Particle filtering based track-before-detect method for passive array sonar systems

Yi, Wei; Fu, Lingzhi; García-Fernández, Ángel F.; Xu, Luxiao; Kong, Lingjiang

doi:10.1016/j.sigpro.2019.07.027

Cited by 62 publications

(22 citation statements)

References 50 publications

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“…Conventional RSN processing features a CFAR detection step at single sensor level followed by a point detection step at FC level. Target tracking methods (e.g., [8]- [23], [25]- [35]), fed with the detected points, can then be applied to generate the target tracks. In UWB RSNs, however, this approach suffers from the fact that weak target echo components may be filter out throughout CFAR detection in any sensor or in point generation at the FC.…”

Section: Filteringmentioning

confidence: 99%

A Track-Before-Detect Algorithm for UWB Radar Sensor Networks

Yan

Giorgetti

2021

Preprint

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Precise localization and tracking of moving non-collaborative persons and objects using a network of ultra-wideband (UWB) radar nodes has been shown to represent a practical and effective approach. In UWB radar sensor networks (RSNs), existence of strong clutter, weak target echoes, and closely spaced targets are obstacles to achieving a satisfactory tracking performance. Using a track-before-detect (TBD) approach, the waveform obtained by each node during a time period are jointly processed. Both spatial information and temporal relationship between measurements are exploited in generating all possible candidate trajectories and only the best trajectories are selected as the outcome. The effectiveness of the developed TBD technique for UWB RSNs is confirmed by numerical simulations and by two experimental results, both carried out with actual UWB signals. In the first experiment, a human target is tracked by a monostatic radar network with an average localization error of 41.9 cm with no false alarm trajectory in a cluttered outdoor environment. In the second experiment, two targets are detected by multistatic radar network with localization errors of 25.4 cm and 19.7 cm, and detection rate of the two targets is 88.75%, and no false alarm trajectory.

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Section: Filteringmentioning

confidence: 99%

A Track-Before-Detect Algorithm for UWB Radar Sensor Networks

Yan

Giorgetti

2021

Preprint

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“…Two kinds of tracking mechanisms can be applied to underwater target tracking scenarios, namely, active tracking [ 3 , 4 ] and passive tracking [ 5 , 6 , 7 ]. By utilizing the active sonar systems mounted on facility platforms (usually ship-borne or shore-based platforms), the active tracking system can track an underwater target with high precision in terms of both angle information and range information.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Two-Step Bearing-Only Underwater Uncooperative Target Tracking with Uncertain Underwater Disturbances

Hou

Zhou

Yang

et al. 2021

Entropy

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The bearing-only tracking of an underwater uncooperative target can protect maritime territories and allows for the utilization of sea resources. Considering the influences of an unknown underwater environment, this work aimed to estimate 2-D locations and velocities of an underwater target with uncertain underwater disturbances. In this paper, an adaptive two-step bearing-only underwater uncooperative target tracking filter (ATSF) for uncertain underwater disturbances is proposed. Considering the nonlinearities of the target’s kinematics and the bearing-only measurements, in addition to the uncertain noise caused by an unknown underwater environment, the proposed ATSF consists of two major components, namely, an online noise estimator and a robust extended two-step filter. First, using a modified Sage-Husa online noise estimator, the uncertain process and measurement noise are estimated at each tracking step. Then, by adopting an extended state and by using a robust negative matrix-correcting method in conjunction with a regularized Newton-Gauss iteration scheme, the current state of the underwater uncooperative target is estimated. Finally, the proposed ATSF was tested via simulations of a 2-D underwater uncooperative target tracking scenario. The Monte Carlo simulation results demonstrated the reliability and accuracy of the proposed ATSF in bearing-only underwater uncooperative tracking missions.

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“…Hence, the active underwater tracking system lacks mobility and is easy to be discovered by the uncooperative target or third-party surveillance systems so that the target can make maneuvers to avoid the detections. Considering the increasingly complex modern underwater attacking and defensive environment, the passive tracking techniques seized the attentions from many researchers [ 3 , 4 , 5 ]. Passive tracking techniques only utilize the radiation noise from the target to make parameter estimations without sending any detecting signals during the tracking period.…”

Section: Introductionmentioning

confidence: 99%

3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

Hou

Zhou

Yang

et al. 2021

Entropy

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Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer–Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target’s dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.

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Particle filtering based track-before-detect method for passive array sonar systems

Cited by 62 publications

References 50 publications

A Track-Before-Detect Algorithm for UWB Radar Sensor Networks

A Track-Before-Detect Algorithm for UWB Radar Sensor Networks

Adaptive Two-Step Bearing-Only Underwater Uncooperative Target Tracking with Uncertain Underwater Disturbances

3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

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