Sidelobe suppression algorithm for chaotic FM signal based on neural network

The chaotic FM signal is used to improve the electronic counter-counter measure (ECCM) capabilities of radar. However, the sidelobe level of this signal after matching processing is very high, thus would greatly debase the radarpsilas performance. Based on the Radial Basis Function (RBF) network, a novel range sidelobe processing technique is proposed, in which the quantum-behaved particle swarm optimization (QPSO) algorithm is applied to realize the optimization computing. A multidimensional vector composed of RBF network parameters is regarded as a particle to evolve. Then, the feasible sampling space is searched for the global optima. The simulation results show that this algorithm has easier computation and more rapid convergence compared with traditional algorithms. This method can also successfully suppress the sidelobe with good numerical stability.