δ-广义标签多伯努利滤波算法的非线性扩展

针对高斯混合(Gaussian mixture, GM)实现的变分贝叶斯-δ-广义标签多伯努利(variational Bayesian-δ-generalized labeled multi-Bernoulli, VB-δ-GLMB)滤波算法在非线性场景下跟踪性能较低这一问题, 结合基于临近点算法(proximal point algorithm, PPA)和变分贝叶斯(variational Bayesian, VB)的迭代优化与容积卡尔曼滤波(cubature Kalman filtering, CKF), 提出一种适用于非线性模型的机动多目标跟踪算法。该算法在GM-VB-δ-GLMB的基础上采用逆伽马(inverse-Gamma, IG)和高斯乘积混合分布近似量测噪声协方差和状态联合后验分布; 利用PPA-CKF-VB(PCKF-VB)方法对传递过程中的高斯项参数进行预测更新; 最后为提高滤波精度进行变分贝叶斯容积RTS(VB cubature Rauch-Tung-Striebel, VB-CRTS)平滑。仿真结果表明, 对于量测噪声未知的非线性系统, 所提的算法与现有的VB-δ-GLMB算法相比目标跟踪精度有显著提高。

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

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.