Weak Multitarget Detection Exploiting CPHD Filter for Ubiquitous Radar

In order to detect and track the moving weak multitarget through ubiquitous radar observations, this paper proposes a cardinalized probability hypothesis density (CPHD) filter based on sequential Monte Carlo (SMC) implementation. The traditional tracking method, such as joint probabilistic data association (JPDA) algorithm, suffers from computational problem in closely-spaced targets and low signal-to-noise ratio (SNR) environment. The CPHD filter is a multitarget moment approximation of the multitarget Bayes filter with both the detecting and tracking capability. The proposed method is designed for the general weak multitarget detecting scenarios with unknown parameters. In particular, the filter is derived by exploiting the tools of the finite set statistics (FISST) to solve the multisensory-multitarget computational problem of CPHD. Instead of processing in data level, the processing in signal level schemes alleviates the information loss, since the measurements of ubiquitous radar can be in in-phase and quadrature (I/Q) form. Simulation results show that the proposed filter yields stable cardinality and state estimates.