As a cost-effective and robust technology, automotive radar has seen steady improvement during the last years. Radio frequency (RF) images, serving as a radar data format with rich semantic information, have attracted considerable interest in radar object detection. Previous RF-based models heavily rely on convolutional neural networks, leading to the high computational cost. To solve this problem, we propose a model called Mask-RadarNet to fully utilize the hierarchical semantic features from the RF image sequences. Mask-RadarNet exploits the combination of interleaved convolution and attention operations in the encoder. In addition, patch shift is introduced to Mask-RadarNet for efficient spatial-temporal feature learning. By shifting part of patches with a specific mosaic pattern in the temporal dimension, Mask-RadarNet achieves competitive performance while reducing the computational burden of the spatial-temporal modeling. In order to capture the spatial-temporal semantic contextual information, we design the class masking attention module (CMAM) in our encoder. Moreover, a lightweight auxiliary decoder is added to our model to aggregate prior maps generated from the CMAM. Experiments on the CRUW dataset demonstrate that the proposed Mask-RadarNet achieves state-of-the-art performance with relatively lower computational complexity and fewer parameters.

UAV-assisted joint radar and communication (JRC) systems are widely adopted in Internet of Things applications. This is due to their convenience, affordability, and space and resource-saving. A joint power, bandwidth, and subchannel allocation (JPBSA) strategy is proposed for a UAV-assisted dual-function radar and communication (DFRC) network. The predicted posterior Cramér-Rao lower bound (PCRLB) is utilized to measure the target tracking accuracy. The optimization model is established as minimizing the sum of weighted predicted PCRLBs while meeting the communication data ratio (CDR) requirements, total power and bandwidth budget. It is shown that the JPBSA problem involves a mixed-integer programming (MIP) problem. Even worse, the bandwidth and subchannel are coherent. A three-stage alternating optimization method (TSAOM) is constructed for the solution. By integrating the radar and communication power allocation as a whole, the alternating ascent-descent method (AADM) is employed to solve the power allocation. Then, two propositions are proposed to provide the upper bounds for bandwidth allocation. Finally, the subchannel allocation is solved using a greedy search-based method. Simulation results confirm the effectiveness and efficiency of the proposed method, compared with the state-of-the-art methods. It also shows that using the PCRLB as the optimization metric is better than the signal-to-interference-plus-noise ratio (SINR) and mutual information (MI).

The generalized direction detection (GDD) problem involves determining the presence of a signal of interest within matrix-valued data, where the row and column spaces of the signal (if present) are known, but the specific coordinates are unknown. Many detectors have been proposed for GDD, yet there is a lack of analytical results regarding their statistical detection performance. This paper presents a theoretical analysis of two adaptive detectors for GDD in scenarios with known spatial steering vectors. Specifically, we establish their statistical distributions and develop closed-form expressions for both detection probability (PD) and false alarm probability (PFA). Simulation experiments are carried out to validate the theoretical results, demonstrating good agreement between theoretical and simulated results.

Over-the-horizon radar (OTHR) utilizes the ionospheric refraction and reflection in high-frequency band for air-sea targets detection. However, non-stationary ionospheric dynamics induce inhomogeneous distortions in sea clutter and targets, significantly degrading detection and localization performance. To address this challenge, we present a comprehensive investigation on OTHR full-link modeling and simulation to systematically analyze the impact of various trans-ionospheric propagation effects on echo signals. In Part I, we develop a unified framework for full-link modeling of sea clutter that incorporate background ionospheric and oceanic conditions, enabling simulation and analysis of sea clutter characteristics. Firstly, we establish the models for radar cross section of sea clutter and ionospheric propagation effects. Key parameters, including the wave propagation range, actual range, group delay, phase disturbance, and propagation loss, are calculated based on the Appleton-Hartree formula and ray tracing technique. Secondly, we construct the echo signal models in the fast- and slow-time domain and derive the corresponding range-Doppler spectrum. The origin mechanism and intrinsic cause of Doppler shifting, broadening, and splitting, as well as range localization errors are theoretically analyzed. Finally, simulation experiments of three scenarios are designed to produce OTHR sea clutter data in sea and air modes, which are validated in comparison with the real data. The typical phenomena of Doppler shifting, broadening, and splitting observed in real data are reproduced, and the results indicate that the multi-mode propagation is the main obstacle to range localization and ionospheric decontamination. The full-link sea clutter model provides critical insights for the subsequent signal processing tasks including ionospheric decontamination, clutter suppression, target detection, and localization.

In this article, we address the problem of clutter covariance matrix estimation for radar adaptive detection. Traditional estimation methods are usually based on specific models. However, performance will experience degradation in the presence of model mismatch, which occurs commonly in reality. Therefore, we resort to the data-driven deep learning method and construct a network based on the convolutional neural network (CNN) to estimate the clutter covariance matrix. Besides, due to the unavailable ground truth of the covariance matrix of measured data, simulated data are usually applied for training as a compromise. We design a loss function according to the whitening metric, which makes it possible to train the network directly by measured data. Compared with traditional covariance matrix estimators, the proposed network estimator has higher whitening ability. Moreover, we exploit the obtained covariance matrix estimations to an adaptive detector to evaluate the detection performance. Results with the Intelligent Pixel (IPIX) datasets show that the detector applying the network covariance matrix estimator gains a higher probability of detection (PD).

With the increasing of commercial communication applications, the scarcity of spectrum resources is becoming obvious, and the joint radar and communication (JRC) systems have been attracted much attention. The resource allocation technique is crucial for mitigating mutual interference and enhancing radar sensing and communication performance in JRC systems. A strategy for joint target and user assignment, power allocation, and subchannel allocation (JCAPASA) in an unmanned aerial vehicle (UAV)-based radar and communication coexistence (RCC) network is proposed. The predicted conditional Cramér-Rao lower bound (PC-CRLB) incorporating uncertainty of sensor location (USL) is derived and utilized as the optimization metric. The optimization model aims to minimize the sum of weighted PC-CRLBs for multiple targets while adhering to constraints, such as communication data ratio (CDR) requirements, beam assignment, power budget, and subchannel nonoverlap. The optimization model is nonconvex and falls into the NP-hard class. An iterative descent-based optimization method (IDBOM) is proposed for its solution. The simulation results confirm the efficiency and effectiveness of the proposed strategy compared to state-of-the-art methods. The results also imply that the proposed strategy comprehensively considers spatial diversity and power budget to maximize tracking performance while meeting the CDR requirements.

Multichannel adaptive detection (MAD) can achieve better performance compared with the constant false alarm rate (CFAR) methods in target detection in inhomogeneous clutter. However, its application still faces many challenges, such as the lack of sufficient training samples and huge computational costs. In this letter, a low-complexity reduced-dimension MAD (RD-MAD) scheme in an inhomogeneous clutter environment is proposed based on arbitrary subarray synthesis. By this scheme, we derive the RD generalized likelihood ratio test (GLRT). The theoretical performance of the proposed method is analyzed, including the CFAR property, RD performance, and computational complexity. Finally, with tri-channel X-band airborne radar real data, the detection performance of the proposed RD-MAD scheme is verified. Compared with the existing detectors, the proposed detector can provide better detection performance in sample-insufficient environments with much lower computational complexity.

Detection performance of the over-the-horizon radar (OTHR) is seriously affected by transient interference such as atmospheric noise, meteor echoes, and lightning strikes. In this letter, a transient interference suppression method is proposed based on minimax concave (MC) penalty. The proposed method utilizes the sparsity of transient interference in the time domain and sea clutter (or targets) in the frequency domain. The sparsity is constrained by MC penalty in the objective function, and an$\ell _{2} $-norm is applied to separate noise components. Finally, the extended projected generalized gradient (PGG) method is used to minimize the objective function to suppress transient interference. Without additional interference localization and data completion, the proposed method locates, suppresses the transient interference, and recovers pure spectrum simultaneously. In addition, it has higher spectrum reconstruction accuracy and output signal-to-noise ratio (SNR) compared with the existing methods. The effectiveness of the proposed method is verified by the real data of the OTHR.

In the case of distributed target detection in unknown Gaussian noise, training data are often limited, and signal mismatch is a common issue. To tackle these problems, we utilize the Bayesian theory by taking the noise covariance matrix as an inverse Wishart distribution. Our approach involves incorporating a fictitious determinist jamming signal in the signal-absence hypothesis to create a selective detector. Although this detector provides enhanced capability to reject mismatched signals, it comes at the cost of lower detection performance in the absence of signal mismatch. We propose a flexible Bayesian detector to tackle this limitation, wherein a customizable parameter can regulate the performance of mismatched signals. The tunable Bayesian detector is particularly robust to signal mismatch. In addition, it can achieve a higher probability of detection (PD) compared with existing methods when the tunable parameter is appropriately adjusted for matched signals. All the proposed Bayesian detectors can work with limited training data or even with no training data. The effectiveness of the proposed detectors is demonstrated through both simulated and actual data.

This letter introduces a novel coalitional game-theoretic power allocation (CGTPA) paradigm, tailored for resource management for communication and netted radar systems. Taking the power allocation for enhancing downlink throughput in a distributed antenna system (DAS) as a clear-cut example, theoretical expositions, and experimental simulations are presented accordingly. As an optimal decision-making method grounded in behavioral rules, the CGTPA method distinguishes itself from the methods yielded by conventional convex optimization and heuristic methods. It evolves around the idea that cooperation among antennas can be modeled as a coalitional game. Then the Sharpley value-based power allocation ensures a Pareto solution for both optimality and fairness. Furthermore, it is mathematically proven that the throughput consistently improves through iterative application of the allocation rule. Mathematical analysis and simulation results validate the effectiveness of the proposed method.

In this letter, the problem of detecting a multiple subspace-based target in the presence of deterministic interference is considered. To solve the problem, we utilize the Kullback-Leibler information criterion and model order selection rules to design detection schemes. The alternative hypothesis related to the most likely signal subspace is selected from multiple alternative hypotheses, and is tested versus the null hypothesis for target detection. Numerical examples verify the effectiveness of the proposed detection schemes, which can achieve the target detection and subspace-based target classification simultaneously.

Abstract For adaptive multichannel radar detection in the framework of multiple alternative hypotheses, where either the target or jamming could be present, a kind of two‐stage detector and classifier is proposed. Precisely, in the first stage, a decision is made on whether a target or a jamming exists. In the second stage, the decision is determined whether it is a target or a jamming. The detector is chosen as subspace‐based adaptive matched filter (SAMF) or adaptive energy detector (AED), while the classifier is selected as the subspace‐based adaptive beamformer orthogonal rejection test (SABORT), whitened SABORT (W‐SABORT), or orthogonal subspace‐based generalised likelihood ratio test (OSGLRT). Among these detectors and classifiers, the OSGLRT is proposed specially for classification in the scheme. Numerical experiments indicate that the proposed methods can achieve better detection and classification performance.

In this letter, a constant false alarm rate (CFAR) detector is proposed for skywave over-the-horizon radar (OTHR) under Weibull distribution. First, an objective function, composed of a data fidelity term and regularization terms, is formulated in the Bayesian framework. The low rank prior of the distribution parameter matrix, smoothness of the parameters along the Doppler/range dimension, and sparsity of the target in the range-Doppler (RD) matrix are used to regularize the objective function. Then, the distribution parameters of all cells under test (CUTs) in the RD matrix are estimated by optimizing the objective function, and the abnormal CUTs with targets are identified and separated. Finally, target detection is performed by calculating the detection threshold through the threshold coefficient and estimated distribution parameters. Experiments on OTHR real data demonstrate that the proposed detector has a good CFAR property and significantly improved probability of detection, especially in the case of multi-target and clutter edge.

It is often difficult to obtain sufficient training data for adaptive signal detection, which is required to calculate the unknown noise covariance matrix. Additionally, interference is frequently present, which complicates the detecting issue. We provide a two-step method, termed interference cancellation before detection (ICBD), to address the issue of signal detection in the unknown Gaussian noise and subspace interference. The first involves projecting the test and training data to the interference-orthogonal subspace in order to suppress the interference. Utilizing traditional adaptive detector design ideas is the next stage. Due to the smaller dimension of the projected data, the ICBD-based detectors can function with little training data. The ICBD has two additional benefits over traditional detectors. Lower computational burden and proper operation with interference being in the training data are two additional benefits of ICBD-based detectors over conventional ones. We also give the statistical properties of the ICBD-based detectors and demonstrate their equivalence with the traditional ones in the special case of a large amount of training data containing no interference.

This article deals with the problem of detecting maritime targets embedded in nonhomogeneous sea clutter, where the limited number of secondary data is available due to the heterogeneity of sea clutter. A class of linear discriminant analysis (LDA)-based matrix information geometry (MIG) detectors is proposed in the supervised scenario. As customary, Hermitian positive-definite (HPD) matrices are used to model the observational sample data, and the clutter covariance matrix of the received dataset is estimated as the geometric mean of the secondary HPD matrices. Given a set of training HPD matrices with class labels, which are elements of a higher dimensional HPD matrix manifold, the LDA manifold projection learns a mapping from the higher dimensional HPD matrix manifold to a lower dimensional one subject to maximum discrimination. In this study, the LDA manifold projection, with the cost function maximizing between-class distance while minimizing within-class distance, is formulated as an optimization problem in the Stiefel manifold. Four robust LDA-MIG detectors corresponding to different geometric measures are proposed. Numerical results based on both simulated radar clutter with interferences and real IPIX radar data show the advantage of the proposed LDA-MIG detectors against their counterparts without using LDA and the state-of-the-art maritime target detection methods in nonhomogeneous sea clutter.

In this letter, we consider the problem of detecting a signal in a kind of nonhomogeneity environment caused by random unknown interference. We propose an effective detector according to the detector design criterion of the two-step Durbin test or the two-step Wald test. The simulation results show that the proposed detector can provide slightly superior performance than that of existing detectors while maintaining approximately the same computational load, both under hypothetical interference conditions and other interference conditions.

The polarimetric whitening filter (PWF) is widely used in constant false alarm rate (CFAR) ship detection in polarimetric synthetic aperture radar (PolSAR) imagery. The detection threshold plays a key role in the CFAR detection, which is generally determined by the statistical model of clutter. Many product models with different distributed textures are considered to fit the PWF output for an accurate threshold. Unfortunately, the product model with the general inverse Gaussian (G) texture, which can be namedG-Wishart model according to the polarimetric covariance matrix, has not been well studied for its effective calculation. In this letter, the probability density function (PDF), the probability of false alarm (PFA), and the threshold in the CFAR algorithm based on the PWF are all obtained corresponding to theGdistribution. The closed forms for the PDF and PFA are obtained with the Fox H function and its multivariate version, respectively. The threshold is derived by the bisection method when the false alarm rate (FAR) is constant. Finally, experimental results using both simulated and real data demonstrate that the different statistical models with the same log-cumulants can achieve almost the same CFAR loss.

It is a compelling task to detect sea-surface small targets in the background of strong sea clutter. Traditional detection methods usually suffer from poor detection performance and a high probability of false alarm (PFA). In this letter, a PFA-controllable and feature-based detection method is proposed based on an enhanced convolutional neural network (CNN). The time-frequency features of received signals are first extracted by the short-time Fourier transform and converted into feature images. These feature images are then employed as inputs of an enhanced CNN with a PFA control unit. The enhanced CNN takes full advantage of the subtle feature extraction ability of the asymmetric convolution and robust time-frequency maps. Finally, detection results are obtained according to the given PFA. The results on the IPIX dataset show that the probability of detection of the proposed method is about 0.864 when the observation time is 1.024s and PFA is 10-3. Compared with five typical detection methods, the proposed method achieves better detection performance. Besides, results also verify the stable PFA control ability of the proposed method. The source code is available at https://github.com/quqizhe-whu/STDN.

In this letter, we consider the problem of target detection in unknown Gaussian noise for a colocated multi-input multi-output (MIMO) radar. To improve the detection performance, we adopt the training data, which were not utilized in existing references for the considered problem. We derive the generalized likelihood ratio test (GLRT) and Rao and Wald tests. Moreover, the corresponding analytical expressions for the probabilities of detection (PDs) and probabilities of false alarm (PFAs), which indicate that the proposed detectors have constant false alarm rate (CFAR) properties. Simulation results show that the proposed detectors can provide higher PDs than the existing detectors which do not utilize training data.

In this letter, we consider the problem of detecting a multichannel subspace signal in the presence of deterministic interference and structure nonhomogeneity. We derive the gradient test, Durbin test, and their two-step (2S) variants. The gradient test and its 2S variant have the same form as the existing generalized likelihood ratio test for the same detection problem, whereas the Durbin test and its 2S variant are new detectors. Numerical examples show that the two proposed new detectors, i.e., the Durbin test and its 2S variant, can provide better detection performance in some scenarios. In particular, they are robust to signal mismatch, and can perform well when the structure nonhomogeneity is not serious.

To solve the problem of adaptive detection in partially homogeneous environment with outliers and limited samples, a class of two-step detectors are designed based on geometric barycenters. The first step is to construct a data selector based on generalized inner product and eliminate sample data containing outliers. The second step is to construct detection statistics of the adaptive coherence estimator using covariance matrix estimators, which are based on geometric barycenters. The detectors utilize geometric barycenters of the positive definite matrix space without any knowledge of prior probability distribution of sample data. The performance of the proposed two-step detectors is evaluated in terms of the probabilities of correct outliers excision, false alarm, and detection. Experiment results, based on simulated and real data, show that the proposed approach has better detection performance than the existing ones based on traditional covariance estimator.

In this paper, we investigate the anomaly detection problem for multi-pixel targets in hyperspectral imagery when training data are available. We derive the generalized likelihood ratio test and obtain its analytical expressions of the probability of false alarm and probability of detection. The performance of the proposed detector is evaluated by using simulated and real data. The results demonstrate that this training data assisted detector outperforms its counterpart without training data.

This letter investigates the problem of distributed target detection with signal mismatch in homogeneous environment. Precisely, the actual signal steering vector is not aligned with the nominal one which is assumed by radar system. Within this framework, we propose a tunable detector which can adjust its directivity (robustness or selectivity) flexibly and effectively by changing a tunable parameter. Moreover, this tunable detector encompasses the generalized Kelly's generalized likelihood ratio test (GKGLRT) and the generalized adaptive matched filter (GAMF) as its two special cases. Finally, to test the effectiveness of this detector, the real data received by the IPIX radar are used for experiments. The results illustrate the superiority of the proposed detector both in simulation environment and realistic environment.

For the problem of detecting a subspace signal in the presence of signal mismatch, we propose a two-stage detector, referred to as the AESD. The AESD is constructed by cascading two detectors, namely, the adaptive energy detector (AED) and the adaptive subspace detector (ASD). The AESD offers great flexibility in controlling the detection performance for mismatched subspace signals, since the AED is very robust, while the ASD is very selective. We derive the analytical expressions for the probabilities of detection and false alarm, which are verified by Monte Carlo simulations.

In this letter, we consider the problem of direction detection in deterministic interference and partially homogeneous noise. The target echoes, reflected by a distributed target, all come from the same direction. However, the signal steering vector is only known to lie in a subspace of dimension greater than one. The interference belongs to a subspace linearly independent of the signal subspace. We propose two effective detectors according to the two-step design criteria of the generalized likelihood ratio test and the Wald test. It is shown that the proposed detectors possess the constant false alarm rate property and have better detection performance than their counterparts.

The problem of detecting a subspace signal is studied in colored Gaussian noise with an unknown covariance matrix. In the subspace model, the target signal belongs to a known subspace, but with unknown coordinates. We propose a modified Rao test (MRT) by introducing a tunable parameter. The MRT is more general, which includes the Rao test and the generalized likelihood ratio test as special cases. Moreover, closed-form expressions for the probabilities of false alarm and detection of the MRT are derived. Numerical results demonstrate that the MRT can offer the flexibility of being adjustable in the mismatched case where the target signal deviates from the presumed signal subspace. In particular, the MRT provides better mismatch rejection capacities as the tunable parameter increases.

This letter addresses the problem of high-resolution radar detection in interference and nonhomogeneous noise. The target signal and interference lie in two linearly independent known subspaces, but with unknown coordinate. The noise is modeled by a compound-Gaussian process with unknown covariance matrix and random texture. According to the two-step generalized likelihood ratio test-based design approach, we derive a distributed target detector. Numerical examples show that the proposed detector can provide better detection performance than their counterparts in nonhomogeneous environments.

Besides noise and potential targets, there usually exists jamming, which can significantly degrade detection performance of a detector. In this letter, we analyze the statistical performance of the adaptive orthogonal rejection detector (AORD), recently proposed for the case of completely unknown jamming. We derive closed-form expressions for the probabilities of detection and false alarm and show how the jamming affects the detection performance. The theoretical results are verified by Monte Carlo (MC) simulations.

Our aim in this work is to analyze the detection performance of a constant false alarm rata matched filter (CFAR-MF) which was developed for the detection problem in white Gaussian noise with unknown noise power. An exact expression for the detection probability of the CFAR-MF is derived in the mismatched case where mismatch exists between the actual signal steering vector and the nominal one. This theoretical expression can be used to facilitate the performance evaluation of the CFAR-MF in real-world scenarios when signal mismatch cannot be neglected.

In this letter, we consider the problem of detecting a distributed target with unknown signal steering in the Gaussian noise. We derive the Rao and Wald tests. It is found that the Rao test coincides with the so-called modified two-step generalized likelihood ratio test (M2S-GLRT), while the Wald test is equivalent to the plain two-step GLRT (2S-GLRT). We also give some intuitive interpretations about the Rao and Wald tests, as well as other existing detectors.