δ-广义标签多伯努利滤波算法的非线性扩展

doi:10.12305/j.issn.1001-506X.2021.12.19

Abstract

Abstract:

Aiming at the low tracking performance of the Gaussian mixture(GM)variational Bayesian-δ-generalized labeled multi-Bernoulli (VB-δ-GLMB) filtering algorithm in nonlinear scenes, a maneuvering multi-target tracking algorithm is proposed, which combined with the iterative optimization based on proximal point algorithm (PPA) and (variational Bayesian, VB) approximate and cubature Kalman filter (CKF), to suit for nonlinear models. Based on GM-VB-δ-GLMB, the proposed algorithm uses inverse Gamma (IG) and Gaussian product mixture distribution to approximate the joint posterior density of the measurement noise covariance and state. Then, PPA-CKF-VB (PCKF-VB) method is used to predict and update the Gaussian term parameters in the transfer process. Finally, smoothing of variational Bayesian cubature Rauch-Tung-Striebel (VB-CRTS) is utilized to improve the filtering accuracy. Simulation results indicate that the target tracking accuracy of the proposed algorithm is obviously improved compared with the existing VB-δ-GLMB algorithm for the nonlinear system with unknown measurement noise.

Key words: δ-generalized labeled multi-Bernoulli (δ-GLMB) algorithm, nonlinear model, cubature Kalman filter (CKF), proximal point algorithm, variational Bayesian approximation

CLC Number:

TN953

Meibin QI, Jingjing HU, Peilin CHENG, Xueming JIN. Nonlinear extension of δ-generalized labeled multi-Bernoulli filtering algorithm[J]. Systems Engineering and Electronics, 2021, 43(12): 3571-3578.

Figures/Tables 6

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References 30

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目标	起始时刻/s	结束时刻/s	目标初始状态	转弯频率rad/s
1	1	100	[1 000, －10, 1 500, -10]^T	π/120
2	1	100	[－250, 20, 1 000, 3]^T	π/60
3	10	100	[－1 500, 11, 250, 10]^T	－π/120
4	10	100	[1 000, 5, 1 500, -15]^T	π/60
5	20	80	[250, -8, 750, -15]^T	π/120
6	20	100	[－250, -12, 1 000, -12]^T	π/60
7	30	90	[－1 500, 11, 250, -10]^T	π/80
8	30	80	[250, -25, 750, 0]^T	－π/120
9	40	100	[1 000, -20, 1 500, -10]^T	π/120
10	60	100	[250, -5, 750, 20]^T	π/120

滤波器	OSPA距离	OSPA势估计
VB-δ-GLMB	34.06	8.589
VB-δ-GLMB平滑之后	20.11	7.475
PCKF-VB-δ-GLMB	29.43	7.579
PCKF-VB-δ-GLMB平滑之后	18.03	6.353

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Nonlinear extension of δ-generalized labeled multi-Bernoulli filtering algorithm

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