School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2022 Jul 19;22(14):5389. doi: 10.3390/s22145389.
Although there have been numerous studies on maneuvering target tracking, few studies have focused on the distinction between unknown maneuvers and inaccurate measurements, leading to low accuracy, poor robustness, or even divergence. To this end, a noise-adaption extended Kalman filter is proposed to track maneuvering targets with multiple synchronous sensors. This filter avoids the simultaneous adjustment of the process model and measurement model without distinction. Instead, the maneuver detection based on the Dempster-Shafer evidence theory is constructed to achieve the reliable distinction between unknown maneuvers and inaccurate measurements by fusing multi-sensor information, which effectively improves the robustness of the filter. Moreover, the adaptive estimation of the process noise covariance is modeled by a Markovian decision process with a proper reward function. Deep deterministic policy gradient is designed to obtain the optimal process noise covariance by taking the innovation as the state and the compensation factor as the action. Furthermore, the recursive estimation of the measurement noise covariance is applied to modify a priori measurement noise covariance of the corresponding sensor. Finally, the fusion algorithm is developed for the global estimation. Simulation experiments are carried out in two scenarios, and simulation results illustrate the feasibility and superiority of the proposed algorithm.
虽然已经有许多关于机动目标跟踪的研究，但很少有研究关注未知机动和不准确测量之间的区别，这导致了精度低、鲁棒性差，甚至发散。为此，提出了一种噪声自适应扩展卡尔曼滤波器，用于使用多个同步传感器跟踪机动目标。该滤波器避免了同时调整过程模型和测量模型而不加区分。相反，基于 Dempster-Shafer 证据理论构建机动检测，通过融合多传感器信息实现未知机动和不准确测量的可靠区分，有效提高了滤波器的鲁棒性。此外，通过具有适当奖励函数的马尔可夫决策过程对过程噪声协方差进行自适应估计。设计深度确定性策略梯度算法，将新息作为状态，补偿因子作为动作，以获得最优的过程噪声协方差。进一步，应用递推估计方法对相应传感器的先验测量噪声协方差进行修正。最后，开发融合算法进行全局估计。在两种场景下进行了仿真实验，仿真结果验证了所提出算法的可行性和优越性。
