A measurement-driven adaptive probability hypothesis density filter for multitarget tracking

Si, Weijian; Wang, Liwei; Qu, Zhiyu

doi:10.1016/j.cja.2015.10.004

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…[1] i denotes the i-th row or column of the generator [1]. Through equation (9), which is proved in APPENDIX, the approximation of posterior mean and error covariance can be addressed.…”

Section: Square Root Cubature Joint Probabiliistic Data Association a The Ckf Frameworkmentioning

confidence: 99%

“…But we can establish a transformation of the standard Gaussian weighted integrals to calculate the nonstandard Gaussian weighted integrals. Consider the left side of equation (9). Since is a positive definite matrix, then can be factorized to be = where n k+1 is the number of joint events, θ i (k + 1) is the i-th event, ωi jt (θ i (k + 1)) is the following binary element:…”

Section: A Derivation Of Equation (9)mentioning

confidence: 99%

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Adaptive Non-Linear Joint Probabilistic Data Association for Vehicle Target Tracking

Zhao

Wang

et al. 2021

IEEE Access

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This paper investigated the problem of multi-target tracking (MTT) over a vehicle sensor network. A novel adaptive square root cubature joint probabilistic data association (ASRCJPDA) was proposed. Motivated by enhancing the stability of joint probabilistic data association (JPDA) in practical application, the proposed methodology implemented a numerically stabled cubature Kalman filter for JPDA state estimate process. It improved numerical stability and acquired more accurate estimated results. Additionally, enlightened by enhancing the real time efficiency of JPDA, an adaptive tracking gate designed for the JPDA measurement associate process was proposed. It combined with the kinematics of vehicle to reduce the computational complexity of data association, which improved the robustness of MTT in complex scenarios. The virtual vehicle target tracking scenarios were built in PreScan software in order to better simulate the real traffic condition. Simulations of target tracking examples are presented to show great effectiveness and superiority of the proposed method.INDEX TERMS Multi-target tracking, square root cubature Kalman filter, adaptive tracking gate, joint probabilistic data association.

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“…[1] i denotes the i-th row or column of the generator [1]. Through equation (9), which is proved in APPENDIX, the approximation of posterior mean and error covariance can be addressed.…”

Section: Square Root Cubature Joint Probabiliistic Data Association a The Ckf Frameworkmentioning

confidence: 99%

Section: A Derivation Of Equation (9)mentioning

confidence: 99%

Adaptive Non-Linear Joint Probabilistic Data Association for Vehicle Target Tracking

Zhao

Wang

et al. 2021

IEEE Access

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“…The centralized state estimation method for multiple-target tracking with multiple sensors can integrate detecting information from different sensors according to certain rules, which will make the tracking results more accurate than that of a single sensor [ 1 , 2 , 3 ]. Multi-sensor fusion is an important data processing method in the field of target tracking, especially under nonlinear conditions, and the fused result with multiple sensors is often better for multiple-target tracking, which has been widely concerned by scholars both at home and abroad [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Although approximation methods have been a good choice for this problem, the computational cost is too great, and improper approximation will lead to degradation of the tracking accuracy, thus making it a long way from being extensively utilized in practical applications [ 4 ]. The latter method has been widely applied in various tracking systems, and is still the most common method for centralized tracking [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association

Liu

et al. 2017

Sensors

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This paper focuses on the tracking problem of multiple targets with multiple sensors in a nonlinear cluttered environment. To avoid Jacobian matrix computation and scaling parameter adjustment, improve numerical stability, and acquire more accurate estimated results for centralized nonlinear tracking, a novel centralized multi-sensor square root cubature joint probabilistic data association algorithm (CMSCJPDA) is proposed. Firstly, the multi-sensor tracking problem is decomposed into several single-sensor multi-target tracking problems, which are sequentially processed during the estimation. Then, in each sensor, the assignment of its measurements to target tracks is accomplished on the basis of joint probabilistic data association (JPDA), and a weighted probability fusion method with square root version of a cubature Kalman filter (SRCKF) is utilized to estimate the targets’ state. With the measurements in all sensors processed CMSCJPDA is derived and the global estimated state is achieved. Experimental results show that CMSCJPDA is superior to the state-of-the-art algorithms in the aspects of tracking accuracy, numerical stability, and computational cost, which provides a new idea to solve multi-sensor tracking problems.

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Multi-EAP: Extended EAP for multi-estimate extraction for SMC-PHD filter

Corchado

Sun

et al. 2017

Chinese Journal of Aeronautics

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A measurement-driven adaptive probability hypothesis density filter for multitarget tracking

Cited by 10 publications

References 22 publications

Adaptive Non-Linear Joint Probabilistic Data Association for Vehicle Target Tracking

Adaptive Non-Linear Joint Probabilistic Data Association for Vehicle Target Tracking

Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association

Multi-EAP: Extended EAP for multi-estimate extraction for SMC-PHD filter

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