Article
Multitarget Tracking Algorithm Using Multiple
GMPHD Filter Data Fusion for Sonar Networks
Xueli Sheng
1,2,3
, Yang Chen
1,2,3
, Longxiang Guo
1,2,3,
*, Jingwei Yin
1,2,3
and Xiao Han
1,2,3
1
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China;
shengxueli@hrbeu.edu.cn (X.S.); cy5311@hrbeu.edu.cn (Y.C.); yinjingwei@hrbeu.edu.cn (J.Y.);
hanxiao1322@hrbeu.edu.cn (X.H.)
2
Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University),
Ministry of Industry and Information Technology, Harbin 150001, China
3
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
* Correspondence: heu503@hrbeu.edu.cn; Tel.: +86-137-9667-1095
Received: 8 August 2018; Accepted: 19 September 2018; Published: 21 September 2018
Abstract:
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.
Keywords: multisensor data fusion; multitarget tracking; GMPHD; sonar network; RFS
1. Introduction
The issue of multiple target tracking (MTT) has emerged as an area of interest in radar, sonar, etc.
Traditionally, there are many classical MTT algorithms based on explicit data association information,
such as probability data association (PDA) [
1
,
2
], joint probability data association (JPDA) [
3
–
5
], multiple
hypothesis tracking (MHT) [
6
] and derivative algorithms [
7
,
8
]. As the key of these MTT algorithms is
data association, the data association algorithm usually causes computational bottlenecks when the
number of targets is too large. Therefore, these algorithms usually perform poorly when the number
of targets is large.
In response, the random finite set (RFS) [
9
,
10
] has attracted the attention of scholars engaged in
MTT algorithm research. As no explicit data association is required, MTT algorithms based on RFS have
a computational advantage [
11
,
12
]. In the last 15 years, the probability hypothesis density (PHD) [
10
],
cardinalized PHD (CPHD) filter [
13
], sequential Monte Carlo PHD (SMCPHD) [
14
], Gaussian Mixture
PHD (GMPHD) [
15
] and multi-Bernoulli filters [
16
] have been proposed for MTT. In 2013, the notion
of labeled RFS [
17
] was introduced to address target trajectories and their uniqueness. Thus, by
Sensors 2018, 18, 3193; doi:10.3390/s18103193 www.mdpi.com/journal/sensors
