Research On Waveform Optimization For Cognitive Radar

Cognitive radar updates and optimizes the parameters of radar by establishing a closed-loop feedback system.Waveform optimization technology is an effective way to improve the performance of cognitive radar.The research of cognitive radar waveform optimization includes waveform design method and optimal waveform solving method.By optimizing the design and efficient algorithm to obtain the optimal transmit waveform,the target detection and tracking performance of the radar can be improved.In the target detection and estimation task of cognitive radar,the target impulse response model plays a crucial role in the process of radar transmitted waveform optimization design.This paper proposes a nature-inspired waveform optimization(NIWO)framework for cognitive radar detection and estimation of range-spread targets.The NIWO framework first uses the maximum a posteriori probability(MAP)and kalman filter(KF)joint estimation methods to estimate the target impulse response.Secondly,a waveform optimization problem model is built based on the estimation results.Finally,in order to solve this non-convex optimization problem more accurately and effectively,the nature-inspired algorithm is applied to the problem,and the optimal waveform is obtained through iterative search.Based on the NIWO framework,this paper first proposes a waveform design method based on modified particle swarm optimization.The search accuracy of the standard particle swarm optimization algorithm is limited by the set inertia weight.Therefore,in the cognitive radar waveform optimization,the idea of dynamic weight is introduced to obtain the optimal waveform accurately,and the local search and global search of the algorithm are adaptively adjusted according to the requirements of the search space for different iteration periods.The simulation results show that the performance of the waveform design method based on the modified particle swarm optimization algorithm is significantly better than the commonly used semi-definite relaxation method when estimating the distance expansion target.Therefore,this method can be used as an effective cognitive radar waveform optimization tool.Secondly,in order to further study the performance of nature-inspired algorithm in solving cognitive radar waveform optimization problems,this paper proposes a waveform design method based on bat algorithm.In this method,bats represent all possible waveform solutions.Each bat can adjust different pulse frequencies,pulse wavelengths,pulse loudness and pulse emissivity to control the dynamic behavior of the bat population,so as to more accurately search the feasible domain.Simulation experiments show that compared with the semi-positive relaxation method and the waveform design method based on the improved particle swarm optimization algorithm,the method can converge to the optimal solution more quickly,and the performance of the target estimation is better.