Track-Before-Detect Algorithm Based on Optimization Particle Filter
An improved track-before-detect (TBD) algorithm based on the Cubature particle filter is an efficient approach for weak target detection and track under low signal to noise radio environment. Under the framework of particle filter, the algorithm which com
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Track-Before-Detect Algorithm Based on Optimization Particle Filter Huajian Wang
Abstract An improved track-before-detect (TBD) algorithm based on the Cubature particle filter is an efficient approach for weak target detection and track under low signal to noise radio environment. Under the framework of particle filter, the algorithm which combines the particle filter with cubature kalman filter (CKF) algorithm is presented to generate the important density function of particle filter. The simulation results demonstrate that the improved algorithm can provide stable and reliable detection as well as accurate tracking. Keywords Weak target
Track-before-detect Cubature particle filter
75.1 Introduction The Track-Before-Detect (TBD) [1] is good for weak target detecting and tracking in low signal to noise radio (SNR) environment. The method uses the original measurement data of the sensor directly to promote SNR by accumulating target information for a period of time. At the same time the target is being joint detected and estimated, the detected results and target track are announcing. In recent years, beasuce Particle filter (PF) [2] is not the limit of the nonlinear non-Gaussian problem. The Track-Before-Detect algorithm based on PF has been a lot of attention [3–8]. This is a crucial problem, which affect real-time and tracking accuracy of PF-TBD. In the Paper [5, 6], these described that the
H. Wang (&) Department of Information Engineering, Engineering University of China Armed Police Force, No. 710086 WuJing Road, Xi’An Shaanxi, China e-mail: [email protected]
W. Lu et al. (eds.), Proceedings of the 2012 International Conference on Information Technology and Software Engineering, Lecture Notes in Electrical Engineering 210, DOI: 10.1007/978-3-642-34528-9_75, Ó Springer-Verlag Berlin Heidelberg 2013
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algorithm, which Rao-Blackwellised particle filter-based track-before-detect algorithm for Over-the-horizon radar target. But The algorithm requires a statespace model for condition of linear Gauss model. In the Paper [7], it could avoid the sample impoverishment problem, but convergence problem can not be well guaranteed and operation time is long time. In the Paper [8], it described that the TBD algorithm based on gauss particle filtering avoided resampling and achieved relatively easy. But It required the posterior probability density for gauss distribution. So it’s application was restricted. In order to improve the accuracy and the algorithm running time relationship, this article proposed TBD method based on cubature particle filter. In the tracking phase, the algorithm uses CKF (cubature kalman filter) to construct of the particle filter importance proposal function. The simulation results demonstrate that the algorithm can be obtained under higher filtering accuracy in a small number of particles, meet the requirements of real-time control, and strong robustness.
75.2 Problem Description 75.2.1 State Model Assuming that the target is in uniform motion in X–Y plane. System state
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