Radar Target Detection Research Articles

SAR target detection based on the optimal fractional Gabor spectrum feature

In this paper, an algorithm based on a fractional time-frequency spectrum feature is proposed to improve the accuracy of synthetic aperture radar (SAR) target detection. By extending the fractional Gabor transform (FrGT) into two dimensions, the fractional time-frequency spectrum feature of an image can be obtained. In the achievement process, we search for the optimal order and design the optimal window function to accomplish the two-dimensional optimal FrGT. Finally, the energy attenuation gradient (EAG) feature of the optimal time-frequency spectrum is extracted for high-frequency detection. The simulation results show that the proposed algorithm has a good performance in SAR target detection and lays the foundation for recognition.

LSCB: a lightweight feature extraction block for SAR automatic target recognition and detection

ABSTRACT Automatic target recognition and detection in synthetic aperture radar (SAR) images is playing a significant role in military and civilian fields. Traditional methods are weak in migration capabilities and cumbersome in manual design. Existing deep learning-based methods can achieve high recognition and detection accuracy, but most of them have a very large number of parameters and slow recognition and computational efficiency, which makes it difficult for them to migrate to edge computing equipments, and computational efficiency is crucial in rapid maritime rescue and emergency military deployment. Therefore, we propose a lightweight feature extraction block, lightweight split concat block (LSCB), for SAR automatic target recognition and detection. Specifically, LSCB consists of several operators, such as split, depthwise convolution (DC), multiple kernel sizes block (MKSB), and channel-shuffle (CS). MKSB is a novel channel attention mechanism designed by us, which can make feature maps have target features of different scales and adjust their receptive field sizes adaptively. In addition, to assess the performance of LSCB, we use LSCB to build LSCNet-R and LSCNet-D for SAR target recognition and detection, respectively, and conduct extensive experiments on MSTAR, SSDD, and SAR-Ship-Dataset. The experimental results show that our LSCNet-R and LSCNet-D built with LSCB achieve higher recognition and detection accuracy and computational efficiency with few parameters compared with existing excellent methods. This further illustrates that the proposed LSCB can extract fine and stable features with few parameters.

Echoformer: Transformer Architecture Based on Radar Echo Characteristics for UAV Detection

While recent years have witnessed an increasing number of commercial applications of unmanned aerial vehicles (UAVs), an imperative problem people have to face is the rapid growth of malicious use. So, it is imperative for security agencies to develop anti-UAV technology. The introduction of deep learning (DL) has a positive influence on radar signal processing, but DL-based methodologies have yet to be widespread in radar target detection because of the lack of unique architecture based on radar echo characteristics and the annotation method of radar data. In this article, a novel Transformer-based architecture is proposed, which transforms the problem of UAV detection into a binary classification task in each range cell. The complex encoder architecture and the Transformer-based extractor are designed to extract the Doppler frequency shift feature and the micro-Doppler signature (mDS) of a UAV simultaneously. The well-designed architecture based on radar echo characteristics can achieve a combination training of echoes with different coherent processing intervals (CPIs). In addition, we provide an annotation method and a data augmentation skill for our real measured dataset. The results of the experiment demonstrate that the proposed method has better detection performance and measuring accuracy under different SNRs in comparison with traditional radar target detection and other DL-based methods.

Joint Secure Transmit Beamforming Designs for Integrated Sensing and Communication Systems

Integrated sensing and communication (ISAC), which allows individual radar and communication systems to share the same spectrum bands, is an emerging and promising technique for alleviating spectrum congestion problems. In this paper, we investigate how to exploit the inherent interference from strong radar signals to ensure the physical layer security (PLS) for the considered multi-user multi-input single-output (MU-MISO) communication and colocated multi-input multi-output (MIMO) radar coexistence system. In particular, with known eavesdroppers' channel state information (CSI), we propose to jointly design the transmit beamformers of communication and radar systems to minimize the maximum eavesdropping signal-to-interference-plus-noise ratio (SINR) on multiple legitimate users, while guaranteeing the communication quality-of-service (QoS) of legitimate transmissions, the requirement of radar detection performance, and the transmit power constraints of both radar and communication systems. When eavesdroppers' CSI is unavailable, we develop a joint artificial noise (AN)-aided transmit beamforming design scheme, which utilizes residual available power to generate AN for disrupting malicious receptions as well as satisfying the requirements of both legitimate transmissions and radar target detection. Extensive simulations verify the advantages of the proposed joint beamforming designs for ISAC systems on secure transmissions and the effectiveness of the developed algorithms.

Radar Target Detection by Using Levenberg-Marquardt Algorithm

Radar Target Detection by Using Levenberg-Marquardt Algorithm

A Radar Detection Method of Reentry Target Based on Frequency-Domain Processing

When the reentry target passes through the earth’s atmosphere, its surface is covered with a layer of plasma sheath. In the process of radar detection of the reentry target, the echo signal will couple with multi-Doppler frequency components and form multiple mismatch effects with matched filter, which will produce false targets on the 1-D range profile. Meanwhile, the excessive speed of the reentry target will lead to the phenomenon of migration through range cells (MTRCs), which will further affect the detection and recognition of the target. This article proposes an improved frequency-domain compensation method that aims at false target and cross-range unit migration. By constructing the intrapulse Doppler frequency and interpulse Doppler frequency compensation functions, the multiperiod echo signal is processed to eliminate the MTRC phenomenon and improve the energy gain of the real target. Finally, the feasibility of the proposed algorithm is verified by simulation, which lays a theoretical foundation for the effective detection and reliable tracking of plasma-sheath-covered targets.

Radar Band Fusion Using Frame-Based Compressed Sensing

Existing compressed sensing algorithms fail when applied to radar target detection in the presence of a large gap in the frequency band, i.e., presence of signals in separate, discontinuous bands. A new algorithm based on a subdivision-fusion scheme is proposed to solve this problem. The main goal is to use a structured sensing matrix based on radar signals to an advantage and obtain a good range resolution in spite of high coherence. Parameters influencing the performance of the algorithm are discussed. Simulative examples and results based on real measurement data are presented. The results show superior performance of the proposed method in the presence of band gaps.

A MIMO moving target detection radar with optimal transmit and receive weightage

Target detection using multiple-input multiple-output (MIMO) radar has recently gained popularity in radar research due to its ability to mitigate the target radar cross-section fading and substantial spatial diversity gain. Recently, an optimal MIMO radar detector, including optimal transmit and receive weightage, has been proposed for the detection of steady targets. A moving target differs the scenario from a steady target case due to its Doppler. Hence, the optimal detector of a steady target case does not work for the case of a moving target. Therefore, in this paper, we proposed a MIMO radar detector for the context of moving targets, including optimal transmit and receive weight schemes. For obtaining the proposed detector, knowledge about the target fading that can be applied to a moving target scenario is a prerequisite. Therefore, we proposed to use a training mechanism in which a few transmit pulses are introduced once in every coherent processing interval, and the corresponding target returns are used to estimate the target fading. However, in a moving target scenario, the target returns are Doppler-steered versions of the target fading. Therefore, it is proposed to compensate the Doppler for target fading estimation by matching a steering vector to the target returns. This type of use of online estimation of the target fading characteristics and applying it for moving target detection is the first of its kind in radar research. The proposed radar is shown to outperform the state-of-the-art MIMO radars addressed in the literature.

Deep learning-based circular disk type radar target detection in complex environment

Target identification is one of the most popular radar uses in real life. Target identification is a classifier that analyzes whether a signal contains an echo from a target (target-present) or is merely noise (target-absent). Deep learning techniques are a popular topic in classification, and they have evinced to be effective in a range of applications. In this paper, a 64 layers Circular Disk type RADAR Target Detection (CDRTD) model is proposed based on Transfer Learning using the SqueezeNet architecture of Convolutional Neural Network (CNN) that functions directly with processed radar target return eco signal and minimize the requirement of conventional laborious radar signal processing. Further, the proposed 64 layers SqueezeNet-based CNN CDRTD model was then implemented to identify circular disk type targets in complex environment. Finally, the target return eco data was tested to identify the circular disk type radar target in complex environments. We further analyzed target detection probability, false alarm rate, precision, recall, F1 in a complex environment and compared it with the ideal case. We found that our proposed CDRTD model can classify 83.3% of the test samples correctly with an overall accuracy of 94.59% in a noisy and cluttered environment whereas 100% of the test samples are classified correctly with an overall accuracy of 100% in an ideal environment.

Weak Signal Processing Method for Moving Target of GNSS-S Radar Based on Amplitude and Phase Self-Correction

Navigation satellite signals have the advantages of all-day, all-weather, and global coverage, and the use of navigation signals for the detection of moving targets has significant application prospects. However, the GNSS signal is very weak, and the signal power is greatly attenuated after being scattered by the target. In order to detect the echo signal, a long integration time is required. However, the movement of the target will cause the echo signal to produce unpredictable range migration and a Doppler frequency shift, which will weaken the cumulative effect of long-term integration. This paper proposes a weak signal processing method with amplitude and phase self-correction for moving target detection in the GNSS-S radar. First, the phase consistency of the echoes of a single GNSS satellite is realized by the block expansion compression and phase differential correction method to improve the coherent accumulation gain; then, multi-star joint accumulation is carried out after the signal amplitudes of multiple satellites are corrected by the improved keystone method, so as to obtain a stable echo signal track. This method can effectively improve the integral gain of the scattering signal of the moving target and realize target detection. The simulation results and field tests show that this method can effectively improve the SNR of the GNSS-S signal and can realize the detection of small moving targets such as cars with GNSS-S radar.

Improving the Performance of RODNet for MMW Radar Target Detection in Dense Pedestrian Scene

In the field of autonomous driving, millimeter-wave (MMW) radar is often used as a supplement sensor of other types of sensors, such as optics, in severe weather conditions to provide target-detection services for autonomous driving. RODNet (A Real-Time Radar Object-Detection Network) is one of the most widely used MMW radar range–azimuth (RA) image sequence target-detection algorithms based on Convolutional Neural Networks (CNNs). However, RODNet adopts an object-location similarity (OLS) detection method that is independent of the number of targets to obtain the final target detections from the predicted confidence map. Therefore, it gives a poor performance on missed detection ratio in dense pedestrian scenes. Based on the analysis of the predicted confidence map distribution characteristics, we propose a new generative model-based target-location detection algorithm to improve the performance of RODNet in dense pedestrian scenes. The confidence value and space distribution predicted by RODNet are analyzed in this paper. It shows that the space distribution is more robust than the value distribution for clustering. This is useful in selecting a clustering method to estimate the clustering centers of multiple targets in close range under the effects of distributed target and radar measurement variance and multipath scattering. Another key idea of this algorithm is the derivation of a Gaussian Mixture Model with target number (GMM-TN) for generating the likelihood probability distributions of different target number assumptions. Furthermore, a minimum Kullback–Leibler (KL) divergence target number estimation scheme is proposed combined with K-means clustering and a GMM-TN model. Through the CRUW dataset, the target-detection experiment on a dense pedestrian scene is carried out, and the confidence distribution under typical hidden variable conditions is analyzed. The effectiveness of the improved algorithm is verified: the Average Precision (AP) is improved by 29% and the Average Recall (AR) is improved by 36%.

A Parallel Principal Skewness Analysis and Its Application in Radar Target Detection

Radar is often affected by various clutter backgrounds in complex environments, so clutter suppression has important practical significance for radar target detection. The clutter suppression process conforms to the blind source separation (BSS) model. The principal skewness analysis (PSA) algorithm is a BSS algorithm with third-order statistics as the objective function, and its running speed is faster than the conventional BSS algorithm. Still, the PSA algorithm has the problem of error accumulation. This paper improves the PSA algorithm and proposes a parallel PSA (PPSA) algorithm. PPSA can estimate the directions corresponding to each independent component simultaneously and avoid the problem of error accumulation. PPSA uses parallel instead of serial computing, significantly improving the running speed. In this paper, the PPSA algorithm is applied to radar target detection. The simulation data and real data experiments verify the effectiveness and superiority of the PPSA algorithm in suppressing clutter.

Radar Target Detection via Global Optimality Conditions for Binary Quadratic Programming

Radar Target Detection via Global Optimality Conditions for Binary Quadratic Programming

Radar Target Detection and Localization Aided by an Active Reconfigurable Intelligent Surface

Radar Target Detection and Localization Aided by an Active Reconfigurable Intelligent Surface

Imaging Algorithm for Inverse Synthetic Aperture Radar in Condition of Non-Uniform Data Rate

Inverse synthetic aperture radar imaging is an important technique for radar detection and recognition of non-cooperative targets. However, for most of the existing imaging methods, pulse signals are transmitted and received at equal intervals, without considering the possible problems under condition of non-uniform data rate, such as accuracy decline of echo's range alignment and high side lobes in Doppler spectrum. The resulting vertical and horizontal defocusing can reduce the imaging quality and affect the feature extraction of the target. Therefore, an inverse synthetic aperture radar imaging method for non-uniform data rate is proposed. First, by designing the weight to simulate the alignment situation of uniform data rate, the correlation method with weighted sliding window is used to solve the problems of declined range alignment accuracy and vertical defocusing. Then, based on the linear form of non-uniform Fourier transform, the Fourier base matrix of non-uniform data rate is built, and CLEAN technology is used to complete the projection of echo signal on the bases iteratively. Finally, the Fourier basis matrix with uniform data rate is used to reconstruct the echo signal, which solves the problem of horizontal defocusing and makes the data rate of echo signal uniform. The effectiveness of the proposed method is verified by experiments using measured data.

Lightweight Deep Neural Networks for Ship Target Detection in SAR Imagery.

In recent years, deep convolutional neural networks (DCNNs) have been widely used in the task of ship target detection in synthetic aperture radar (SAR) imagery. However, the vast storage and computational cost of DCNN limits its application to spaceborne or airborne onboard devices with limited resources. In this paper, a set of lightweight detection networks for SAR ship target detection are proposed. To obtain these lightweight networks, this paper designs a network structure optimization algorithm based on the multi-objective firefly algorithm (termed NOFA). In our design, the NOFA algorithm encodes the filters of a well-performing ship target detection network into a list of probabilities, which will determine whether the lightweight network will inherit the corresponding filter structure and parameters. After that, the multi-objective firefly optimization algorithm (MFA) continuously optimizes the probability list and finally outputs a set of lightweight network encodings that can meet the different needs of the trade-off between detection network precision and size. Finally, the network pruning technology transforms the encoding that meets the task requirements into a lightweight ship target detection network. The experiments on SSDD and SDCD datasets prove that the method proposed in this paper can provide more flexible and lighter detection networks than traditional detection networks.

Saliency-Based SAR Target Detection via Convolutional Sparse Feature Enhancement and Bayesian Inference

Traditional synthetic aperture radar (SAR) target detection methods use matched filtered SAR images as input, and the detection performance is restricted due to the high sidelobes and speckle noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing sidelobes, noise, and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this article, to improve the target detection performance using sparse SAR images as input, we proposed a convolutional sparse feature enhancement method to meet the needs of Bayesian saliency detection. The proposed Bayesian saliency joint target detection method comprised the following three steps: first, to obtain sparse SAR images with continuous contours and fewer holes in the target area, we proposed a convolutional L1 sparse regularization method. Second, a regularization parameter optimization method was derived to quickly obtain optimal regularization parameters for saliency detection. Finally, target detection results were obtained through a superpixel-based Bayesian saliency joint detector. Extensive experiments verified that the proposed method could improve the SAR target detection accuracy in complex backgrounds.

Clutter Removal Method for GPR Based on Low-Rank and Sparse Decomposition With Total Variation Regularization

The performance of ground penetrating radar (GPR) target detection is seriously affected by the clutter. In this letter, an effective GPR clutter removal method is proposed based on low-rank and sparse decomposition with total variation regularization (LRSD-TVR). In the proposed method, a total variation (TV) regularization of sparse matrix is introduced to further remove the remaining clutter and to obtain a clearer target image. An iterative approach based on alternating direction method of multipliers (ADMM) is developed to solve the optimization problem of LRSD-TVR. In each iteration, the low rank component which corresponds to the clutter is computed by singular value decomposition (SVD) thresholding. Besides, the sparse component corresponding to the target is obtained by solving the sub-optimization problem reformulated in terms of TV component. The effectiveness of proposed method is verified by both numerical simulations and field experiments.

Design of A W-band Dual-circular-polarization Monopulse Cassegrain Antenna for Polarization Detection of Radar Target

Design of A W-band Dual-circular-polarization Monopulse Cassegrain Antenna for Polarization Detection of Radar Target

Transmitted-Signal-Free Target Information Extraction for OFDM-Based Passive Radar With Time-Varying Sparse Model

Compressive sensing theory have been proposed in the field of radar for target detection. The challenge of compressive sensing applied to passive bistatic radar lies in the high computational complexity aggravated by the transmitted signal dependent time-varying sparse model. So, we propose a transmitted-signal-free and time-invariant sparse model for passive radar based on orthogonal frequency division multiplexing waveforms. We first generate the sparse model by using the surveillance signal and pilot information only, and exploiting the sparsity of scene including only a few targets and clutter. Then, range-Doppler profile for target detection can be implemented based on our proposed sparse model. Finally, simulation and experimental results illustrate that our proposed sparse model has high detection performance.