Multi-sensor target data detection method based on improved deep learning

Abstract

Aiming at the problems of low detection accuracy and long detection time in the traditional multi-sensor target data detection method, a multi-sensor target data detection method based on improved deep learning is proposed. A regional nomination network model is built to locate all rectangular areas where multi-sensor targets may exist. A sample acquisition model is established by fusing multi-sensor target data in this area in combination with the storage structure of multi-sensor target data to collect multi-sensor target data and filter the collected target data. The histogram feature method is used to extract the processed multi-sensor target data features and the extracted multi-sensor target data features are input into the neural network training samples. The square error function is used to calculate the sample deviation and the deviation is corrected by the deviation gradient sinking method. Finally, the multi-sensor target data detection results are output. Simulation results show that the proposed method has high accuracy, low false detection rate and short detection time along with applicability.