Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China.
Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China.
Sensors (Basel). 2018 Sep 21;18(10):3193. doi: 10.3390/s18103193.
Multitarget tracking algorithms based on sonar usually run into detection uncertainty, complex channel and more clutters, which cause lower detection probability, single sonar sensors failing to measure when the target is in an acoustic shadow zone, and computational bottlenecks. This paper proposes a novel tracking algorithm based on multisensor data fusion to solve the above problems. Firstly, under more clutters and lower detection probability condition, a Gaussian Mixture Probability Hypothesis Density (GMPHD) filter with computational advantages was used to get local estimations. Secondly, this paper provided a maximum-detection capability multitarget track fusion algorithm to deal with the problems caused by low detection probability and the target being in acoustic shadow zones. Lastly, a novel feedback algorithm was proposed to improve the GMPHD filter tracking performance, which fed the global estimations as a random finite set (RFS). In the end, the statistical characteristics of OSPA were used as evaluation criteria in Monte Carlo simulations, which showed this algorithm's performance against those sonar tracking problems. When the detection probability is 0.7, compared with the GMPHD filter, the OSPA mean of two sensor and three sensor fusion was decrease almost by 40% and 55%, respectively. Moreover, this algorithm successfully tracks targets in acoustic shadow zones.
基于声纳的多目标跟踪算法通常会遇到检测不确定性、复杂的通道和更多的杂波，这会导致检测概率降低、当目标处于声影区域时单个声纳传感器无法进行测量以及计算瓶颈等问题。本文提出了一种基于多传感器数据融合的新型跟踪算法来解决上述问题。首先，在存在更多杂波和较低检测概率的情况下，使用具有计算优势的高斯混合概率假设密度（GMPHD）滤波器来获取局部估计值。其次，本文提出了一种最大检测能力多目标跟踪融合算法，以解决低检测概率和目标处于声影区域所带来的问题。最后，提出了一种新的反馈算法来提高 GMPHD 滤波器的跟踪性能，该算法将全局估计值作为随机有限集（RFS）进行反馈。最后，使用蒙特卡罗模拟中的 OSPA 统计特性作为评估标准，对该算法的性能进行了评估，以应对这些声纳跟踪问题。当检测概率为 0.7 时，与 GMPHD 滤波器相比，两个传感器和三个传感器融合的 OSPA 平均值分别降低了近 40%和 55%。此外，该算法成功地对处于声影区域的目标进行了跟踪。
