基于连续蚁群优化算法的小快拍加权子空间拟合快速算法

Continuous Ant Colony Optimization Based Weighted Subspace Fitting Fast Algorithm for DOA Estimation with Few Snapshots

该文针对加权子空间拟合(WSF)算法多维非线性优化计算量大,难以工程应用的问题,将连续空间蚁群算法与加权子空间拟合算法相结合,提出了基于蚁群算法的加权子空间拟合(Ant Colony Optimization based Weighted Subspace Fitting,ACO-WSF)方位估计新方法。该方法利用连续蚁群算法中的信息量高斯核概率分布函数,经过有限次迭代得到加权子空间拟合算法的非线性全局最优解。仿真结果表明,低信噪比、小快拍条件下该方法估计性能与WSF方法基本相同,优于MUSIC方法,而且显著减少了计算量。

Weighted Subspace Fitting（WSF） algorithm is a well-known excellent algorithm for DOA estimation with low SNR and few snapshots.However,this algorithm is totally impractical for its prohibitive computational burden incurred by multi-dimensional nonlinear search.In order to solve this problem,Ant Colony Optimization（ACO） is introduced to combine with the WSF algorithm and a new algorithm with lower computational burden called ACO-WSF is proposed.The proposed algorithm exploits Gaussian kernel probability density function in the sampling process.The global maximum of WSF spatial spectrum function can be reached after reasonable iterations.Simulation results illustrate that the proposed algorithm not only provides similar performance as WSF algorithm and better performance than MUSIC algorithm in the situation of low SNR and few snapshots,but also reduces computational complexity significantly.