EVD clutter suppression method based on the self-organizing neural network
The subspace decomposition method is a common method for clutter suppression of slow moving targets in strong clutter environment.But the traditional subspace decomposition method has a poor adaptability.The SVD clutter suppression algorithm based on K-means clustering makes up for the above defects,but when the slow-moving target is close to the clutter Doppler or aliasing,the feature set discrimination decreases and the clustering results are unstable.Therefore,an eigenvalue-decomposition(EVD)clutter suppression algorithm based on self-organizing neural networks is proposed,with the differences between targets,clutter and noise analyzed deeply,and the features with high differentiation between slow-moving targets and clutter extracted to construct the feature set.Then,the self-organizing neural network,which is less affected by the initial value and has stable clustering results,is used for clustering,adaptive selection of clutter basis to construct clutter subspace.Finally,the clutter is suppressed by orthotropic subspace projection.Simulation and measured data are used to verify the performance of the algorithm.By combining with the target tracking algorithm,it is further verified that the algorithm has strong robustness and engineering practicability.
slow-moving targetcluttereigen value decomposition(EVD)self-organizing neural network
万方数据
维普
