Article
Multi-Target Joint Detection and Estimation
Error Bound for the Sensor with Clutter and
Missed Detection
Feng Lian
∗
, Guang-Hua Zhang, Zhan-Sheng Duan and Chong-Zhao Han
Ministry of Education Key Laboratory for Intelligent Networks and Network Security (MOE KLINNS),
College of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
MichaelZgh@stu.xjtu.edu.cn (G.-H.Z.); zsduan@xjtu.edu.cn (Z.-S.D.); czhan@xjtu.edu.cn (C.-Z.H.)
* Correspondence: lianfeng1981@xjtu.edu.cn; Tel.: +86-158-2907-9318
Academic Editor: Fabrizio Lamberti
Received: 18 November 2015; Accepted: 21 January 2016; Published: 28 January 2016
Abstract:
The error bound is a typical measure of the limiting performance of all filters for the given
sensor measurement setting. This is of practical importance in guiding the design and management
of sensors to improve target tracking performance. Within the random finite set (RFS) framework, an
error bound for joint detection and estimation (JDE) of multiple targets using a single sensor with
clutter and missed detection is developed by using multi-Bernoulli or Poisson approximation to
multi-target Bayes recursion. Here, JDE refers to jointly estimating the number and states of targets
from a sequence of sensor measurements. In order to obtain the results of this paper, all detectors
and estimators are restricted to maximum a posteriori (MAP) detectors and unbiased estimators,
and the second-order optimal sub-pattern assignment (OSPA) distance is used to measure the error
metric between the true and estimated state sets. The simulation results show that clutter density
and detection probability have significant impact on the error bound, and the effectiveness of the
proposed bound is verified by indicating the performance limitations of the single-sensor probability
hypothesis density (PHD) and cardinalized PHD (CPHD) filters for various clutter densities and
detection probabilities.
Keywords:
performance evaluation; error bound; multi-target tracking; joint detection and
estimation; random finite set
1. Introduction
The problem of joint detection and estimation (JDE) of multiple targets arises from many
applications in surveillance and defense [
1
], where the number of targets is unknown and the sensor
may receive measurements generated randomly from either targets or clutters. There is no information
about which are the measurements of interest or which are the clutters. The aim of multi-target JDE is
to determine the number of targets and to estimate their states if exist using prior information, as well
as a sequence of the sensor measurements. In recent years, multi-target JDE has attracted extensive
attention, and many approaches for it have been proposed [2–10].
Obviously, it is very necessary to find an error (lower) bound to assess the achievable performance
of the multi-target JDE algorithms for the given sensor measurements. It is well known that
Tichavsky et al. [11]
proposed a recursive posterior Cramér-Rao lower bound (CRLB) for evaluating
the performance of nonlinear filters when a target was asserted and observed by a sensor. Then, the
CRLB was extended to the cases in which clutter or missed detection was present in the sensor [
12
–
15
].
Nevertheless, these CRLBs [
12
–
15
] could barely be applied to such a JDE problem, since CRLB only
considers the estimation error of a target state, but not the detection error of the target number (or
Sensors 2016, 16, 169; doi:10.3390/s16020169 www.mdpi.com/journal/sensors