Track-before-Detect Algorithm Based on Gaussian Particle Cardinalized Probability Hypothesis Density

Abstract

In unknown dim target number environment, a new track-before-detect (TBD) algorithm based on cardinalized probability hypothesis density (CPHD) filter is proposed. It can avoid the low tracking robustness and high computational amount. The Gaussian particle filter (GPF) approximated posterior densities as Gaussians, the mean and covariance of each Gaussian components in CPHD can be operated recursively by using particle filter, re-sampling is not required. Meanwhile, according to the actual TBD situation, updated the function for calculating particle weight. Simulation reveals that, compared with the conventional algorithm, the proposed is able to convey the cardinalized information more reliably, has lower computational time-consuming with a better tracking performance in multi-dim targets estimation.