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摘要:
概率假设密度（PHD）滤波算法已被证明是实时多目标跟踪的有效方法，但现有这些基于PHD滤波的方法假设量测噪声协方差先验已知，而实际中量测噪声协方差可能是未知或随着环境改变而变化。针对这一问题，提出了一种适用于非线性量测模型的自适应噪声协方差多目标跟踪算法。该算法以PHD滤波为基础，采用容积卡尔曼（CK）技术近似非线性量测模型，利用逆威沙特（IW）分布描述量测噪声协方差分布，通过变分贝叶斯（VB）近似技术迭代估计量测噪声协方差和多目标状态联合后验密度。仿真结果表明，本文所提算法可有效估计量测噪声协方差，同时实现准确的目标数和目标状态估计。
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关键词:
- 随机有限集 /
- 多目标跟踪 /
- 未知量测噪声协方差 /
- 变分贝叶斯(VB) /
- 概率假设密度(PHD)滤波
Abstract:Probability hypothesis density (PHD) filter has been demonstrated to be an effective approach for multi-target tracking in real time. However, these methods based on the PHD filter assume that the measurement noise covariance is known as a priori. This is unrealistic for real applications because it may be previously unknown or its value may be time-varying as the environment changes. To solve this problem, an adaptive noise covariance algorithm for multi-target tracking under the nonlinear measurement is proposed. Based on the PHD filter, the proposed algorithm employs the cubature Kalman (CK) technology to approximate the nonlinear model, models the noise covariance distribution as inverse Wishart (IW) distribution, and recursively estimates the joint posterior density of the measurement noise covariance and multi-target states by the variational Bayesian (VB) approach. The simulation results indicate that the proposed algorithm could effectively estimate measurement noise covariance, and achieve the accurate estimation of the target number and corresponding multi-target states.
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图 2 包含杂波目标运动航迹量测结果
Figure 2. Track measurement results of target movementimmersed in clutters
图 5 不同算法OSPA距离比较(c=300,p=2)
Figure 5. Comparison of OSPA distance for different algorithms(c=300,p=2)
图 6 时变量测噪声协方差下目标数和协方差估计结果
Figure 6. Target number and covariance estimation results fortime-varying measurement noise covariance
图 7 时变量测噪声协方差OSPA距离比较(c=300,p=2)
Figure 7. Comparison of OSPA distance for time-varyingmeasurement noise covariance(c=300,p=2)
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