Recent advances in radar automatic target recognition (RATR) techniques utilizing deep neural networks have demonstrated remarkable performance, largely due to their robust generalization capabilities. To address the challenge for applications with polarimetric HRRP sequences, a dual-polarization feature fusion network (DPFFN) is proposed along with a novel two-stage feature fusion strategy. Moreover, a specific fusion loss function is developed, which enables the adaptive generation of comprehensive multi-modal representations from polarimetric HRRP sequences. Experimental results demonstrate that the proposed network significantly improves performance in radar target recognition tasks, thus validating its effectiveness. The PyTorch implementation of our proposed DPFFN is available at https://github.com/xmpan/DPFFN.
Responding to the challenge of detecting unusual radar targets in a well identified environment, innovative anomaly and novelty detection methods keep emerging in the literature. This work aims at presenting a benchmark gathering common and recently introduced unsupervised anomaly detection (AD) methods, the results being generated using high-resolution range profiles. A semi-supervised AD (SAD) is considered to demonstrate the added value of having a few labeled anomalies to improve performances. Experiments were conducted with and without pollution of the training set with anomalous samples …
The linear frequency modulated (LFM) frequency agile radar (FAR) can synthesize a wide signal bandwidth through coherent processing while keeping the bandwidth of each pulse narrow. In this way, high range resolution profiles (HRRP) can be obtained without increasing the hardware system cost. Furthermore, the agility provides improved both robustness to jamming and spectrum efficiency. Motivated by the Newtonalized orthogonal matching pursuit (NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed as NOMP-FAR is designed to process each coarse range bin to extract the HR…
In considering human-machine interface (HMI) for smart environment, a simple but effective method is proposed for automatic arm motion recognition with a Doppler radar sensor. Arms, in lieu of hands, have stronger radar cross-section and can be recognized from relatively longer distances. An energy-based thresholding algorithm is applied to the spectrograms to extract the micro-Doppler (MD) signature envelopes. The positive and negative frequency envelopes are concatenated to form a feature vector. The nearest neighbor (NN) classifier with Manhattan distance (L1) is then used to recognize the …
Several schemes for gain control are used for preventing saturation of receiver, and overloading of data processor, tracker or display in pulse radars. The use of digital processing techniques open the door to a variety of digital automatic gain control schemes for analyzing digitized return signals and controlling receiver gain only at saturating clutter zones without affecting the detection at other zones. In this paper, we present a novel scheme of Digital Instantaneous Automatic Gain Control (DIAGC) which is based on storing digitally the dwell based clutter returns and deriving the gain c…
Deep neural networks (DNNs) have found applications in diverse signal processing (SP) problems. Most efforts either directly adopt the DNN as a black-box approach to perform certain SP tasks without taking into account of any known properties of the signal models, or insert a pre-defined SP operator into a DNN as an add-on data processing stage. This paper presents a novel hybrid-NN framework in which one or more SP layers are inserted into the DNN architecture in a coherent manner to enhance the network capability and efficiency in feature extraction. These SP layers are properly designed to …
The automatic classification of radar waveform is a fundamental technique in electronic countermeasures (ECM).Recent supervised deep learning-based methods have achieved great success in a such classification task.However, those methods require enough labeled samples to work properly and in many circumstances, it is not available.To tackle this problem, in this paper, we propose a three-stages deep radar waveform clustering(DRSC) technique to automatically group the received signal samples without labels.Firstly, a pretext model is trained in a self-supervised way with the help of several data…
University of Bologna（博洛尼亚大学）；CEA LETI（法国原子能和替代能源委员会电子信息技术实验室）
Digital maps will revolutionize our experience of perceiving and navigating indoor environments. While today we rely only on the representation of the outdoors, the mapping of indoors is mainly a part of the traditional SLAM problem where robots discover the surrounding and perform self-localization. Nonetheless, robot deployment prevents from a large diffusion and fast mapping of indoors and, further, they are usually equipped with laser and vision technology that fail in scarce visibility conditions. To this end, a possible solution is to turn future personal devices into personal radars as …
Range profiling refers to the measurement of target response along the radar slant range. It plays an important role in automatic target recognition. In this paper, we consider the design of transmit waveform to improve the range profiling performance of radar systems. Two design metrics are adopted for the waveform optimization problem: one is to maximize the mutual information between the received signal and the target impulse response (TIR); the other is to minimize the minimum mean-square error for estimating the TIR. In addition, practical constraints on the waveforms are considered, incl…
This paper investigates RF-based system for automatic American Sign Language (ASL) recognition. We consider radar for ASL by joint spatio-temporal preprocessing of radar returns using time frequency (TF) analysis and high-resolution receive beamforming. The additional degrees of freedom offered by joint temporal and spatial processing using a multiple antenna sensor can help to recognize ASL conversation between two or more individuals. This is performed by applying beamforming to collect spatial images in an attempt to resolve individuals communicating at the same time through hand and arm mo…
We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies which are generated during each gesture act. We detect the positive and negative envelopes separately, and form a feature vector of their augmentation. We use the $k$-nearest neighbor ($k$NN) classifier and Manhattan distance (L1) measure, in lieu of Euclidea…
Automatic Target Recognition (ATR) for military applications is one of the core processes towards enhancing intelligencer and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this paper surveys and assesses current SAR ATR architectures that employ the most popular dataset for the SAR domain, namely the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Based on the current methodology trends, we propose a taxonomy for the SAR ATR architectures, along wi…
Detection and classification of radars based on pulses they transmit is an important application in electronic warfare systems. In this work, we propose a novel deep-learning based technique that automatically recognizes intra-pulse modulation types of radar signals. Re-assigned spectrogram of measured radar signal and detected outliers of its instantaneous phases filtered by a special function are used for training multiple convolutional neural networks. Automatically extracted features from the networks are fused to distinguish frequency and phase modulated signals. Simulation results show t…
Automatic target recognition (ATR) based on inverse synthetic aperture radar (ISAR) images, which is extensively utilized to surveil environment in military and civil fields, must be high-precision and reliable. Photonic technologies' advantage of broad bandwidth enables ISAR systems to realize high-resolution imaging, which is in favor of achieving high-performance ATR. Deep learning (DL) algorithms have achieved excellent recognition accuracies. However, the lack of interpretability of DL algorithms causes the head-scratching problem of credibility. In this paper, we exploit the inner relati…
Ultralight Signal Classification Model for Automatic Modulation Recognition
Multiverse Computing（多元宇宙计算公司）；DAS Photonics（DAS光子学公司）；DIPC (Donostia International Physics Center)（多诺斯蒂亚国际物理中心（DIPC））；Ikerbasque Foundation for Science（伊克尔巴斯克科学基金会）
Eat-Radar: Continuous Fine-Grained Intake Gesture Detection Using FMCW Radar and 3D Temporal Convolutional Network with Attention
KU Leuven（鲁汶大学）；Vellore Institute of Technology（韦洛尔理工学院）；IMEC（比利时微电子研究中心）；Wageningen University and Research（瓦赫宁根大学及研究中心）；OnePlanet Research Center（壹行星研究中心）
Ground-penetrating radar (GPR) has been used as a non-destructive tool for tree root inspection. Estimating root-related parameters from GPR radargrams greatly facilitates root health monitoring and imaging. However, the task of estimating root-related parameters is challenging as the root reflection is a complex function of multiple root parameters and root orientations. Existing methods can only estimate a single root parameter at a time without considering the influence of other parameters and root orientations, resulting in limited estimation accuracy under different root conditions. In ad…
