A supervised learning detection method with pre-processing of sparsity-based adaptive line enhancer

For the weak line-spectrum target detection of unmanned underwater vehicles in the complex environment,a supervised learning detection method with pre-processing of sparsity-based Adaptive Line Enhancer(ALE) is proposed.This method incorporates a l_p-norm sparse penalty into the cost function of ALE,and it also promotes the sparse regularization model to the 0<p<1's one.After the processing of SALE,the entropy features of target beam spectrum become obviously different.Using the small sample learning ability of Support Vector Machine(SVM),the method classifies the entropy characteristic curve of beam spectrum and determines if the target exists.The simulation result shows that with the-20 dB input SNR,the SNR gain of l_1/2-norm SALE is 11.5 dB higher than that of conventional ALE.The effectiveness of the method is verified by using the Unmanned Underwater Vehicle(UUV) experimental data.Under the influence of wideband strong interferences,the false alarm rate is 3.5% and the detection rate is 95.8%,which improved the detection probability of weak line-spectrum targets.