Robust Beamforming For Factored Radar STAP

In the optimization of the radar array signal processing system,it is assumed that the target signal and the interference signal do not match the components corresponding to the actual received signal,resulting in the inability to correctly identify the reflected target signal.In the past research on radar array signal processing algorithms,although there are many achievements with excellent performance,in the actual application process,it is often considered that the a priori conditions are too stringent,the algorithm complexity is high,and the calculation time is too long.In order to solve these problems,this paper improves some classic algorithm models,and uses optimal adaptive beamforming(OAB)technology for robust factored radar space-time adaptive processing(Factored-STAP)to maximize the worst case The signal-to-interference-plus-noise ratio(SINR)of the received signal of the lower radar achieves the purpose of optimizing the weight of the array antenna signal,weakening the impact of the signal mismatch problem on the system,and braking the subsequent further processing of the signal.Based on the design research of the robust factored radar space-time adaptive processing technology,this paper proposes two new robust adaptive beamforming algorithms.These algorithms are based on the criterion of signal-to-interference-plusnoise ratio and have low computational complexity and strongly robust.The main contents are as follows:First,this paper converts the signal-to-interference-plus-noise ratio problem of maximizing the worst-case radar received signal into a second-order cone programming problem,and then decomposes the weight vector of the radar space-time adaptive processing technology into the weight of the space and time weight vector.The Kronecker product,by alternately iteratively optimizing the spatial weight vector and the temporal weight vector,quickly and efficiently obtains a suboptimal solution.In addition,the strong duality theorem of linear cone programming is used to transform the worst-case signal-to-interference-noise ratio problem into a bi-quadratic matrix inequality(BQMI)problem.By analyzing and studying the bi-quadratic matrix inequality problem of this model,this paper finds that there is a closed optimal solution to this problem.Specifically,the robust optimal choice for factored space-time adaptive processing is a non-adaptive filter given by the Kronecker product of space and time steering vectors.This is the most important contribution of this paper,indicating that in the presence of steering and covariance mismatch(the model considered in this paper),it is very convenient to avoid adaptability and use only non-adaptive beamformers.In order to prove the performance of the beamforming algorithm based on the robust factored radar space-time adaptive processing technology proposed in this paper,computer simulations are carried out on this algorithm.The performance is even better.