton Lunter. "Demographic inference from multiple whole genomes using a particle filter for continuous Markov jump processes." PLOS ONE 16, no. 3 (March 2, 2021): e0247647. http://dx.doi.org/10.1371/journal.pone.0247647.

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Demographic events shape a population’s genetic diversity, a process described by the coalescent-with-recombination model that relates demography and genetics by an unobserved sequence of genealogies along the genome. As the space of genealogies over genomes is large and complex, inference under this model is challenging. Formulating the coalescent-with-recombination model as a continuous-time and -space Markov jump process, we develop a particle filter for such processes, and use waypoints that under appropriate conditions allow the problem to be reduced to the discrete-time case. To improve inference, we generalise the Auxiliary Particle Filter for discrete-time models, and use Variational Bayes to model the uncertainty in parameter estimates for rare events, avoiding biases seen with Expectation Maximization. Using real and simulated genomes, we show that past population sizes can be accurately inferred over a larger range of epochs than was previously possible, opening the possibility of jointly analyzing multiple genomes under complex demographic models. Code is available at https://github.com/luntergroup/smcsmc.

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Lee, Dong Geun, Taeyong Jeong, and Keum Cheol Hwang. "Design of a Wide-Beamwidth Pixelated Dielectric Resonator Antenna Using a Modified Stepped-Impedance Filter to Suppress Harmonics." Applied Sciences 12, no. 15 (August 2, 2022): 7765. http://dx.doi.org/10.3390/app12157765.

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This study designed a wide-beamwidth pixelated dielectric resonator antenna (DRA) combined with a low-pass filter (LPF) to suppress harmonics. The DRA was designed to create a wide-beam pattern with a pixelated structure. The pixelated DRA was optimized by a genetic-learning particle swarm optimization algorithm. To prevent significant higher-mode radiation and harmonics from occurring in the DRA, an LPF was included in its feeding line. The filter had a seventh-order Chebyshev design, and a hybrid step-impedance filter was proposed by modifying the step-impedance filter for use in narrow spaces behind the ground.

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Lin, Jie, Hongyang Zhao, Yuan Ma, Jiubin Tan, and Peng Jin. "New hybrid genetic particle swarm optimization algorithm to design multi-zone binary filter." Optics Express 24, no. 10 (May 9, 2016): 10748. http://dx.doi.org/10.1364/oe.24.010748.

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Oshman, Yaakov, and Avishy Carmi. "Attitude Estimation from Vector Observations Using a Genetic-Algorithm-Embedded Quaternion Particle Filter." Journal of Guidance, Control, and Dynamics 29, no. 4 (July 2006): 879–91. http://dx.doi.org/10.2514/1.17951.

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Moghaddasi, Somayyeh Sadegh, and Neda Faraji. "A hybrid algorithm based on particle filter and genetic algorithm for target tracking." Expert Systems with Applications 147 (June 2020): 113188. http://dx.doi.org/10.1016/j.eswa.2020.113188.

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Liu, Hong, Kunde Yang, and Qiulong Yang. "Sequential Parameter Estimation of Modal Dispersion Curves with an Adaptive Particle Filter in Shallow Water: Experimental Results." Remote Sensing 13, no. 12 (June 18, 2021): 2387. http://dx.doi.org/10.3390/rs13122387.

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An adaptive particle filter method is presented for performing sequential geoacoustic estimation of a shallow water acoustic environment using the explosive sound sources. This approach treats environmental parameters and source–receiver distance as unknown random variables that evolve as the source moves. As a sequential estimation method, this approach reduces the expense of computation than genetic algorithm and yields results with the same accuracy. Comparing with standard Particle filter, proposed method can adjust control parameters to adapt to a rapidly changing environment. This approach is demonstrated on the shallow water sound propagation data which was collected during the ASIAEX 2001 experiment. The results indicate that the geoacoustic parameters are well estimated and source–receiver distance are also well determined.

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Yong Kang, Ou, and Cheng Long. "A Robust Indoor Mobile Localization Algorithm for Wireless Sensor Network in Mixed LOS/NLOS Environments." Journal of Sensors 2020 (September 1, 2020): 1–16. http://dx.doi.org/10.1155/2020/8854389.

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Wireless sensor network (WSN) is a self-organizing network which is composed of a large number of cheap microsensor nodes deployed in the monitoring area and formed by wireless communication. Since it has the characteristics of rapid deployment and strong resistance to destruction, the WSN positioning technology has a wide application prospect. In WSN positioning, the nonline of sight (NLOS) is a very common phenomenon affecting accuracy. In this paper, we propose a NLOS correction method algorithm base on the time of arrival (TOA) to solve the NLOS problem. We firstly propose a tendency amendment algorithm in order to correct the NLOS error in geometry. Secondly, this paper propose a particle selection strategy to select the standard deviation of the particle swarm as the basis of evolution and combine the genetic evolution algorithm, the particle filter algorithm, and the unscented Kalman filter (UKF) algorithm. At the same time, we apply orthogon theory to the UKF to make it have the ability to deal with the target trajectory mutation. Finally we use maximum likelihood localization (ML) to determine the position of the mobile node (MN). The simulation and experimental results show that the proposed algorithm can perform better than the extend Kalman filter (EKF), Kalman filter (KF), and robust interactive multiple model (RIMM).

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Song, Jin Bao, Long Ye, Qin Zhang, and Jian Ping Chai. "Motion Redirection Based on Markov Chain Monte Carlo Particle Filter." Applied Mechanics and Materials 668-669 (October 2014): 1086–89. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1086.

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This paper aims at the difficulty that lack of observation model and high-dimensional sampling in video tooning, proposes a method based on key frame matching and dual-directional Markov chain Monte Carlo sampling of video motion redirection. At first, after extracting the key frame of a given video, By affine transformation and linear superposition, the subject initializes the video’s space-time parameters and forms the observation model; Secondly, in each space-time, based on the bi-directional Markov property of each frame, This paper proposed a dual-directional Markov chain Monte Carlo sampling particle filter structure and takes full advantage of the relationship of the front and back frame of the parameters to estimate motion redirection parameters. At the same time, for high-dimensional sampling problem, the subject according to the directional parameters’ correlation implements classification of skeleton parameters-morphological parameters-physical parameters, proposes a hierarchical genetic strategy to optimize the output parameters and improves the efficiency of the algorithm. The research of this paper will produce an efficient and prominent animation expressive video motion redirection method and play an important role on video animation of the development.

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Han, Hua, Yong-Sheng Ding, Kuang-Rong Hao, and Xiao Liang. "An evolutionary particle filter with the immune genetic algorithm for intelligent video target tracking." Computers & Mathematics with Applications 62, no. 7 (October 2011): 2685–95. http://dx.doi.org/10.1016/j.camwa.2011.06.050.

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S. Kumar, R. Mathusoothana. "Robust multi-view videos face recognition based on particle filter with immune genetic algorithm." IET Image Processing 13, no. 4 (March 28, 2019): 600–606. http://dx.doi.org/10.1049/iet-ipr.2018.5268.

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Zan, Jianhua, and Chunjun Chen. "Life Prediction Method of Electromagnetic Contactor Based on Fusion of Improved Particle Filter and Degradation Model." Journal of Physics: Conference Series 2383, no. 1 (December 1, 2022): 012053. http://dx.doi.org/10.1088/1742-6596/2383/1/012053.

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Electromagnetic contactors are the key components of high-speed trains. A reasonable evaluation of the remaining life of the contactors can optimize the replacement cycle of the contactors and ensure the safe operation of the train. Aiming at the problem of remaining life prediction of contactors, a contactor life prediction method based on the fusion of improved particle filter algorithm and performance degradation model is proposed. Firstly, on the basis of analyzing the failure mechanism of the contactor and establishing its electromagnetic and dynamic simulation model, a performance degradation model based on overtravel time is established. Then design the contactor life test to obtain its full life cycle degradation data. Next, the adaptive genetic algorithm is used to optimize the resampling process of the particle filter algorithm. Finally, the optimal parameter values of the performance degradation model are obtained by the improved particle filter algorithm, and then the prediction of the remaining life of the contactor is realized. The prediction results show that the method can effectively predict the remaining life of the contactor, and the prediction accuracy meets the actual engineering needs, which provides an important reference for the operation and maintenance of the electromagnetic contactor for high-speed trains.

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Saha, S. K., Rajib Kar, D. Mandal, and S. P. Ghoshal. "Digital Stable IIR Band Pass Filter Design Using Seeker Optimization Technique." Advanced Materials Research 905 (April 2014): 406–10. http://dx.doi.org/10.4028/www.scientific.net/amr.905.406.

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This paper presents a novel, control parameter independent evolutionary search technique known as Seeker Optimization Algorithm (SOA) for the design of a eighth order Infinite Impulse Response (IIR) Band Pass (BP) filter. A new fitness function has also been adopted in this paper to improve the stop band attenuation to a great extent. The performance of the SOA based IIR BP filter design has proven to be much superior as compared to those obtained by real coded genetic algorithm (RGA) and standard particle swarm optimization (PSO) in terms of highest sharpness at cut-off, smallest pass band ripple, highest stop band attenuation, smallest stop band ripple and also the fastest convergence speed with assured stability recognized by the pole-zero analysis of the designed optimized IIR filter.

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WANG Ling-ling, 王玲玲, and 辛云宏 XIN Yun-hong. "A Small IR Target Detection and Tracking Algorithm Based on Morphological and Genetic-particle Filter." ACTA PHOTONICA SINICA 42, no. 7 (2013): 849–56. http://dx.doi.org/10.3788/gzxb20134207.0849.

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Yu, Ming, Hang Li, Wuhua Jiang, Hai Wang, and Canghua Jiang. "Fault Diagnosis and RUL Prediction of Nonlinear Mechatronic System via Adaptive Genetic Algorithm-Particle Filter." IEEE Access 7 (2019): 11140–51. http://dx.doi.org/10.1109/access.2019.2891854.

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Kim, Young-Rok, Eunseo Park, Eun-Jung Choi, Sang-Young Park, Chandeok Park, and Hyung-Chul Lim. "Precise orbit determination using the batch filter based on particle filtering with genetic resampling approach." Advances in Space Research 54, no. 6 (September 2014): 998–1007. http://dx.doi.org/10.1016/j.asr.2014.06.001.

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Li, Lin, Alfredo Alan Flores Saldivar, Yun Bai, and Yun Li. "Battery Remaining Useful Life Prediction with Inheritance Particle Filtering." Energies 12, no. 14 (July 19, 2019): 2784. http://dx.doi.org/10.3390/en12142784.

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Accurately forecasting a battery’s remaining useful life (RUL) plays an important role in the prognostics and health management of rechargeable batteries. An effective forecast is reported using a particle filter (PF), but it currently suffers from particle degeneracy and impoverishment deficiencies in RUL evaluations. In this paper, an inheritance PF is developed to predict lithium-ion battery RUL for the first time. A battery degradation model is first mapped onto a PF problem using the genetic algorithm (GA) framework. Then, a Lamarckian inheritance operator is designed to improve the light-weight particles by heavy-weight ones and thus to tackle particle degeneracy. In addition, the inheritance mechanism retains certain existing information to tackle particle impoverishment. The performance of the inheritance PF is compared with an elitism GA-based PF. The former has fewer tuning parameters than the latter and is less sensitive to tuning parameters. Both PFs are applied to the prediction of lithium-ion battery RUL, which is validated using capacity degradation data from the NASA Ames Research Center. The experimental results show that the inheritance PF method offers improved RUL prediction and wider applications. Further improvement is obtained with one-step ahead prediction when the charging and discharging cycles move along.

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Selvaraj, Lokesh, and Balakrishnan Ganesan. "Enhancing Speech Recognition Using Improved Particle Swarm Optimization Based Hidden Markov Model." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/270576.

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Enhancing speech recognition is the primary intention of this work. In this paper a novel speech recognition method based on vector quantization and improved particle swarm optimization (IPSO) is suggested. The suggested methodology contains four stages, namely, (i) denoising, (ii) feature mining (iii), vector quantization, and (iv) IPSO based hidden Markov model (HMM) technique (IP-HMM). At first, the speech signals are denoised using median filter. Next, characteristics such as peak, pitch spectrum, Mel frequency Cepstral coefficients (MFCC), mean, standard deviation, and minimum and maximum of the signal are extorted from the denoised signal. Following that, to accomplish the training process, the extracted characteristics are given to genetic algorithm based codebook generation in vector quantization. The initial populations are created by selecting random code vectors from the training set for the codebooks for the genetic algorithm process and IP-HMM helps in doing the recognition. At this point the creativeness will be done in terms of one of the genetic operation crossovers. The proposed speech recognition technique offers 97.14% accuracy.

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Journal articles on the topic 'GENETIC PARTICLE FILTER'

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