A CNN-LSTM Network for Augmenting Target Detection in Real Maritime Wide Area Surveillance Radar Data

Typical radar detectors exploit only a small proportion of the valuable information contained in radar reflections, i.e. magnitude and Doppler. A neural network-based approach for augmenting traditional radar detector structures using machine learning (ML) is proposed in this paper. Specifically, the network is designed to augment target detection in the field of maritime wide area surveillance for non-coherent data. A combination network consisting of a convolutional neural network (CNN) to extract spatial features and a long short-term memory (LSTM) for extracting temporal patterns in the spatial features is proposed. The network augments the detector structure by blanking out regions of the frame which are classified as not containing a target, thus reducing false alarms. The network is tested on data containing four marine targets collected by a ground-based radar. The data set was chosen because it contains strong sea clutter returns. When ML is used, the receiver operating characteristic (ROC) curves are shifted to lower probability of false alarm (PFA). A Kalman filter tracker was applied to the ML-augmented and baseline detections, and it was shown that ML-augmented detections produced similar tracks at lower PFA. The feature discovering capability of the network is analyzed through a series of tests, and the argument is made that the CNN-LSTM network presented in this work demonstrates the ability to improve the detection performance by exploiting spatial and temporal information in the data.