Detection of low-velocity and floating small targets in sea clutter via income-reference particle filters

In this paper, an adaptive composite generalized likelihood ratio test (GLRT) detector using the forward-backward income-reference particle filter (FB-IRPF) for signal estimation is proposed to detect low-velocity and floating small targets in high-resolution sea clutter. In integration duration of the order of seconds, target returns with nonlinear Doppler modulation and amplitude slow fluctuation are parameterized by a piecewise linear frequency modulated (PW-LFM) model equipped by a two-dimensional dynamic system of unknown statistics. Correspondingly, temporal nonstationary sea clutter time series is modelled into a piecewise spherical invariant random vector (PW-SIRV) sequences. By introducing the optimal test statistic at each piece as the user-defined cost and embedding it into the route of the forward-backward cost-reference particle filter (FB-CRPF), the FB-IRPF is developed to estimate the state sequence of a PW-LFM signal and to integrate its income at all the pieces. Using the integrated income as the test statistic, the adaptive composite GLRT is derived. It is compared with the fractal-based detector and tri-feature-based detector in a recognized sea clutter dataset for small target detection. The results show that the proposed detector attains better overall detection performance and is complementary with the tri-feature-based detector to some extent.