Aerospace and Electronic Systems Magazine December 2017 - 72

Feature Article:

DOI. No. 10.1109/MAES.2017.170033

Adaptive Maneuvering Target Tracking With 2-HFSWR
Multisensor Surveillance System
Ling Zhang, Yi Jiang, Yingshuai Li, Gangsheng Li, Ocean University of China, Qingdao,
China,
Yonggang Ji, First Institute of Oceanography, Qingdao, China

INTRODUCTION
The high frequency surface wave radar (HFSWR) with detection
capability of hundreds of kilometers plays an important role in marine surveillance [1]-[8]. Nevertheless, HFSWR has low azimuth
measurement accuracy, which causes great deviation of target location and tracking [9]. Meanwhile, the miniaturization of radar arrays leads to the limitation of antenna length, which further causes
severe decline of azimuth accuracy. Moreover, the interference
from external environment also decreases the azimuth accuracy,
making the accurate tracking of vessels difficult. In recent years,
the 2-HFSWR multisensor surveillance system has become a hot
spot, because it only requires parameters of distance and velocity without using the azimuth measurement when tracking vessels.
The 2-HFSWR multisensor surveillance system consists of two
independent radars implementing the target location and tracking
task by detecting the radial distance and velocity, respectively. The
2-HFSWR multisensor surveillance system can ensure the detection accuracy without azimuth measurement, which is advantageous to the miniaturization of HFSWR. However, there are still
certain complicated cases to be solved for continuous tracking of
vessels, for example sudden change in motion state, an inaccurate
model, and unknown priori statistical properties of noise.
To solve the above problems certain tracking approaches have
been proposed, such as track-before-detection methods [10]-[13].
However, these algorithms are not quite effective for maneuvering targets. Puranik [14] proposed a suboptimal tracking algorithm
based on the interacting multiple model (IMM), which significantly improves the tracking performance [15]. However, the tracking
precision and robustness of these algorithms severely depends on
Authors' addresses: L. Zhang, Y. Jiang, Y. Li, College of Engineering, Ocean University of China, Qingdao, Shandong
266100, China; G. Li, Department of Education and College
of Marine Geosciences, Ocean University of China, Qingdao 266100, China, E-mail: (ligang0663@163.com); Y. Ji, First
Institute of Oceanography, State Oceanic Administration,
Qingdao, Shandong 266061, China.
Manuscript received January 30, 2017, revised August 3, 2017,
and ready for publication September 22, 2017.
Review handled by D. O'Hagan.
0885/8985/17/$26.00 © 2017 IEEE
70

the accuracy of the prior information. When parameters of the motion model are not accurate enough or the target maneuvers strongly, the nonlinear filtering algorithms including extended Kalman
filter EKF [16], unscented Kalman filter [17], and Cubature Kalman filter [18] are no longer applicable since the motion varies
frequently and cannot be correctly modeled. Yang [19] proposed
a tracking algorithm based on strong tracking filter (STF) to compensate the uncertainties of the system. By introducing the timevarying fading factor, STF can adjust the gain matrix in real time
to guarantee the stability and precision of estimation. However,
due to the variation of the observational conditions, the statistical
properties of the process noise are difficult to obtain in real time.
As a result, the cumulative estimation error may lead to filtering divergence. Adaptive Kalman filter has been widely applied with the
ability of estimating the statistical properties of noises adaptively.
The Saga-Husa adaptive filter [20] is applied to estimate the mean
and variance of noises in order to compensate the filtering errors
and improve the filtering accuracy. Nevertheless, the variance of
noises is not positive definite or positive semidefinite in a high-order system, which leads to the filtering algorithm divergent. Zhang
[21] provided a remedial method based on the Saga-Husa adaptive
filter to improve the stability of the adaptive filter, but for tracking
maneuvering targets the performance should be improved.

Sudden changes in target
motion cause complications
for continuous tracking
of vessels.
In consideration of complicated factors including strong maneuverability and system uncertainty, a novel maneuvering target
tracking algorithm is proposed in which STF and adaptive filter
are combined with IMM approach. The Interacting MultipleModel Asynchronous Track Fusion (IMMASTF) can estimate
the statistical characteristics of noises and adjust filtering gain
in real time to compensate errors caused by those complicated
factors. In addition, in order to simplify the calculation, a criterion to judge whether the filter has been operating improperly

IEEE A&E SYSTEMS MAGAZINE

DECEMBER 2017



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