False-Alarm-Controllable Radar Detection for Marine Target Based on Multi Features Fusion via CNNs

Abstract

Due to the influence of the complex marine environment, the marine target detection based on statistical theory is difficult to achieve high-performance. Moreover, due to various targets' motion characteristics, only using a single feature for detection is unreliable. In this paper, from the perspective of feature extraction and classification, marine target and sea clutter are classified by deep learning methods. To achieve the required false alarm rate, the dual-channel convolutional neural networks (DCCNN) and false-alarm-controllable classifier (FACC)-based marine target detection method is proposed. Firstly, the measured sea clutter and the target signal are preprocessed to obtain the time-Doppler spectrum and amplitude information. The Marine-DCCNN (MDCCNN) is then constructed for features extraction and fusion, and the feature vectors of the signals are obtained. The performance of different feature extraction models is tested and compared. Finally, the FACC is used as a detector to classify the feature vectors into two categories and the control of the false alarm rate is realized. The detection performances were verified by two popular public radar datasets, i.e., IPIX radar dataset (floating target) and CSIR dataset (maneuvering marine target). The results show that compared with single-channel CNN and histogram of oriented gradient support vector machine (Hog-SVM) classification, a combination of MDCCNN feature extraction model and softmax classifier can achieve higher performance and controllable false alarm rate. Moreover, HH polarization and mixed training datasets under different sea states can help improve detection performance. Index Terms-Radar target detection; marine target; feature extraction; dual-channel convolutional neural network (DCCNN); false alarm controllable classifier (FACC)

Deep-Learning for Radar: A Survey

“…Specifically, in [161], Pan et al used the Faster R-CNN proposed by Ren et al in [104] for target detection using the sea clutter dataset collected with the X-band ground-based Fynmeet marine radar by the council for scientific and industrial research (CSIR). In [162], Chen et al proposed a dual-channel convolutional neural network (DCCNN) made of LeNet and VGG16, for which the amplitude and the timefrequency information were used as two inputs, and the features extracted from the two channels were fused at the FC layer. One distinctive characteristic of [162] is that softmax classifier with variable threshold and SVM classifier with controllable false alarm rates were designed.…”

“…In [162], Chen et al proposed a dual-channel convolutional neural network (DCCNN) made of LeNet and VGG16, for which the amplitude and the timefrequency information were used as two inputs, and the features extracted from the two channels were fused at the FC layer. One distinctive characteristic of [162] is that softmax classifier with variable threshold and SVM classifier with controllable false alarm rates were designed. The performance of the proposed network was tested with two datasets, the Intelligent PIXel processing radar (IPIX) dataset collected by the fully coherent dual-pol X-band radar for floating target and the CSIR dataset for maneuvering marine target.…”

Micro-Motion Classification of Flying Bird and Rotor Drones via Data Augmentation and Modified Multi-Scale CNN

“…CNNs, as the most classical method of deep learning, have been widely used in image detection and classification [18,19], which has two important properties: local connection and weight sharing. However, it is easy for a CNN to learn some useless feature information, which will lead to over-fitting problems with worse generalization performance [20][21][22]. There is large-scale periodic modulation information in the micro-motion features, as well as small-scale details and small motions.…”

Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

“…In order to deal with the above problems and challenges, radar needs to develop intelligent processing with self-learning, adapting, and self-optimizing capabilities. The development of artificial intelligence technology in recent years has provided technical support for the intelligent design of radar [15,16]. Artificial intelligence technology possesses the ability to simulate the memory, learning, and decision-making process through sample training.…”