Journal articles: 'Sonar tracking'

1

Mandić, Filip, Ivor Rendulić, Nikola Mišković, and Đula Nađ. "Underwater Object Tracking Using Sonar and USBL Measurements." Journal of Sensors 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8070286.

Full text

Abstract:

In the scenario where an underwater vehicle tracks an underwater target, reliable estimation of the target position is required. While USBL measurements provide target position measurements at low but regular update rate, multibeam sonar imagery gives high precision measurements but in a limited field of view. This paper describes the development of the tracking filter that fuses USBL and processed sonar image measurements for tracking underwater targets for the purpose of obtaining reliable tracking estimates at steady rate, even in cases when either sonar or USBL measurements are not available or are faulty. The proposed algorithms significantly increase safety in scenarios where underwater vehicle has to maneuver in close vicinity to human diver who emits air bubbles that can deteriorate tracking performance. In addition to the tracking filter development, special attention is devoted to adaptation of the region of interest within the sonar image by using tracking filter covariance transformation for the purpose of improving detection and avoiding false sonar measurements. Developed algorithms are tested on real experimental data obtained in field conditions. Statistical analysis shows superior performance of the proposed filter compared to conventional tracking using pure USBL or sonar measurements.

APA, Harvard, Vancouver, ISO, and other styles

2

Coraluppi, S., and C. Carthel. "Distributed tracking in multistatic sonar." IEEE Transactions on Aerospace and Electronic Systems 41, no. 3 (July 2005): 1138–47. http://dx.doi.org/10.1109/taes.2005.1541460.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Diebold, Clarice Anna, Angeles Salles, and Cynthia F. Moss. "Adaptive Echolocation and Flight Behaviors in Bats Can Inspire Technology Innovations for Sonar Tracking and Interception." Sensors 20, no. 10 (May 23, 2020): 2958. http://dx.doi.org/10.3390/s20102958.

Full text

Abstract:

Target tracking and interception in a dynamic world proves to be a fundamental challenge faced by both animals and artificial systems. To track moving objects under natural conditions, agents must employ strategies to mitigate interference and conditions of uncertainty. Animal studies of prey tracking and capture reveal biological solutions, which can inspire new technologies, particularly for operations in complex and noisy environments. By reviewing research on target tracking and interception by echolocating bats, we aim to highlight biological solutions that could inform new approaches to artificial sonar tracking and navigation systems. Most bat species use wideband echolocation signals to navigate dense forests and hunt for evasive insects in the dark. Importantly, bats exhibit rapid adaptations in flight trajectory, sonar beam aim, and echolocation signal design, which appear to be key to the success of these animals in a variety of tasks. The rich suite of adaptive behaviors of echolocating bats could be leveraged in new sonar tracking technologies by implementing dynamic sensorimotor feedback control of wideband sonar signal design, head, and ear movements.

APA, Harvard, Vancouver, ISO, and other styles

4

Park, J. Daniel, and John F. Doherty. "A Steganographic Approach to Sonar Tracking." IEEE Journal of Oceanic Engineering 44, no. 4 (October 2019): 1213–27. http://dx.doi.org/10.1109/joe.2018.2847160.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Rukmani, Dr K. V., Lt Dr D. Antony Arul Raj, Ms Lakshana V, Mr Ravishinu G, and Mr Gokul K. "Biomimetic Sonar Innovation Inspired from Dolphins: A Comprehensive Review." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 921–28. http://dx.doi.org/10.22214/ijraset.2024.59836.

Full text

Abstract:

Abstract: The sonar of dolphins has evolved over millions of years and has attained outstanding performance levels. Using the exceptional performance of the dolphins’ sonar as an impetus, bio-inspired wideband acoustic sensing approaches for underwater target recognition and tracking are under development. In this study, we have sight seen what they expect to extract as a gain from such a wideband sonar. The systems wideband sensors are grounded on bottlenose dolphins’ sonar, encapsulating a frequency band from about 30 to 150 kHz and having a frequency reliant on beamwidth substantially larger than that of conventional imaging sonars. The system can be made fairly compact and apt for mounting on diverse platforms including small-scale autonomous underwater automobiles to permit the sonar to function in an analogous way to that used by dolphins. In this paper, we have highlighted the mechanism and applications of the sonar innovation in various domains along with the ongoing developments with regard to it.

APA, Harvard, Vancouver, ISO, and other styles

6

Karpov, Konstantin A., Andrew Lauermann, Mary Bergen, and Michael Prall. "Accuracy and Precision of Measurements of Transect Length and Width Made with a Remotely Operated Vehicle." Marine Technology Society Journal 40, no. 3 (September 1, 2006): 79–85. http://dx.doi.org/10.4031/002533206787353196.

Full text

Abstract:

Remotely operated vehicles (ROVs) have been used to estimate the density of fish and invertebrates on strip transects. However, there is little published information about the accuracy and precision of measurements of transect length and width, a critical component of the density estimate. In this study, we evaluate straight line constant velocity protocols, the accuracy and precision of estimates of transect length from ultra short baseline acoustic tracking, and compare measurements of transect width based on sonar and lasers. When ROV tracking was compared to distances measured on sonar maps, the difference between linear tracked and mapped distance (|LDt-LDm|) averaged 1.7 ± 0.5 m. The error was not significantly different with distance. Our navigation protocols allowed us to maintain relatively constant heading and speed. Distance computed from velocity exceeded mapped distance by 2-4 m. The error was not significantly different with distance. Measurements of transect width made with lasers and sonar were comparable, particularly when the ROV was within 4 m of the substrate. Based on our data, ROV tracking can be used to measure transect length within 2 m. If tracking fails, distance can be estimated from velocity within 2-4 m. Sonar can be used to measure transect width with considerable cost savings.

APA, Harvard, Vancouver, ISO, and other styles

7

Guo, Yu, Yalin Li, Haoyang Tan, Zenghui Zhang, Junxiang Ye, and Chaoqi Ren. "Research on Target Tracking Simulation System Framework for Multi-Static Sonar Buoys." Journal of Physics: Conference Series 2486, no. 1 (May 1, 2023): 012097. http://dx.doi.org/10.1088/1742-6596/2486/1/012097.

Full text

Abstract:

Abstract In the modern war, with the improvement of the target mobility, the increase of the diversity of the target platform, the decrease in the radiation noise of underwater targets, active sonar technology is becoming increasingly important in the underwater target detection. The data processing has also been continuously improved. Especially with the development of information technology in the modern warfare, the target tracking technology is highly valued by various countries around the world, and it has now become a very important field of research. This article proposes a general tracking framework based on the Multi-Static Sonar Buoys System and a variety of tracking algorithms. It lays the foundation for further completing the multi-target tracking simulation system.

APA, Harvard, Vancouver, ISO, and other styles

8

Kuc, Roman. "Three-dimensional tracking using qualitative bionic sonar." Robotics and Autonomous Systems 11, no. 3-4 (December 1993): 213–19. http://dx.doi.org/10.1016/0921-8890(93)90026-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Yan, Jun, Junxia Meng, and Jianhu Zhao. "Real-Time Bottom Tracking Using Side Scan Sonar Data Through One-Dimensional Convolutional Neural Networks." Remote Sensing 12, no. 1 (December 20, 2019): 37. http://dx.doi.org/10.3390/rs12010037.

Full text

Abstract:

As one of the most commonly used acoustic systems in seabed surveys, the altitude of the side scan sonar from the seafloor is always difficult to determine, especially when raw signal levels and gain information are unavailable. The inaccurate sonar altitudes would limit the applications of sonar image geocoding, target detection, and sediment classification. The sonar altitude can be obtained by using bottom tracking methods, but traditional methods often require manual thresholds or complex post-processing procedures, which cannot ensure accurate and real-time bottom tracking. In this paper, a real-time bottom tracking method of side scan data is proposed based on a one-dimensional convolution neural network. First, according to the characteristics of side scan backscatter strength sequences, positive (bottom sequences) and negative (water column and seabed sequences) samples are extracted to establish the sample sets. Second, a one-dimensional convolution neural network is carefully designed and trained by using the sample set to recognize the bottom sequences. Third, a complete processing procedure of the real-time bottom tracking method is established by traversing each side scan ping data and recognizing the bottom sequences. The auxiliary methods for improving real-time performance and sample data augmentation are also explained in detail. The proposed method is implemented on the measured side scan data from the marine area in Meizhou Bay. The trained network model achieves a 100% recognition of the initial sample set as well as 100% bottom tracking accuracy of the training survey line. The average bottom tracking accuracy of the testing survey lines excluding missed pings reaches 99.2%. By comparison with multi-beam bathymetric data and the statistical analysis of real-time performance, the experimental results prove the validity and accuracy of the proposed real-time bottom tracking method.

APA, Harvard, Vancouver, ISO, and other styles

10

Yao, Yu, Junhui Zhao, and Lenan Wu. "Doppler Data Association Scheme for Multi-Target Tracking in an Active Sonar System." Sensors 19, no. 9 (April 29, 2019): 2003. http://dx.doi.org/10.3390/s19092003.

Full text

Abstract:

In many wireless sensors, the target kinematic states include location and Doppler information that can be observed from a time series of range and velocity measurements. In this work, we present a tracking strategy for comprising target velocity components as part of the measurement supplement procedure and evaluate the advantages of the proposed scheme. Data association capability can be considered as the key performance for multi-target tracking in an active sonar system. Then, we proposed an enhanced Doppler data association (DDA) scheme which exploits target range and target velocity components for linear multi-target tracking. If the target velocity measurements are not incorporated into target kinematic state tracking, the linear filter bank for the combination of target velocity components can be implemented. Finally, a significant enhancement in the multi-target tracking capability provided by the proposed DDA scheme with the linear multi-target combined probabilistic data association method is demonstrated in a sonar underwater scenario.

APA, Harvard, Vancouver, ISO, and other styles

11

Sheng, Xueli, Yang Chen, Longxiang Guo, Jingwei Yin, and Xiao Han. "Multitarget Tracking Algorithm Using Multiple GMPHD Filter Data Fusion for Sonar Networks." Sensors 18, no. 10 (September 21, 2018): 3193. http://dx.doi.org/10.3390/s18103193.

Full text

Abstract:

Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm’s performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.

APA, Harvard, Vancouver, ISO, and other styles

12

Wang, Ying, Hongjian Wang, Qing Li, Yao Xiao, and Xicheng Ban. "Passive Sonar Target Tracking Based on Deep Learning." Journal of Marine Science and Engineering 10, no. 2 (January 28, 2022): 181. http://dx.doi.org/10.3390/jmse10020181.

Full text

Abstract:

At present, the tracking accuracy of underwater passive target tracking is often limited due to models that are overly simple, with low complexity, poor universality, and an inability to learn. In this paper, a cubature Kalman filter (CKF) algorithm based on a gated recurrent unit (GRU) network is proposed. The filter innovation, prediction error, and filter gain obtained from the CKF are used as the input to the GRU network, and the filter error value is used as the output to train the network. End-to-end online learning is carried out using the designed fully connected network, and the current state of the target is predicted. In this paper, a deep neural network based on the GRU architecture is used to convert the tracking prediction problem into a time series prediction problem in the field of artificial intelligence, and its strong fitting ability is used to resolve the uncertainty of the target motion. Simulation results show that an unmanned underwater vehicle (UUV) state estimation method based on the GRU filter proposed in this paper offers better accuracy and stability than the traditional state estimation method.

APA, Harvard, Vancouver, ISO, and other styles

13

Liu, Chao, and Shiliang Fang. "An Interactive Transient Model Correction Active Sonar Target Tracking Method." Applied Sciences 14, no. 11 (June 4, 2024): 4865. http://dx.doi.org/10.3390/app14114865.

Full text

Abstract:

Active sonar can usually only directly measure the distance and bearing information of underwater targets, and cannot directly obtain target velocity, acceleration and other information. Therefore, the amount of information is relatively small, making it difficult to support the construction of complex motion models. At the same time, the motion state of underwater maneuvering targets is changeable. In response to the problem of detecting and tracking underwater moving targets by active sonar, this paper proposes a target transient model correction (TMC) filtering tracking method. Based on the conventional Kalman filter (KF) estimation, residual covariance is used as a signal quantity. When there is a large change in it, a transient filter with constant gain is adopted to filter the measurement value. The filtered output is used to correct the KF gain matrix and the target motion state model, to avoid the problem of increasing or even diverging KF estimation errors caused by changes in process noise. Using this method can solve the problem of maintaining stability and filtering estimation accuracy of active sonar tracking of underwater maneuvering targets with less computational and engineering costs.

APA, Harvard, Vancouver, ISO, and other styles

14

Chen, Roujie, Tingting Li, Imran Memon, Yifang Shi, Ihsan Ullah, and Sufyan Ali Memon. "Multi-Sonar Distributed Fusion for Target Detection and Tracking in Marine Environment." Sensors 22, no. 9 (April 27, 2022): 3335. http://dx.doi.org/10.3390/s22093335.

Full text

Abstract:

The multi-sonar distributed fusion system has been pervasively deployed to jointly detect and track marine targets. In the realistic scenario, the origin of locally transmitted tracks is uncertain due to clutter disturbance and the presence of multi-target. Moreover, attributed to the different sonar internal processing times and diverse communication delays between sonar and the fusion center, tracks unavoidably arrive in the fusion center with temporal out-of-sequence (OOS), both problems pose significant challenges to the fusion system. Under the distributed fusion framework with memory, this paper proposes a novel multiple forward prediction-integrated equivalent measurement fusion (MFP-IEMF) method, it fuses the multi-lag OOST with track origin uncertainty in an optimal manner and is capable to be implemented in both the synchronous and asynchronous multi-sonar tracks fusion system. Furthermore, a random central track initialization technique is also proposed to detect the randomly born marine target in time via quickly initiating and confirming true tracks. The numerical results show that the proposed algorithm achieves the same optimality as the existing OOS reprocessing method, and delivers substantially improved detection and tracking performance in terms of both ANCTT and estimation accuracy compared to the existing OOST discarding fusion method and the ANF-IFPFD method.

APA, Harvard, Vancouver, ISO, and other styles

15

Zhang, Tiedong, Shuwei Liu, Xiao He, Hai Huang, and Kangda Hao. "Underwater Target Tracking Using Forward-Looking Sonar for Autonomous Underwater Vehicles." Sensors 20, no. 1 (December 23, 2019): 102. http://dx.doi.org/10.3390/s20010102.

Full text

Abstract:

In the scenario where autonomous underwater vehicles (AUVs) carry out tasks, it is necessary to reliably estimate underwater-moving-target positioning. While cameras often give low-precision visibility in a limited field of view, the forward-looking sonar is still an attractive method for underwater sensing, which is especially effective for long-range tracking. This paper describes an online processing framework based on forward-looking-sonar (FLS) images, and presents a novel tracking approach based on a Gaussian particle filter (GPF) to resolve persistent multiple-target tracking in cluttered environments. First, the character of acoustic-vision images is considered, and methods of median filtering and region-growing segmentation were modified to improve image-processing results. Second, a generalized regression neural network was adopted to evaluate multiple features of target regions, and a representation of feature subsets was created to improve tracking performance. Thus, an adaptive fusion strategy is introduced to integrate feature cues into the observation model, and the complete procedure of underwater target tracking based on GPF is displayed. Results obtained on a real acoustic-vision AUV platform during sea trials are shown and discussed. These showed that the proposed method is feasible and effective in tracking targets in complex underwater environments.

APA, Harvard, Vancouver, ISO, and other styles

16

Yan, Jun, Junxia Meng, and Jianhu Zhao. "Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet." Remote Sensing 13, no. 5 (March 8, 2021): 1024. http://dx.doi.org/10.3390/rs13051024.

Full text

Abstract:

As widely applicated in many underwater research fields, conventional side-scan sonars require the sonar height to be at the seabed for geocoding seabed images. However, many interference factors, including compensation with unknown gains, suspended matters, etc., would bring difficulties in bottom detection. Existing methods need manual parameter setups or to use postprocessing methods, which limits automatic and real-time processing in complex situations. To solve this problem, a one-dimensional U-Net (1D-UNet) model for sea bottom detection of side-scan data and the bottom detection and tracking method based on 1D-UNet are proposed in this work. First, the basic theory of sonar bottom detection and the interference factors is introduced, which indicates that deep learning of the bottom is a feasible solution. Then, a 1D-UNet model for detecting the sea bottom position from the side-scan backscatter strength sequences is proposed, and the structure and implementation of this model are illustrated in detail. Finally, the bottom detection and tracking algorithms of a single ping and continuous pings are presented on the basis of the proposed model. The measured side-scan sonar data in Meizhou Bay and Bayuquan District were selected in the experiments to verify the model and methods. The 1D-UNet model was first trained and applied with the side-scan data in Meizhou Bay. The training and validation accuracies were 99.92% and 99.77%, respectively, and the sea bottom detection accuracy of the training survey line was 99.88%. The 1D-UNet model showed good robustness to the interference factors of bottom detection and fully real-time performance in comparison with other methods. Moreover, the trained 1D-UNet model is used to process the data in the Bayuquan District for proving model generality. The proposed 1D-UNet model for bottom detection has been proven effective for side-scan sonar data and also has great potentials in wider applications on other types of sonars.

APA, Harvard, Vancouver, ISO, and other styles

17

Lakshmi, Bejjam Naga, and Halavath Balaji. "Tracking of Under Water Objects using Particle Filter Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 794–806. http://dx.doi.org/10.22214/ijraset.2023.48929.

Full text

Abstract:

Abstract: It is difficult to track objects in under water, marine sciences and home security. To track the motion of objects which are in linear and gaussian system it will be quite difficult for trackers. The optics camera is subject to effect light and opaque, its visibility is very poor in under water. The other solution for optics camera is Forward sonar images, but the resulted tracked sonar images are with high noise and low contrast. In addition to provide effective result for tracked images we are using Particle filter algorithm. In this article we are modifying the traditional systems by tracking the motion of non-linear and nongaussian system by using particle filter algorithm. To solve non-Gaussian problems an image processing is used to detect and track the underwater object automatically.

APA, Harvard, Vancouver, ISO, and other styles

18

Handegard, Nils Olav, and Kresimir Williams. "Automated tracking of fish in trawls using the DIDSON (Dual frequency IDentification SONar)." ICES Journal of Marine Science 65, no. 4 (March 11, 2008): 636–44. http://dx.doi.org/10.1093/icesjms/fsn029.

Full text

Abstract:

Abstract Handegard, N. O., and Williams, K. 2008. Automated tracking of fish in trawls using the DIDSON (Dual frequency IDentification SONar). – ICES Journal of Marine Science. 65: 636–644. An application for the automated tracking of dual-frequency, identification sonar (DIDSON) data was developed and tested on fish observations taken in midwater trawls. The process incorporates target detection, multiple target tracking, and the extraction of behaviour information such as target speed and direction from the track data. The automatic tracker was evaluated using three test datasets with different target sizes, observation ranges, and densities. The targets in the datasets were tracked manually and with the automated tracker, using the manual-tracking results as the standard for estimating the performance of the automated tracking process. In the first and third dataset, where the targets were smaller and less dense, the automated tracking performed well, correctly identifying 74% and 57% of targets, respectively, and associating targets into tracks with <10% error compared with the manually tracked data. In the second dataset, where targets were dense and appeared large owing to the shorter observation range, 45% of targets were correctly identified, and the track error rate was 21%. Target speed and direction, derived from the tracking data, agreed well between the manual and automatic methods for all three test cases. Automated tracking represents a useful technique for processing DIDSON data, and a valuable alternative to time-consuming, manual data-processing, when used in appropriate conditions.

APA, Harvard, Vancouver, ISO, and other styles

19

Zheng, Gen, Hongmei Zhang, Yuqing Li, and Jianhu Zhao. "A Universal Automatic Bottom Tracking Method of Side Scan Sonar Data Based on Semantic Segmentation." Remote Sensing 13, no. 10 (May 17, 2021): 1945. http://dx.doi.org/10.3390/rs13101945.

Full text

Abstract:

Determining the altitude of side-scan sonar (SSS) above the seabed is critical to correct the geometric distortions in the sonar images. Usually, a technology named bottom tracking is applied to estimate the distance between the sonar and the seafloor. However, the traditional methods for bottom tracking often require pre-defined thresholds and complex optimization processes, which make it difficult to achieve ideal results in complex underwater environments without manual intervention. In this paper, a universal automatic bottom tracking method is proposed based on semantic segmentation. First, the waterfall images generated from SSS backscatter sequences are labeled as water column (WC) and seabed parts, then split into specific patches to build the training dataset. Second, a symmetrical information synthesis module (SISM) is designed and added to DeepLabv3+, which not only weakens the strong echoes in the WC area, but also gives the network the capability of considering the symmetry characteristic of bottom lines, and most importantly, the independent module can be easily combined with any other neural networks. Then, the integrated network is trained with the established dataset. Third, a coarse-to-fine segmentation strategy with the well-trained model is proposed to segment the SSS waterfall images quickly and accurately. Besides, a fast bottom line search algorithm is proposed to further reduce the time consumption of bottom tracking. Finally, the proposed method is validated by the data measured with several commonly used SSSs in various underwater environments. The results show that the proposed method can achieve the bottom tracking accuracy of 1.1 pixels of mean error and 1.26 pixels of standard deviation at the speed of 2128 ping/s, and is robust to interference factors.

APA, Harvard, Vancouver, ISO, and other styles

20

Chen, Xiao, Yaan Li, Yuxing Li, and Jing Yu. "Active Sonar Target Tracking Based on the GM-CPHD Filter Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 4 (August 2018): 656–63. http://dx.doi.org/10.1051/jnwpu/20183640656.

Full text

Abstract:

The estimation of underwater multi-target state has always been the difficult problem of active sonar target tracking.In order to get the variable number of target and their state, the random finite set theory is applied to multi-target tracking system.This theory not only effectively avoids the problem of multi-target tracking data association, and also realizes the estimation of time-varying number of targets and their states.Due to Probability Hypothesis Density(PHD) recursion propagates cardnality distribution with only a single parameter, a new generalization of the PHD recursion called Cardinalized Probability Hypothesis Density(CPHD) recursion, which jointly propagates the intensity function and the cardnality distribution, while have a big computation than PHD.Also there did not have closed-form solution for PHD recursion and CPHD recursion, so for linear Gaussian multi-target tracking system, the Gaussian Mixture Probability Hypothesis Density and Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD) filter algorithm is put forward.GM-CPHD is more accurate than GM-PHD in estimation of the time-varying number of targets.In this paper, we use the ellipse gate tracking strategy to reduce computation in GM-CPHD filtering algorithm.At the same time, according to the characteristics of underwater target tracking, using active sonar equation, we get the relationship between detection probability, distance and false alarm, when fixed false alarm, analytic formula of the relationship between adaptive detection probability and distance is obtained, we puts forward the adaptive detection probability GM-CPHD filtering algorithm.Simulation shows that the combination of ellipse tracking gate strategy and adaptive detection probability GM-CPHD filtering algorithm can realize the estimation of the time-varying number of targets and their state more accuracy in dense clutter environment.

APA, Harvard, Vancouver, ISO, and other styles

21

Pailhas, Yan, Jeremie Houssineau, Yvan René Petillot, and Daniel Edward Clark. "Tracking with MIMO sonar systems: applications to harbour surveillance." IET Radar, Sonar & Navigation 11, no. 4 (April 2017): 629–39. http://dx.doi.org/10.1049/iet-rsn.2016.0080.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Cho, Jung-Hong, Hyoung Rok Kim, Seongil Kim, and Jea Soo Kim. "Measure of Effectiveness Analysis for Tracking in SONAR System." Journal of the Korea Institute of Military Science and Technology 16, no. 1 (February 5, 2013): 5–26. http://dx.doi.org/10.9766/kimst.2013.16.1.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Galloway, James L., and Humfrey Melling. "Tracking the Motion of Sea Ice by Correlation Sonar." Journal of Atmospheric and Oceanic Technology 14, no. 3 (June 1997): 616–29. http://dx.doi.org/10.1175/1520-0426(1997)014<0616:ttmosi>2.0.co;2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Jaffe, Jules S., Edward Reuss, Duncan McGehee, and Girish Chandran. "FTV: a sonar for tracking macrozooplankton in three dimensions." Deep Sea Research Part I: Oceanographic Research Papers 42, no. 8 (August 1995): 1495–512. http://dx.doi.org/10.1016/0967-0637(95)00030-a.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Varadarajan, V., and J. Krolik. "Array shape estimation and tracking using active sonar reverberation." IEEE Transactions on Aerospace and Electronic Systems 40, no. 3 (July 2004): 1073–86. http://dx.doi.org/10.1109/taes.2004.1337475.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Jeong, TaIkyeong T. "Particle PHD filter multiple target tracking in sonar image." IEEE Transactions on Aerospace and Electronic Systems 43, no. 1 (January 2007): 409–16. http://dx.doi.org/10.1109/taes.2007.357143.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Rouseff, Daniel, Scott Schecklman, and Jorge Quijano. "Lisa Zurk’s contributions to striation-based signal processing for active sonar." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A242. http://dx.doi.org/10.1121/10.0016145.

Full text

Abstract:

Strong multipath propagation adds considerable complication to acoustic signal processing in the ocean. When mapped in range-frequency space, the acoustic field exhibits striations, alternating bands of high and low intensity due to constructive and destructive interference between the paths. Nearly forty years ago, Russian scientists showed how this striation pattern could be described by a single scalar parameter, the so-called waveguide invariant that subsequently became a staple of their passive sonar signal processing methods. Lisa Zurk’s contribution was to show how a striation-based approach could be adapted to active sonar processing. Together with her students, she did tank experiments and analysis for monostatic and bistatic configurations. For a horizontal array, she developed a beamformer featuring a linear frequency shift across the array designed to align with high-intensity striations. The result was improved performance in noisy environments. She showed how the waveguide invariant can improve tracking accuracy by providing a constraint on possible tracks. She and her students demonstrated the improved tracking with continuous active sonar data. Lisa Zurk’s innovative work on these topics continues to inspire present day research.

APA, Harvard, Vancouver, ISO, and other styles

28

Kazimierski, Witold, and Grzegorz Zaniewicz. "Determination of Process Noise for Underwater Target Tracking with Forward Looking Sonar." Remote Sensing 13, no. 5 (March 8, 2021): 1014. http://dx.doi.org/10.3390/rs13051014.

Full text

Abstract:

Target tracking is a process that provides information about targets in a specific area and is one of the key issues affecting the safety of any vehicle navigating in water. The main sensor used for underwater target tracking is sonar, with one of the most popular configurations being forward looking sonar (FLS). The target tracking state vector is usually estimated with the use of numerical filter algorithms, such as the Kalman filter (KF) and its modification, or the particle filter (PF). This requires the definition of a process model, including process noise, and a measurement model. This study focused on process noise definition. It is usually implemented as Gaussian noise, with a covariance matrix defined by the author. An analytical and empirical analysis was conducted, including a verification of the existing approaches and a survey of the published literature. Additionally, a theoretical analysis of the factors influencing process noise was conducted, which was followed by an empirical verification. The results were discussed, leading to the conclusions. The results of the theoretical analysis were confirmed by the empirical experiment and the results were compared with commonly used values of process noise in underwater target tracking processes.

APA, Harvard, Vancouver, ISO, and other styles

29

Lubis, Muhammad Zainuddin, Husnul Kausarian, and Wenang Anurogo. "Seabed Detection Using Application Of Image Side Scan Sonar Instrument (Acoustic Signal)." Journal of Geoscience, Engineering, Environment, and Technology 2, no. 3 (September 1, 2017): 230. http://dx.doi.org/10.24273/jgeet.2017.2.3.560.

Full text

Abstract:

The importance of knowing the method for seabed detection using side-scan sonar images with sonar instrument is a much-needed requirement right now. This kind of threat also requires frequent sonar surveys in such areas. These survey operations need specific procedures and special equipment to ensure survey correctness. In this paper describes the method of observation and retrieval of marine imagery data using an acoustic signal method, to determine a target based on the sea. Side scan sonar is an instrument consisting of single beam transducer on both sides. Side scan sonar (SSS) is a sonar development that is able to show in two-dimensional images of the seabed surface with seawater conditions and target targets simultaneously. The side scan sonar data processing is performed through geometric correction to establish the actual position of the image pixel, which consists of bottom tracking, slant-range correction, layback correction and radiometric correction performed for the backscatter intensity of the digital number assigned to each pixel including the Beam Angle Correction (BAC), Automatic Gain Control (AGC), Time Varied Gain (TVG), and Empirical Gain Normalization (EGN).

APA, Harvard, Vancouver, ISO, and other styles

30

Leary, Kate C. P., and Daniel Buscombe. "Estimating sand bed load in rivers by tracking dunes: a comparison of methods based on bed elevation time series." Earth Surface Dynamics 8, no. 1 (February 21, 2020): 161–72. http://dx.doi.org/10.5194/esurf-8-161-2020.

Full text

Abstract:

Abstract. Quantifying bed-load transport is paramount to the effective management of rivers with sand or gravel-dominated bed material. However, a practical and scalable field methodology for reliably estimating bed load remains elusive. A popular approach involves calculating transport from the geometry and celerity of migrating bedforms, extracted from time series of bed elevation profiles (BEPs) acquired using echo sounders. There are various echo sounder sampling methodologies to extract bed elevation profiles. Using two sets of repeat multibeam sonar surveys with high spatiotemporal resolution and coverage, we compute bed load using three field techniques (one actual and two simulated) for acquiring BEPs: repeat multibeam, single-beam, and multiple single-beam sonar. Significant differences in flux arise between repeat multibeam and single-beam sonar. Multibeam and multiple single-beam sonar systems can potentially yield comparable results, but the latter relies on knowledge of bedform geometries and flow that collectively inform optimal beam spacing and sampling rate. These results serve as a guide for design of optimal sampling and for comparing transport estimates from different sonar configurations.

APA, Harvard, Vancouver, ISO, and other styles

31

Neupane, Dhiraj, and Jongwon Seok. "A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition." Electronics 9, no. 11 (November 22, 2020): 1972. http://dx.doi.org/10.3390/electronics9111972.

Full text

Abstract:

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

APA, Harvard, Vancouver, ISO, and other styles

32

Michalopoulou, Zoi-Heleni. "Tracking in ocean acoustics: Insights from the work of Lisa Zurk." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A243. http://dx.doi.org/10.1121/10.0016148.

Full text

Abstract:

Tracking is of paramount importance in ocean acoustics. One of the goals is the continuous location estimation of moving sources. Tracking needs also arise in identifying the structure of dispersion curves for long-range sound propagation and multipath arrival time identification across vertically separated hydrophones. Zurk’s work has shown that tracking can play a significant role in invariance estimation from striation patterns in spectrograms. We investigated ideas from tracking across the spectrum of ocean acoustics and looked into this latter problem. We found novel approaches for the estimation of the passive and active waveguide invariants, building on a seminal contribution of Lisa Zurk in the field of sonar signal processing.

APA, Harvard, Vancouver, ISO, and other styles

33

Yang, Yongshou, and Shiliang Fang. "Improved Velocity Estimation Method for Doppler Sonar Based on Accuracy Evaluation and Selection." Journal of Marine Science and Engineering 9, no. 6 (May 26, 2021): 576. http://dx.doi.org/10.3390/jmse9060576.

Full text

Abstract:

The matched filtering method and the waveform-tracking method cannot maintain optimal velocity estimation performance all of the time. In order to solve this problem, this paper proposes an improved velocity estimation method for Doppler sonar, based on accuracy evaluation and selection. The echo of Doppler sonar is divided into several segments with the same width as the transmitted pulse, and each segment is regarded as the echo of the corresponding water layer. According to our study’s results, the velocity estimation accuracy of each segment is positively correlated with the ratio of its autocorrelation modulus to its power. Based on this conclusion, a velocity accuracy criterion with high accuracy and low complexity is designed in order to select the optimal velocity estimation for water layers or bottoms. The proposed accuracy selection method flexibly selects the echo interval to be processed according to the accuracy criterion, so as to maintain the optimal estimation of the current’s or bottom’s velocity. Water tank and field experiments using a prototype Doppler sonar device demonstrates that, compared with the matched filtering method and the waveform-tracking method, the average velocity estimation accuracy and bias of the proposed method are superior.

APA, Harvard, Vancouver, ISO, and other styles

34

Karthik, S., and V. Annapoorani. "Recognition And Tracking Of Moving Object In Underwtaer Sonar Images." International Journal of MC Square Scientific Research 8, no. 1 (June 15, 2016): 93–98. http://dx.doi.org/10.20894/ijmsr.117.008.001.010.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Sharaga, Nathan, Joseph Tabrikian, and Hagit Messer. "Optimal Cognitive Beamforming for Target Tracking in MIMO Radar/Sonar." IEEE Journal of Selected Topics in Signal Processing 9, no. 8 (December 2015): 1440–50. http://dx.doi.org/10.1109/jstsp.2015.2467354.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

De Robertis, Alex, Chad Schell, and Jules S. Jaffe. "Three‐dimensional acoustic tracking of krill with a multibeam sonar." Journal of the Acoustical Society of America 108, no. 5 (November 2000): 2469. http://dx.doi.org/10.1121/1.4743105.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Mellema, Garfield R. "Improved Active Sonar Tracking in Clutter Using Integrated Feature Data." IEEE Journal of Oceanic Engineering 45, no. 1 (January 2020): 304–18. http://dx.doi.org/10.1109/joe.2018.2870234.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Ciuonzo, Domenico, Peter K. Willett, and Yaakov Bar-Shalom. "Tracking the Tracker from its Passive Sonar ML-PDA Estimates." IEEE Transactions on Aerospace and Electronic Systems 50, no. 1 (January 2014): 573–90. http://dx.doi.org/10.1109/taes.2013.120407.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Kothari, Ninad B., Melville J. Wohlgemuth, and Cynthia F. Moss. "Adaptive sonar call timing supports target tracking in echolocating bats." Journal of Experimental Biology 221, no. 18 (July 11, 2018): jeb176537. http://dx.doi.org/10.1242/jeb.176537.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Trucco, E., Y. R. Petillot, I. Tena Ruiz, K. Plakas, and D. M. Lane. "Feature Tracking in Video and Sonar Subsea Sequences with Applications." Computer Vision and Image Understanding 79, no. 1 (July 2000): 92–122. http://dx.doi.org/10.1006/cviu.2000.0846.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Sheng, Mingwei, Songqi Tang, Hongde Qin, and Lei Wan. "Clustering Cloud-Like Model-Based Targets Underwater Tracking for AUVs." Sensors 19, no. 2 (January 17, 2019): 370. http://dx.doi.org/10.3390/s19020370.

Full text

Abstract:

Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.

APA, Harvard, Vancouver, ISO, and other styles

42

Sun, Yong, and Jun Wei Zhao. "An Improved of Extended Kalman Filtering Method on Tracking Accuracy of Bistatic Sonar System." Applied Mechanics and Materials 596 (July 2014): 494–97. http://dx.doi.org/10.4028/www.scientific.net/amm.596.494.

Full text

Abstract:

The Extended Kalman Filter methods were widely used in the estimation of tracing situation in military fields. In this paper, we proposed a method of using multiple iteration of the observation and covariance matrix in the measuring equations during the tracking process in bistatic sonar system. Therefore the iterating extended kalman filtering (IEKF) algorithm was emerged at this situation. The simulation results show that the proposed tracing algorithm exhibits higher accuracy compared with the EKF algorithm. This new method can take full application of the measured information to improved the tracing accuracy in the whole controlled area. Keywords: bistatic sonar; tracing accuracy; IEKF algorithm; target moving analysis

APA, Harvard, Vancouver, ISO, and other styles

43

Zhang, Tie Dong, Shan Ma, Lei Wan, and Yong Jie Pang. "Semi-Physical Acoustic Vision Simulation System of Autonomous Underwater Vehicle." Applied Mechanics and Materials 128-129 (October 2011): 1006–9. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1006.

Full text

Abstract:

Semi-physical acoustic vision simulation system of autonomous underwater vehicle (AUV) was established by combining physical simulation of decision layer with virtual simulation of sensory and executive layer. The hardware and software architecture of the simulation system were explained in detail. The virtual simulation of sonar sensor and the design of sonar vision processing system of the physical simulation were described. Finally, the long distance obstacle avoidance simulation, special objects search simulation and multi-objects tracking simulation tests were conducted to demonstrate the importance of the system to the success of sea experiments.

APA, Harvard, Vancouver, ISO, and other styles

44

FU, ZUQIANG, ERIC M. DOWLING, and RONALD D. DeGROAT. "SPHERICAL SUBSPACE TRACKING ON SYSTOLIC ARRAYS." International Journal of High Speed Electronics and Systems 06, no. 02 (June 1995): 395–418. http://dx.doi.org/10.1142/s0129156495000110.

Full text

Abstract:

The spherical subspace tracker is a low complexity adaptive algorithm that converges to the dominant (or subdominant) subspace. Spherical subspace updating is a fast way to monitor slowly time-varying subspaces, which arise in high resolution direction of arrival tracking algorithms. In real time sonar and radar array processing applications, it becomes necessary to find parallel algorithms and pipelined architectures that provide high speed solutions to these tracking problems. In this paper the spherical subspace tracker is parallelized and mapped onto systolic array architectures. The arrays are ideally suited for implementation with custom VLSI or networks of available parallel numeric processing chips such as iWarps and TMS320C40s. C-language like cell programs specify the functionality and timing of the systolic array in concrete terms.

APA, Harvard, Vancouver, ISO, and other styles

45

Salles, Angeles, Clarice Anna Diebold, and Cynthia F. Moss. "Echolocating bats accumulate information from acoustic snapshots to predict auditory object motion." Proceedings of the National Academy of Sciences 117, no. 46 (November 2, 2020): 29229–38. http://dx.doi.org/10.1073/pnas.2011719117.

Full text

Abstract:

Unlike other predators that use vision as their primary sensory system, bats compute the three-dimensional (3D) position of flying insects from discrete echo snapshots, which raises questions about the strategies they employ to track and intercept erratically moving prey from interrupted sensory information. Here, we devised an ethologically inspired behavioral paradigm to directly test the hypothesis that echolocating bats build internal prediction models from dynamic acoustic stimuli to anticipate the future location of moving auditory targets. We quantified the direction of the bat’s head/sonar beam aim and echolocation call rate as it tracked a target that moved across its sonar field and applied mathematical models to differentiate between nonpredictive and predictive tracking behaviors. We discovered that big brown bats accumulate information across echo sequences to anticipate an auditory target’s future position. Further, when a moving target is hidden from view by an occluder during a portion of its trajectory, the bat continues to track its position using an internal model of the target’s motion path. Our findings also reveal that the bat increases sonar call rate when its prediction of target trajectory is violated by a sudden change in target velocity. This shows that the bat rapidly adapts its sonar behavior to update internal models of auditory target trajectories, which would enable tracking of evasive prey. Collectively, these results demonstrate that the echolocating big brown bat integrates acoustic snapshots over time to build prediction models of a moving auditory target’s trajectory and enable prey capture under conditions of uncertainty.

APA, Harvard, Vancouver, ISO, and other styles

46

Wang, Maofa, Baochun Qiu, Zefei Zhu, Li Ma, and Chuanping Zhou. "Passive tracking of underwater acoustic targets based on multi-beam LOFAR and deep learning." PLOS ONE 17, no. 12 (December 1, 2022): e0273898. http://dx.doi.org/10.1371/journal.pone.0273898.

Full text

Abstract:

Conventional passive tracking methods for underwater acoustic targets in sonar engineering generate time azimuth histogram and use it as a basis for target azimuth and tracking. Passive underwater acoustic targets only have azimuth information on the time azimuth histogram, which is easy to be lost and disturbed by ocean noise. To improve the accuracy of passive tracking, we propose to adopt the processed multi-beam Low Frequency Analysis and Recording (LOFAR) as the dataset for passive tracking. In this paper, an improved LeNet-5 convolutional neural network model (CNN) model is used to identify targets, and a passive tracking method for underwater acoustic targets based on multi-beam LOFAR and deep learning is proposed, combined with Extended Kalman Filter (EKF) to improve the tracking accuracy. The performance of the method under realistic conditions is evaluated through simulation analysis and validation using data obtained from marine experiments.

APA, Harvard, Vancouver, ISO, and other styles

47

Marston, Timothy M., Bernard R. Hall, Christopher Bassett, Daniel S. Plotnick, and Autumn N. Kidwell. "Motion tracking of fish and bubble clouds in synthetic aperture sonar data." Journal of the Acoustical Society of America 155, no. 3 (March 1, 2024): 2181–91. http://dx.doi.org/10.1121/10.0025384.

Full text

Abstract:

Data captured by a Synthetic Aperture Sonar (SAS) near Mobile Bay during the 2021 Undersea Remote Sensing experiment funded by the Office of Naval Research reveals near surface bubble clouds from wave breaking events and a large aggregation of fish. Tools developed for using SAS data to image hydrodynamic features in the water column were applied to observations of the bubble clouds and fish aggregation. Combining imagery and height data captured by the sonar array with a detection and tracking algorithm enables the trajectories, velocities, and behavior of fish in the aggregation to be observed. Fitting the velocity and height data of the tracked objects to a Gaussian mixture model and performing cluster analysis enables an estimate of the near-surface ambient velocity via observation of the movement of the bubble traces and the general direction of motion of the fish aggregation. We find that the velocity traces associated with bubbles are consistent with ambient currents as opposed to the direction of propagating wave crests while velocities of fish indicate relatively large, pelagic species.

APA, Harvard, Vancouver, ISO, and other styles

48

Bagley, P. M., A. Smith, and I. G. Priede. "Tracking movements of deep demersal fishes in the Porcupine Seabight, north-east Atlantic Ocean." Journal of the Marine Biological Association of the United Kingdom 74, no. 3 (August 1994): 473–80. http://dx.doi.org/10.1017/s0025315400047603.

Full text

Abstract:

Miniature acoustic transponders wrapped in bait were deployed on the sea floor in the continental rise and slope regions of the Porcupine Seabight during August 1992. These were ingested by Centroscymnus coelolepis (Chondrichthyes, Selachii) at 1517–1650 m depth, Antimora rostrata (Osteichthyes, Moridae) at 2020–2501 m depth, and Coryphaenoides (Nematonurus) armatus (Osteichthyes, Macrouridae) at 2501–4050 m depth. Fish with baits in their stomachs were tracked using a scanning sonar deployed on the sea floor. All fish had moved out of range of the sonar (500 m) within 3–9 h of the bait reaching the sea floor, indicating no site fidelity. Swimming speed of C. (N.) armatus increased with depth from 0056 m s-1 at 2500 m to 0·109 m s-1 at 4000 m. This is partially explained by a bigger-deeper trend in fish size.

APA, Harvard, Vancouver, ISO, and other styles

49

Wang, Xingmei, Guoqiang Wang, Zhonghua Zhao, Yue Zhang, and Binghua Duan. "An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking." Applied Sciences 8, no. 11 (November 3, 2018): 2154. http://dx.doi.org/10.3390/app8112154.

Full text

Abstract:

To obtain accurate underwater target tracking results, an improved kernelized correlation filter (IKCF) algorithm is proposed to track the target in forward-looking sonar image sequences. Specifically, a base sample with a dynamically continuous scale is first applied to solve the poor performance of fixed-scale filters. Then, in order to prevent the filter from drifting when the target disappears and appears again, an adaptive filter update strategy with the peak to sidelobe ratio (PSR) of the response diagram is developed to solve the following target tracking errors. Finally, the experimental results show that the proposed IKCF can obtain accurate tracking results for the underwater targets. Compared to other algorithms, the proposed IKCF has obvious superiority and effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

50

Zhang, Yupeng, Hongwei Zhang, Jun Liu, Shitong Zhang, Zhi Liu, Enmou Lyu, and Weiyu Chen. "Submarine pipeline tracking technology based on AUVs with forward looking sonar." Applied Ocean Research 122 (May 2022): 103128. http://dx.doi.org/10.1016/j.apor.2022.103128.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Sonar tracking'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles