Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach

Lan, Hua; Sun, Shang; Wang, Zengfu; Pan, Quan; Zhang, Zhishan

doi:10.1109/taes.2019.2942706

Cited by 25 publications

(12 citation statements)

References 43 publications

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“…Analyzing the above derivation, it can be seen that the implicit solution of the variational update formula is constituted by the equations (22), (24), (25) and 29-(32). The expected maximum approach is used to iteratively calculate ( ) and ( ) to update the parameters and to be estimated continuously.…”

Section: Variational Update Processmentioning

confidence: 99%

“…But its adjustment ability for each filtering channel is the same, and PNCM and MNCM are not estimated [21,22] In recent years, many scholars have introduced the variational Bayesian machine learning method into the KF algorithm and proposed adaptive Kalman filter (VB-KF) algorithm based on the variational Bayesian approach, which is an approximation of the Bayesian method. By choosing a suitable conjugate prior distribution, the slowly time-varying measurement noise covariance can be estimated [23][24][25]. Literature [26] proposed a variational adaptive Kalman filter,(R-VBKF) but only estimated the measurement noise covariance matrix; the accuracy is not satisfactory enough.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Fading Factor and Update Monitoring Strategy Adaptive Kalman Filter Based Variational Bayesian with Inaccurate Time-Varying Process and Measure-Ment Noise Covariance Matrices

Shan¹,

Zhou²,

Yang³

et al. 2020

Preprint

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Aiming at the problem that the performance of Adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement noise matrix are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of Multi-fading factor and update monitoring strategy adaptive Kalman filter based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model, the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the update monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.

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Section: Variational Update Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Fading Factor and Update Monitoring Strategy Adaptive Kalman Filter Based Variational Bayesian with Inaccurate Time-Varying Process and Measure-Ment Noise Covariance Matrices

Shan¹,

Zhou²,

Yang³

et al. 2020

Preprint

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show abstract

“…Laet et al gave the solution to the tracking without known association by using the variational bayesian approach [20]. Furthermore, Lan et al extended the variational bayesian approach to the multitarget and multipath case and proposed the JDT-VB algorithm [21]. One drawback of the variational bayesian based algorithms is that they suffer from computational complexity when there is not proper initialization, such as single element subsets initialization, which may cause performance degradation.…”

Section: Introductionmentioning

confidence: 99%

Joint Multitarget Detection and Tracking in Multipath Environment Using Expectation Maximization Algorithm

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Joint detection and tracking is fundamentally important in signal processing, navigation, and radar applications. Especially, multitarget detection and tracking in multipath environments is a promising issue and broadening range for joint detection and tracking. However, its wide adoption in realworld systems is challenged by the unknown data association, complex and changeable motion environment, and data mutual coupling. To address those problems, we propose a joint detection and tracking scheme based on expectation maximization (EM) algorithm. By alternately computing the complete likelihood function and optimizing it with respect to target existence state (detection) and target kinematic state (tracking), the proposed scheme iteratively optimizes estimation and data association. Furthermore, we provide a hybrid forward and backward algorithm during the M-step to deal with the coupling of the target existence state and target kinematic state. We also provide a convergence speed-up algorithm based on K-means method and stochastic initialization method to accelerate convergence speed. Finally, we verify the effectiveness of the proposed joint detection and tracking scheme by conducting extensive simulations with different tracking scenarios. The simulation results show that the proposed algorithm not only improves the detection and tracking performance but also stays stable performance in complex and changeable motion environment and high noise background.

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“…For the first time, Turner et al [19] proposed a complete variational tracker that integrates the target detection, target tracking and data association in a unified Bayesian framework, and the intractable Bayesian inference is approximated by MF. Inspired by the work of [19], Lan et al [20] proposed an MF-based multipath multitarget tracking algorithm that integrates multipath measurements to improve the performance of both detection and tracking. Williams and Lau [21] addressed the data association problem based on BP.…”

Section: Introductionmentioning

confidence: 99%

“…The extension of [23] to time-varying parameters, such as detection probability and multiple dynamic models, was proposed in [24]. However, the existing MPbased multitarget tracking algorithms are either from the view of variational optimization (MF approximation) [19], [20] or from the view of BP methods [21], [22], [23], [24]. None of them is based on a unified MP method that integrates both MF and BP.…”

Section: Introductionmentioning

confidence: 99%

Measurement-Level Fusion for OTHR Network Using Message Passing

Lan¹,

Wang²,

Bai³

et al. 2020

Preprint

Self Cite

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Tracking an unknown number of targets based on multipath measurements provided by an overthe-horizon radar (OTHR) network with a statistical ionospheric model is complicated, which requires solving four subproblems: target detection, target tracking, multipath data association and ionospheric height identification. A joint solution is desired since the four subproblems are highly correlated, but suffering from the intractable inference problem of high-dimensional latent variables. In this paper, a unified message passing approach, combining belief propagation (BP) and mean-field (MF) approximation, is developed for simplifying the intractable inference. Based upon the factor graph corresponding to a factorization of the joint probability distribution function (PDF) of the latent variables and a choice for a separation of this factorization into BP region and MF region, the posterior PDFs of continuous latent variables including target kinematic state, target visibility state, and ionospheric height, are approximated by MF due to its simple MP update rules for conjugate-exponential models. With regard to discrete multipath data association which contains one-to-one frame (hard) constraints, its PDF is approximated by loopy BP. Finally, the approximated posterior PDFs are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue among target detection, target tracking, multipath data association, and ionospheric height identification. Meanwhile, the proposed approach has the measurement-level fusion architecture due to the direct processing of the raw multipath measurements from an OTHR network, which is benefit to improving target tracking performance. Its performance is demonstrated on a simulated OTHR network multitarget tracking scenario.

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Joint Target Detection and Tracking in Multipath Environment: A Variational Bayesian Approach

Cited by 25 publications

References 43 publications

Multi-Fading Factor and Update Monitoring Strategy Adaptive Kalman Filter Based Variational Bayesian with Inaccurate Time-Varying Process and Measure-Ment Noise Covariance Matrices

Multi-Fading Factor and Update Monitoring Strategy Adaptive Kalman Filter Based Variational Bayesian with Inaccurate Time-Varying Process and Measure-Ment Noise Covariance Matrices

Joint Multitarget Detection and Tracking in Multipath Environment Using Expectation Maximization Algorithm

Measurement-Level Fusion for OTHR Network Using Message Passing

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