Weak Target Detection Based on Optimized Long Short-term Memory Network in Sea Clutter Background
Aiming at the problem that traditional methods are difficult to detect weak target signals under strong chaotic background noise,this paper studied the chaotic phase space reconstruction theory and sparrow optimiza-tion algorithm,and proposed a weak signal detection method in chaotic background based on optimized long short-term memory network(LSTM).The sparrow search algorithm was used to optimize the parameters of the LSTM model,improved the prediction accuracy of the model,reduced the target detection threshold,combined the LSTM model for single-step prediction,and used the prediction error to detect weak target signals from the background of strong sea clutter.Using the Lorenz chaotic system as the chaotic background for simulation ex-periments,the superimposed small signals were detected,and the results showed that the proposed method could effectively detect weak signals.The predicted RMSE error of 0.00171(SNR=-137.707 dB)was signifi-cantly improved compared with the RMSE predicted by the LSTM model,and compared with the RMSE predic-ted by traditional neural networks.The prediction experiment using IPIX radar signal further verified the effec-tiveness of the proposed method.
weak signal detectionlong-term short-term memory networksparrow optimization algorithmsea clutter
万方数据
维普
