Radar Detection Performance Research Articles

Reconfigurable Intelligent Surfaces assisted NLOS radar anti jamming using deep reinforcement learning

The complexity of the radar environment increases with technological advancement, especially when considering the difficulties presented by repeating jammers. These jammers can impede radar detection, especially when they create false targets in non-line-of-sight (NLOS) situations. This study focuses on optimizing the phase shifts of Reconfigurable Intelligent Surfaces (RIS) to address the problem of NLOS between a target and radar for detection in order to address these NLOS issues. Specifically, we investigate RIS phase shift optimization using a Genetic Algorithm (GA) to address the challenges posed by repeating jammers across various dynamic scenarios. Our objective is to increase the radar system’s ability to detect actual targets in non-LOS scenarios when repeater jammers are present in the environment. According to the experimental results, this method offers a practical way to mitigate the effects of repeater jammers by improving radar detection performance in NLOS environments.

Anti-Rain Clutter Interference Method for Millimeter-Wave Radar Based on Convolutional Neural Network

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly investigated, and the modeling of the millimeter-wave radar echo signal in a rainfall environment is completed. The effect of rainfall on radar detection performance is verified through experiments, and an anti-rain clutter interference method based on a convolutional neural network is proposed. The method combines image recognition and classification techniques to effectively distinguish target signals from rain clutter in radar echo signals based on feature differences. In addition, this paper compares the recognition results of the proposed method with VGGnet and Resnet. The experimental results show that the proposed convolutional neural network method significantly improves the target detection capability of the radar system in a rainfall environment, verifying the method’s effectiveness and accuracy. This study provides a new solution for the application of millimeter-wave radar in severe weather conditions.

Radar waveform generative design method

AbstractTo address the issue of radar detection performance degradation caused by interference in complex electromagnetic environments, this paper proposes a generative waveform design method and demonstrates its advantages. This method fully exploits waveform degrees of freedom to achieve a larger design space, generating a multitude of agile waveform parameters within the constraint envelope through algorithmic processes. This approach enables the radar to maintain detection performance in complex electromagnetic environments. Compared with the traditional waveform design method, the simulation experiment verifies the effectiveness of the generative waveform design method.

Isolated point clutter suppression method for airborne STAP radar in wind farm environment

With the large-scale construction of wind farms, wind turbine isolated point clutter has an increasingly serious impact on airborne radar target detection performance. Traditional space-time adaptive processing methods cannot suppress wind turbine clutter (WTC) with spectrum broadening characteristics, which may lead to a decrease in target detection probability and an increase in false alarm rate. In this paper, a WTC suppression method for airborne radar based on micro-Doppler features is proposed, and we construct the feature subspace of wind turbine echo to distinguish wind turbine, target, clutter, and noise. First, the Sobel operator is used to process the radar range-Doppler spectrum, and the range cells of the wind turbines are preliminarily judged. Then the smallest of constant false alarm rate (SOCFAR) method is used to further confirm the range cells where the wind turbines are located. Next, Mahalanobis distance is used to estimate the optimal dictionary atomic parameters of WTC, and the updated dictionary atoms are used to construct an orthogonal projection matrix to suppress WTC. Finally, short-range nonstationary clutter and sidelobe clutter are suppressed by space-time adaptive segment processing. On the one hand, the proposed method realizes the accurate positioning of wind turbines through image edge detection and constant false alarm detection. On the other hand, Mahalanobis distance is used to estimate the atomic parameters of the wind turbine dictionary, which ensures the homogeneity of wind turbine samples after clutter suppression. The simulation and measured data results show that the proposed method can significantly reduce the false alarm rate caused by WTC while ensuring the effective detection of the target.

Radar shielding jamming method based on random phase modulation of digital coding metasurface

In this paper, a novel radar jamming method based on random phase modulation of digital coding metasurface was proposed. For typical radar system, the modulation model of coding metasurface and the echo model are deduced firstly. Then, the ordered statistics greatest of the constant false alarm rate (OSGO-CFAR) detection model was chosen to analyze the radar detection performance. Through serials of simulation, the influence of modulation modes, modulation switching interval, the number of coding elements and the signal-to-noise ratio (SNR) on radar detection performance is analyzed. Finally, the Monte Carlo simulation is performed. The result shows that as the modulation switching interval of metasurface decreases, the spectrum energy distribution of the target becomes more dispersed and the detection probability of the target decreases obviously. Traditional radar detectors will become ineffective, and the shielding of the target will be achieved. This implies a new radar jamming method which can realize target shielding instead of traditional stealth by reducing the radar cross section.

Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals.

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning.

Power Allocation Scheme for Multi-Static Radar to Stably Track Self-Defense Jammers

Due to suppression jamming by jammers, the signal-to-interference-plus-noise ratio (SINR) during tracking tasks is significantly reduced, thereby decreasing the target detection probability of radar systems. This may result in the interruption of the target track. To address this issue, we propose a multi-static radar power allocation algorithm that enhances the detection and tracking performance of multiple radars in relation to their targets by optimizing power resource allocation. Initially, the echo signal model and measurement model of multi-static radar are formulated, followed by the derivation of the Bayesian Cramér–Rao lower bound (BCRLB). The multi-objective optimization method is utilized to establish the objective function for joint tracking and detection, with dynamic adjustment of the weight coefficient to balance the tracking and detection performance of multiple radars. This ensures the reliability and anti-jamming capability of the multi-static radar system. Simulation results indicate that the proposed algorithm can prevent the interruption of jammer tracking and maintain robust tracking performance.

Applying auxiliary arrays for mainlobe blanking to counter mainlobe repeater jamming

Mainlobe repeater jamming exploits mainlobe gain of radar antenna and signal-processing gain to significantly disrupt the target detection performance of radar. The combination of multiple auxiliary radars to form a distributed array radar improves the radar system’s angle resolution, enabling the realization of traditional spatial jamming cancellation. However, the jamming cancellation method struggle to effectively suppress multiple mainlobe repeater jamming signals when their number exceeds that of auxiliary radars, resulting in pattern distortion and subsequent loss of target energy. To address this issue, the mainlobe blanking method with auxiliary radars is proposed based on the specific characteristics of the jamming signals being distributed in multiple discontinuous peaks at the same angle. This approach leverages the superior angular resolution to accurately differentiate between target and jamming signals based on their distance-angle characteristics. Moreover, specific jamming suppression methods are employed for each jamming signal based on whether it overlaps with the target in the time domain, minimizing the loss of target energy. Furthermore, the proposed method is capable of suppressing jamming even when the target and jamming signals overlap in the time domain, and is not constrained by the number of auxiliary radars available. Simulations and experiments are used to validate the effectiveness and correctness of the proposed method in suppressing mainlobe repeater jamming.

On the Detection of Vulnerable Road Users—Simulating Limits of an FMCW Radar

The complexity of urban traffic environments poses unique challenges for automotive radar sensors. Limited range and Doppler resolution hinder the ability to distinguish closely spaced objects and detect vulnerable road users, particularly pedestrians. Therefore, it is important to know the limits of FMCW radars. To assess these limitations, the WinProp channel simulator is used to model and simulate traffic scenarios regarding the detection of the range, velocity, and SNR of targets. To validate WinProp as a simulation tool for traffic scenarios, a real traffic scenario is configured and the simulation results compared to the measurement results in order to demonstrate the suitability of WinProp for simulating traffic scenarios and evaluating radar detection performance. The close agreement between the simulation and measurement results provides validation for WinProp’s accuracy in modeling detections and signal processing within traffic scenarios. To showcase the limitations of FMCW radars, two scenarios with challenging detection conditions are simulated: an occluded pedestrian between parked vehicles and a densely populated road. A monostatic setup with a single patch antenna and a 16 × 16 patch antenna array is evaluated, using chirp bandwidths of 1 GHz and 4 GHz. The simulations have shown that although it is feasible to detect an occluded pedestrian by multipath propagation, there is still a need in improving the detection performance when there is no multipath from an occluded pedestrian. Furthermore, the results indicate that higher bandwidth improves target separation but is insufficient for occluded pedestrian detection without multipath. However, with some targets, even the range resolution at 4 GHz was not sufficient, which required a separation in the Doppler dimension, emphasizing the need for overall resolution improvement of FMCW radars. Future work should focus on developing more complex road user models and simulating a broader range of scenarios to comprehensively evaluate radar performance in road traffic environments.

Airborne Radar Space–Time Adaptive Processing Algorithm Based on Dictionary and Clutter Power Spectrum Correction

Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance.

An Interference Mitigation Method for FMCW Radar Based on Time-Frequency Distribution and Dual-Domain Fusion Filtering.

Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time-frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar's beat signal transformation from time domain to time-frequency domain, thus converting the interference mitigation to the task of time-frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time-frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches.

Joint waveform design and resource allocation strategy for cognitive radar target situation awareness

AbstractTraditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value.

Effect of Haze Particles on Detection Performance of Quantum Interferometric Radar and Simulation

Effect of Haze Particles on Detection Performance of Quantum Interferometric Radar and Simulation

Improving PA-MIMO radar detection performance through transceiver subarray configuration optimization under QoSbased model

Improving PA-MIMO radar detection performance through transceiver subarray configuration optimization under QoSbased model

Sea Clutter Suppression Using Smoothed Pseudo-Wigner–Ville Distribution–Singular Value Decomposition during Sea Spikes

The detection of small targets within the background of sea clutter is a significant challenge faced in radar signal processing. Small target echoes are weak in energy, and can be submerged by sea clutter and sea spikes, which are caused by overturning waves and breaking waves. This severely affects the radar target detection performance. This paper proposes a smoothed pseudo-Wigner–Ville distribution–singular value decomposition (SPWVD-SVD) method for sea clutter suppression. This method determines the instantaneous frequency range of the target by contrasting the time–frequency characteristics of the sea spike and the target. Subsequently, it employs a singular value difference spectrum to reduce the rank of the Hankel matrix, thereby reducing the computational burden of the instantaneous frequency estimation step in the experiment. Based on the instantaneous frequency range of the target in the time–frequency domain, the singular values of the target signal are retained, while the singular values of clutter are set to zero. This process accomplishes the reconstruction of radar echo signals and effectively achieves the suppression of sea clutter. The suppression effect is verified using simulation data alongside ten sets of Intelligent Pixel processing X-band (IPIX) radar data against the background of sea spikes. By contrasting the clutter amplitudes before and after suppression, the SPWVD-SVD algorithm demonstrated an average clutter suppression of 15.06 dB, which proves the effectiveness of the proposed algorithm in suppressing sea clutter.

AIS aided marine radar target tracking in a detection occluded environment

Radar signal occlusion due to islands and large ships occurs frequently in maritime environments, degrading the performance of marine radar target detection and tracking. In order to address the common track breakages and tracking inaccuracies during occlusion, both prior environment knowledge from electronic navigation chart (ENC) and information from automatic identification systems (AIS) are exploited. An AIS-aided integrated probabilistic data association (A-IPDA) algorithm is proposed for maritime target tracking in an occluded environment. It recursively identifies target occlusion based on environmental knowledge and applies a tailored track existence probability transition matrix for track existence estimation during occlusion, meanwhile rich AIS information is exploited to maintain target tracking performance. Experiments based on both simulated data and real data show that A-IPDA has the ability to counter long period target occlusion in complex maritime environments, where track continuity and tracking accuracy can be well maintained.

LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function.

In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models.

Radar Detection Performance via Frequency Agility Using Measured UAVs RCS Data

Radar Detection Performance via Frequency Agility Using Measured UAVs RCS Data

Research on Radar Clutter Suppression Method Based on Stagger MTI

A large number of clutter interference signals were involved in radar echoes. Radar detection and tracking performance degrade when the signal-to-noise ratio of the echo signal is too low. Therefore, how to suppress clutter interference is a problem that is worth studying in designing. In this paper, the statistical characteristics of radar clutter were analyzed, and a clutter suppression method based on staggered MTI was proposed for the blind speed problem of traditional MTI filters. Simulation experiments verified the model and processing method of staggered filtering. The results indicated that the method improved the blind speed while effectively suppressing the clutter.

Photonics-enabled joint communication-radar system with improved detection performance and looser symbol length limitation based on resampling and phase compensation

A photonics-enabled joint communication-radar (JCR) system by employing a multiple phase-shift keying (MPSK)-sliced linearly frequency-modulated (LFM) signal for simultaneous range measurement, microwave imaging, and data transmission is proposed and experimentally demonstrated with improved detection performance and looser symbol length limitation. A two-stage modulation in conjunction with optical filtering is utilized at the radar transmitter for the generation of the frequency-and-bandwidth-multiplied MPSK-sliced LFM signal while resampling and phase compensation operations are introduced at the radar receiving end to break the detection performance limitation induced by the communication symbol length when MPSK-sliced LFM signal is used. We demonstrate the concept experimentally. BPSK-sliced and QPSK-sliced LFM signals centered at 12 GHz are generated for JCR systems. When the time difference between the radar echo and the de-chirping reference is close to or greater than one symbol length, range measurement and microwave imaging that are hard to be fulfilled due to the random phase jumps of the de-chirped signal can be successfully realized via resampling and phase compensation. Experiments show that the proposed resampling and phase compensation method is valid for improving radar detection performance and loosening the communication symbol length limitation. Data communication with a data rate of up to 400 Mbps and ISAR imaging with a radar signal bandwidth of 1 GHz are successfully realized.