Joint Algorithm Based on Interference Suppression and Kalman Filter for Bearing-Only Weak Target Robust Tracking

Chen, Wen; Zhang, Wen; Wu, Yanqun; Chen, Tianyu; Hu, Zhengliang

doi:10.1109/access.2019.2940956

Cited by 12 publications

(6 citation statements)

References 38 publications

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“…In summary, in the lth local filter at time k, the one-step state predicted PDF can be obtained by Equation ( 5), the prior PDF of the measurement loss probability can be obtained by ( 6), the prior PMF of the Bernoulli variable can be obtained by Equation (10), and the conditional likelihood PDF can be obtained by Equation ( 13).…”

Section: Choices Of Prior Distributionsmentioning

confidence: 99%

“…If the measurement loss is known to the estimator, a satisfactory estimation result can be obtained by using an intermittent Kalman filter (IKF) [8]. However, knowledge of the measurement loss is not always available, which makes the existing IKF no longer applicable [9,10]. For the problem of state estimation in a linear system with unknown measurement losses, two different Bayesian Kalman filters are proposed [9].…”

Section: Introductionmentioning

confidence: 99%

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Variational Bayesian‐based adaptive distributed fusion target tracking with unknown sensor measurement losses

Yang

et al. 2021

IET Radar Sonar & Navi

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A new variational Bayesian-based adaptive distributed fusion unscented Kalman filter (ADFUKF-VB) algorithm is proposed for the problem of state estimation in a distributed fusion target tracking system with unknown sensor measurement losses. First, the Bernoulli random variables are introduced as the judgement factors of the measurement losses in each local unscented Kalman filter (UKF) of the distributed fusion target tracking system, and the probability density function (PDF) of the measurement loss probability is modelled as a Beta distribution. Then, the variational Bayesian (VB) approach is used to infer the estimated variables in each local UKF. The measurement losses are judged according to the expectations of the Bernoulli random variables, and three ways are adopted to estimate the state vector based on the obtained judgement results. Finally, the covariance intersection (CI) fusion strategy is used to achieve the fusion of all local state estimates. The simulation results show that the proposed ADFUKF-VB algorithm can accurately judge whether the measurement is lost and estimate the unknown measurement loss probability, moreover, effectively improve the accuracy of state estimation compared with the existing filtering algorithms with unknown measurement losses. K E Y W O R D S sensor fusion, signal processing, state estimation, target trackingThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Choices Of Prior Distributionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variational Bayesian‐based adaptive distributed fusion target tracking with unknown sensor measurement losses

Yang

et al. 2021

IET Radar Sonar & Navi

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“…For the scenario of moving targets, the traditional DOA estimation methods cut the measurement of the output of sonar array signal into small periods in time to process, which ignores the kinematic characteristics of the targets [6][7][8]. The DOA tracking methods not only use the measurement information but also rely on the kinematic characteristics of the underwater targets [9][10][11][12][13][14][15][16][17][18]. Therefore, by not only depending on the current measurements but also utilizing the prior kinematic information of an unknown underwater target, the DOA tracking methods can provide more robust and accurate results than the traditional DOA estimation methods.…”

Section: Introductionmentioning

confidence: 99%

“…Then, based on the framework of the Bayesian filter theory [19], the bearing angle of the target can be recursively estimated from the current measurements. Depending on the bearing angle measurements obtained by the traditional DOA estimation methods, the Kalman filter (KF) is always utilized as the DOA tracking algorithm [9,10] for the linear relationship between the measurement and bearing angle of the target. Besides utilizing the bearing angle measurements, some research take the outputs of sonar array signals as measurements.…”

Section: Introductionmentioning

confidence: 99%

Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Zhang

Hou²,

Yang³

et al. 2023

Front. Phys.

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The direction-of-arrival (DOA) tracking of underwater targets is an important research topic in sonar signal processing. Considering that the underwater DOA tracking is a typical multi-target problem under unknown underwater environment with missing detection, false alarm, and uncertain measurement noise, a robust underwater multi-target DOA tracking method for uncertain measurement noise is proposed. First, a kinematic model of the multiple underwater targets and bearing angle measurement model with missing detection and false alarms are established. Then, the multi-target DOA tracking algorithm is derived by using the cardinalized probability hypothesis density (CPHD) filter, the performance of which largely depends on the accuracy of the parameter of measurement noise variance. In addition, the variational Bayesian approach is used to adaptively estimate the uncertain measurement of noise variance for each measurement of target in the real time of tracking. Thus, the robust underwater multi-target DOA tracking is carried out. Finally, comprehensive experimental validations and discussions are made to prove that the proposed algorithm can provide robust DOA tracking in the multi-target tracking scenario with uncertain measurement noise.

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“…however, this method requires an approximate bearing of the TOI to compute the power ratio. Chen 21 , et al combines the ECA approach and Kalman filter for joint interference suppression and target tracking. Zhang 22 , et al summarises different approaches used for interference suppression in passive sonar and suggests space time-based schemes as prospective futuristic options.…”

Section: Introductionmentioning

confidence: 99%

Cancellation of Towing Ship Interference in Passive SONAR in a Shallow Ocean Environment

et al. 2022

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Towed array sonars are preferred for detecting stealthy underwater targets that emit faint acoustic signals in the ocean, especially in shallow waters. However, the towing ship being near to the array behaves as a loud target, introducing additional interfering signals to the array, severely affecting the detection and classification of potential targets. Canceling this underlying interference signal is a challenging task and is investigated in this paper for a shallow ocean operational scenario where the problem is more critical due to the multipath phenomenon. A method exploiting the eigenvector analysis of spatio-temporal covariance matrix based on space time adaptive processing is proposed for suppressing tow ship interference and thus improving target detection. The developed algorithm learns the interference patterns in the presence of target signals to mitigate the interference across azimuth and to remove the spectral leakage of own-ship. The algorithm is statistically analyzed through a set of relevant metrics and is tested on simulated data that are equivalent to the data received by a towed linear array of acoustic sensors in a shallow ocean. The results indicate a reduction of 20-25dB in the tow ship interference power while the detection of long-range low SNR targets remain largely unaffected with minimal power-loss. In addition, it is demonstrated that the spectral leakage of tow ship, on multiple beams across the azimuth, due to multipath, is also alleviated leading to superior classification capabilities. The robustness of the proposed algorithm is validated by the open ocean experiment in the coastal shallow region of the Arabian Sea at Off-Kochi area of India, which produced results in close agreement with the simulations. A comparison of the simulation and experimental results with the existing PCI and ECA methods is also carried out, suggesting the proposed method is quite effective in suppressing the tow ship interference and is immensely beneficial for the detection and classification of long-range targets.

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Joint Algorithm Based on Interference Suppression and Kalman Filter for Bearing-Only Weak Target Robust Tracking

Cited by 12 publications

References 38 publications

Variational Bayesian‐based adaptive distributed fusion target tracking with unknown sensor measurement losses

Variational Bayesian‐based adaptive distributed fusion target tracking with unknown sensor measurement losses

Variational Bayesian cardinalized probability hypothesis density filter for robust underwater multi-target direction-of-arrival tracking with uncertain measurement noise

Cancellation of Towing Ship Interference in Passive SONAR in a Shallow Ocean Environment

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