Electronic and Electrical Department, Sungkyunkwan University, Suwon 03063, Korea.
Sensors (Basel). 2022 Jul 24;22(15):5512. doi: 10.3390/s22155512.
This article handles tracking multiple targets using bearing-only measurements in underwater noisy environments. For tracking multiple targets in underwater noisy environments, the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter provides good performance with its low computational load. Bearing-only measurements are passive and do not provide position information of a target. Note that the nonlinearity of the bearing-only measurements can be handled by Extended Kalman Filters (EKF) when applying the GM-PHD filter. However, range uncertainty of the target is large for bearing-only measurements. Thus, a single EKF leads to poor performance when it is applied in the GM-PHD. In this article, every bearing measurement gives birth to multiple target samples, which are distributed considering the feasible range of the passive sensor. Thereafter, every target sample is updated utilizing the measurement update step of the EKF. In this way, we run multiple EKFs associated to multiple target samples, instead of running a single EKF. To the best of our knowledge, our article is novel in tracking multiple targets in noisy environments, using the observer with bearing-only measurements. The effectiveness of the proposed GM-PHD is verified utilizing MATLAB simulations.
本文处理在水下噪声环境中使用仅方位测量值来跟踪多个目标的问题。在水下噪声环境中跟踪多个目标时，由于其计算负载低，高斯混合概率假设密度（GM-PHD）滤波器具有良好的性能。仅方位测量是被动的，不提供目标的位置信息。请注意，在应用 GM-PHD 滤波器时，可以通过扩展卡尔曼滤波器（EKF）处理仅方位测量的非线性。然而，对于仅方位测量，目标的距离不确定性很大。因此，当在 GM-PHD 中应用单个 EKF 时，性能会很差。在本文中，每个方位测量都会产生多个目标样本，这些样本的分布考虑了无源传感器的可行范围。此后，利用 EKF 的测量更新步骤更新每个目标样本。通过这种方式，我们运行与多个目标样本相关联的多个 EKF，而不是运行单个 EKF。据我们所知，我们的文章是在噪声环境中使用仅方位测量的观测器跟踪多个目标的新颖方法。使用 MATLAB 仿真验证了所提出的 GM-PHD 的有效性。
