Efficient Radar Modulation Recognition via a Noise-Aware Ensemble Neural Network

Electronic warfare support (ES) systems intercept adversary radar signals and estimate various types of signal information, including modulation schemes. The accurate and rapid identification of modulation schemes under conditions of very low signal power remains a significant challenge for ES systems. This paper proposes a recognition model based on a noise-aware ensemble learning (NAEL) framework to efficiently recognize radar modulation schemes in noisy environments. The NAEL framework evaluates the influence of noise on recognition and adaptively selects an appropriate neural network structure, offering significant advantages in terms of computational efficiency and recognition performance. Furthermore, we employ feature extraction blocks to enhance the efficiency of the proposed recognition model. We present the analysis results of the recognition performance of the proposed model based on experimental data. Our recognition model demonstrates superior recognition accuracy with low computational complexity compared to conventional classification models.

Resolution-Adaptive Micro-Doppler Spectrogram for Human Activity Recognition

The rising demand for remote-sensing systems for detecting hazardous situations has led to increased interest in radar-based human activity recognition (HAR). Conventional radar-based HAR methods predominantly rely on micro-Doppler spectrograms for recognition tasks. However, spectrograms frequently fail to effectively capture micro-Doppler signatures because of their limited linear resolution. To address this limitation, we propose a time--frequency domain representation method that adaptively adjusts the resolution based on activity characteristics. This approach nonlinearly transforms the resolution to focus on the most relevant frequency range for micro-Doppler signatures. We validate the proposed method by training deep-learning-based HAR models on datasets generated using the adaptive representation method. Experimental results demonstrate that the models trained using the proposed method achieve superior recognition accuracy compared with those trained using conventional methods.

Deep Learning Detection Networks in MIMO Decode-Forward Relay Channels

In this paper, we consider signal detection algorithms in a multiple-input multiple-output (MIMO) decode-forward (DF) relay channel with one source, one relay, and one destination. The existing suboptimal near maximum likelihood (NML) detector and the NML with two-level pair-wise error probability (NMLw2PEP) detector achieve excellent performance with instantaneous channel state information (CSI) of the source-relay (SR) link and with statistical CSI of the SR link, respectively. However, the NML detectors require an exponentially increasing complexity as the number of transmit antennas increases. Using deep learning algorithms, NML-based detection networks (NMLDNs) are proposed with and without the CSI of the SR link at the destination. The NMLDNs detect signals in changing channels after a single training using a large number of randomly distributed channels. The detection networks require much lower detection complexity than the exhaustive search NML detectors while exhibiting good performance. To evaluate the performance, we introduce semidefinite relaxation detectors with polynomial complexity based on the NML detectors. Additionally, new linear detectors based on the zero gradient of the NML metrics are proposed. Applying various detection algorithms at the relay (DetR) and detection algorithms at the destination (DetD), we present some DetR-DetD methods in MIMO DF relay channels. An appropriate DetR-DetD method can be employed according to the required error probability and detection complexity. The complexity analysis and simulation results validate the arguments of this paper.