A Fast Algorithm of Generalized Radon-Fourier Transform for Weak Maneuvering Target Detection

Abstract EN

The generalized Radon-Fourier transform (GRFT) has been proposed to detect radar weak maneuvering targets by realizing coherent integration via jointly searching in motion parameter space.

Two main drawbacks of GRFT are the heavy computational burden and the blind speed side lobes (BSSL) which will cause serious false alarms.

The BSSL learning-based particle swarm optimization (BPSO) has been proposed before to reduce the computational burden of GRFT and solve the BSSL problem simultaneously.

However, the BPSO suffers from an apparent loss in detection performance compared with GRFT.

In this paper, a fast implementation algorithm of GRFT using the BSSL learning-based modified wind-driven optimization (BMWDO) is proposed.

In the BMWDO, the BSSL learning procedure is also used to deal with the BSSL phenomenon.

Besides, the MWDO adjusts the coefficients in WDO with Levy distribution and uniform distribution, and it outperforms PSO in a noisy environment.

Compared with BPSO, the proposed method can achieve better detection performance with a similar computational cost.

Several numerical experiments are also provided to demonstrate the effectiveness of the proposed method.