  Detecting weak target is an important and challenging problem in many
applications such as radar, sonar etc. However, conventional detection methods
are often ineffective in this case because of low signal-to-noise ratio (SNR).
This paper presents a track-before-detect (TBD) algorithm based on an improved
particle filter, i.e. cost-reference particle filter bank (CRPFB), which turns
the problem of target detection to the problem of two-layer hypothesis testing.
The first layer is implemented by CRPFB for state estimation of possible
target. CRPFB has entirely parallel structure, consisting amounts of
cost-reference particle filters with different hypothesized prior information.
The second layer is to compare a test metric with a given threshold, which is
constructed from the output of the first layer and fits GEV distribution. The
performance of our proposed TBD algorithm and the existed TBD algorithms are
compared according to the experiments on nonlinear frequency modulated (NLFM)
signal detection and tracking. Simulation results show that the proposed TBD
algorithm has better performance than the state-of-the-arts in detection,
tracking, and time efficiency.
