Abstract
A novel approach to detecting and tracking a moving target using synthetic aperture radar (SAR) images is proposed in this paper. Achieved with the particle filter (PF) based track-before-detect (TBD) algorithm, the approach is capable of detecting and tracking the low signal-to-noise ratio (SNR) moving target with SAR systems, which the traditional track-after-detect (TAD) approach is inadequate for. By incorporating the signal model of the SAR moving target into the algorithm, the ambiguity in target azimuth position and radial velocity is resolved while tracking, which leads directly to the true estimation. With the sub-area substituted for the whole area to calculate the likelihood ratio and a pertinent choice of the number of particles, the computational efficiency is improved with little loss in the detection and tracking performance. The feasibility of the approach is validated and the performance is evaluated with Monte Carlo trials. It is demonstrated that the proposed approach is capable to detect and track a moving target with SNR as low as 7 dB, and outperforms the traditional TAD approach when the SNR is below 14 dB.
Highlights
Detection and tracking of a moving target with synthetic aperture radar (SAR) systems is a challenging problem, which has drawn an increasing attention recently
Multiple TBD algorithms have been developed previously, including batch methods, such as the Hough transform, dynamic programming (DP) and maximum likelihood techniques, and recursive methods based on the Bayesian approach, such as the particle filter (PF) [8]
Among all of the algorithms, the DP-based TBD algorithm and PF-based algorithm are widely used for solving problems of radar target detection and tracking under low signal-to-noise ratio (SNR) conditions
Summary
Detection and tracking of a moving target with synthetic aperture radar (SAR) systems is a challenging problem, which has drawn an increasing attention recently. Current approaches to detecting and tracking a moving target with SAR systems are mainly achieved with the track-after-detect (TAD) scheme, based on the acquisition of multi-frame. SAR images [1,2,3,4] The concept of this scheme is to detect the target at a plot level at first, associate the detections and estimate the trajectory by passing through a tracker filter. It is effective when the target has a high signal-to-noise ratio (SNR) [5].
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