Adaptive Radar Detection Research Articles

Adaptive radar target detection in nonzero-mean compound Gaussian sea clutter with random texture

This paper deals with the radar target detecting problem in nonzero-mean compound Gaussian sea clutter with random texture. The texture is considered to be an inverse Gamma, Gamma, or inverse Gaussian variable. Three novel adaptive detectors using the two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) are proposed. More precisely, we derive the test statistics of the proposed detectors for known mean vector (MV) and speckle covariance matrix (CM) in the first step. In the second step, unbiased and consistent estimators are proposed to estimate the MV and CM in nonzero-mean compound Gaussian circumstances. We acquire the fully adaptive nonzero-mean GLRT detectors by substituting the estimates into the test statistics. Then, the constant false alarm rate (CFAR) properties of the proposed detectors with respect to (w.r.t.) the speckle CM are proved. Finally, the performance of three proposed detectors is verified by simulation experiments using the synthetic and real sea clutter data.

Data‐driven target localization using adaptive radar processing and convolutional neural networks

AbstractLeveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data‐driven approach to improve accuracy in radar target localization post adaptive radar detection. To this end, we generate a large number of radar returns by randomly placing targets of variable strengths in a predefined area, using RFView®, a high‐fidelity, site‐specific, RF modeling & simulation tool. We produce heatmap tensors from the radar returns, in range, azimuth [and Doppler], of the normalized adaptive matched filter (NAMF) test statistic. We then train a regression convolutional neural network (CNN) to estimate target locations from these heatmap tensors, and we compare the target localization accuracy of this approach with that of peak‐finding and local search methods. This empirical study shows that our regression CNN achieves a considerable improvement in target location estimation accuracy. The regression CNN offers significant gains and reasonable accuracy even at signal‐to‐clutter‐plus‐noise ratio (SCNR) regimes that are close to the breakdown threshold SCNR of the NAMF. We also study the robustness of our trained CNN to mismatches in the radar data, where the CNN is tested on heatmap tensors collected from areas that it was not trained on. We show that our CNN can be made robust to mismatches in the radar data through few‐shot learning, using a relatively small number of new training samples.

Adaptive radar pulse detection design based on difference of box filter

AbstractPrecise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low‐complexity and environment‐adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed‐form expression of the detection probability is derived. Simulations and on‐board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal‐to‐noise ratio ≥3 dB and false alarm probability = 10−8. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals.

The knowledge-aided generalized multipath adaptive detector

In urban environments, the challenges brought about by multipath propagation are significant for traditional adaptive radar detection problems. To tackle this issue, a generalized multipath signal model combined with environmental geometric prior information (EGPI) is developed. This model can be utilized regardless of whether the multipath signals are fully, partially, or partially-resolvable in the range domain. During the process of designing a detector using the criteria of the one-step generalized likelihood ratio test (GLRT), an attempt to find a closed-form expression for the maximum likelihood estimator of the unknown parameters was unsuccessful. However, by utilizing an adaptive configuration for the initial value, a cyclic optimization algorithm was able to obtain the estimated value within just a few iterations, demonstrating a rapid convergence rate. Furthermore, a detector based on the two-step GLRT criteria is proposed. Compared to traditional detectors, the newly developed detectors make more efficient use of the energy in multipath signals, resulting in improved detection performance. It has been demonstrated that when the whitened angles between subspaces are set to two specific values, the GLRT detectors exhibit the Constant False Alarm Probability (CFAR) property. Simulation results show that the false alarm probability(Pfa) at these two specific angles provides upper and lower bounds of Pfa.

A Metacognitive Approach to Adaptive Radar Detection

A Metacognitive Approach to Adaptive Radar Detection

Adaptive radar detection of subspace-based distributed target in power heterogeneous clutter

Adaptive radar detection of subspace-based distributed target in power heterogeneous clutter

Joint ML-Bayesian Approach to Adaptive Radar Detection in the presence of Gaussian Interference

Joint ML-Bayesian Approach to Adaptive Radar Detection in the presence of Gaussian Interference

Covariance Matrix Estimation of Texture Correlated Compound-Gaussian Vectors for Adaptive Radar Detection

Covariance matrix estimation of compound-Gaussian vectors with texture-correlation (spatial correlation for the adaptive radar detectors) is examined. The texture parameters are treated as hidden random parameters whose statistical description is given by a Markov chain. States of the chain represent the value of texture coefficient and the transition probabilities establish the correlation in the texture sequence. An Expectation-Maximization (EM) method based covariance matrix estimation solution is given for both noiseless and noisy snapshots. An extension to the practically important case of persymmetric covariance matrices is developed and possible extensions to other structured covariance matrices are described. The numerical results indicate that the benefit of utilizing spatial correlation in the covariance matrix estimation can be significant especially when the total number of snapshots in the secondary data is small. From applications viewpoint, the suggested model is well suited for the adaptive target detection in sea-clutter where some spatial correlation between range cells has been experimentally observed. The performance improvements of the suggested approach for small number of snapshots can be particularly important in this application area.

Adaptive Target Detection With Polarimetric FDA-MIMO Radar

The problem of adaptive radar detection with a polarimetric Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar is addressed in this paper. At the design stage, the target detection problem is formulated as a composite hypothesis test, with the unknowns given by the target angle, incremental range (target displacement with respect to the center of the occupied range cell), and scattering matrix, as well as the interference covariance matrix. The formulated detection problem is handled by resorting to sub-optimal design strategies based on the Generalized Likelihood Ratio (GLR) criterion. The resulting detectors demand, under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_\mathrm{{1}}$</tex-math></inline-formula> hypothesis, the solution of a box-constrained optimization problem for which several iterative techniques, i.e., the Linearized Array Manifold (LAM), the Gradient Projection Method (GPM), and the Coordinate Descent (CD) algorithms, are exploited. At the analysis stage, the performance of the proposed architectures, which ensure the bounded CFAR property, is evaluated via Monte Carlo simulations and compared with the benchmarks in both white and colored disturbance.

Adaptive Radar Detection and Bearing Estimation in the Presence of Unknown Mutual Coupling

This paper deals with joint adaptive radar detection and target bearing estimation in the presence of mutual coupling among the array elements. First of all, a suitable model of the signal received by the multichannel radar is developed via a linearization procedure of the Uniform Linear Array (ULA) manifold around the nominal array looking direction together with the use of symmetric Toeplitz structured matrices to represent the mutual coupling effects. Hence, the Generalized Likelihood Ratio Test (GLRT) detector is evaluated under the assumption of homogeneous radar environment. Its computation leverages a specific Minorization-Maximization (MM) framework, with proven convergence properties, to optimize the concentrated likelihood function under the target presence hypothesis. Besides, when the number of active mutual coupling coefficients is unknown, a Multifamily Likelihood Ratio Test (MFLRT) approach is invoked. During the analysis phase, the performance of the new detectors is compared with benchmarks as well as with counterparts available in the open literature which neglect the mutual coupling phenomenon. The results indicate that it is necessary to consider judiciously the coupling effect since the design phase, to guarantee performance levels close to the benchmark.

Adaptive Radar Detection in the Clutter and Noise Cover Pulse Jamming Environment

This paper deals with the adaptive detection of the point-like target under clutter and noise cover pulse (NCP) jamming. In the design stage, we assume that the jamming is orthogonal to the signal subspace. Simultaneously, two training datasets are considered available. According to generalized likelihood ratio test (GLRT), Wald, and Rao criteria, NCP-GLRT, NCP-Wald, and NCP-Rao detectors are designed respectively. Then, considering the degraded detection performance of the NCP-Rao, a modified detector called NCP-MRao is proposed. Moreover, the performance of the newly presented detectors is analyzed, and the probability of false alarm and the detection probability for the NCP-GLRT are derived. Finally, numerical simulation results show that compared with the analogous counterparts, the proposed detector can improve detection performance.

Adaptive Radar Detection in the Presence of Missing-data

This article deals with the problem of adaptive radar detection in a missing-data context, where the complete observations (i.e., downstream information loss mechanisms) are characterized by homogeneous Gaussian disturbance with an unknown but possibly structured covariance matrix. The detection problem, formulated as a composite hypothesis test, is tackled by resorting to suboptimal design strategies, leveraging the generalized likelihood ratio criterion demanding appropriate maximum likelihood estimates (MLEs) of the unknowns under both hypotheses. Capitalizing on some possible <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge about the interference covariance matrix structure, the optimization problems involved in the MLE computation are handled by employing the expectation–maximization (EM) algorithm or its expectation–conditional maximization and multicycle EM variants. At the analysis stage, the performance of the devised architectures is assessed both via Monte Carlo simulations and on measured data for some covariance matrix structures of practical interest.

Detection architecture with improved classification capabilities for covariance structures

This paper proposes a radar adaptive detection architecture composed of an interference covariance matrix structure classifier before a bank of adaptive radar detectors. The former relies on the model order selection framework. This classifer accounts for six interference covariance matrix structure classes, including the additional Toeplitz structures. The proposed architecture exhibits an improved estimation of covariance matrix, and better detection performance with an improved classifer, especially in the presence of a small volume of training samples. As a consequence, the newly proposed architecture can guarantee excellent detection performance for a wider class of operating scenarios. Numerical examples show that the proposed architecture exhibits improved detection performance with respect to its competitor when the interference covariance matrix exhibits the Toeplitz structure.

Design of Customized Adaptive Radar Detectors in the CFAR Feature Plane

The paper addresses the design of adaptive radar detectors having desired behavior, in Gaussian disturbance with unknown statistics. Specifically, given detection probability specifications for chosen signal-to-noise ratios and steering vector mismatch levels, a methodology for the optimal design of customized CFAR detectors is devised in a suitable feature plane based on maximal invariant statistics. To overcome the analytical and numerical intractability of the resulting optimization problem, a novel general reduced-complexity algorithm is developed, which is shown to be effective in providing a close approximation of the desired detector. The proposed approach solves the open problem of ensuring a prefixed false alarm probability while controlling the behavior under both matched and mismatched conditions, so enabling the design of fully customized adaptive CFAR detectors.

Adaptive Radar Detection in Gaussian Interference Using Clutter-Free Training Data

This paper addresses adaptive detection of range spread targets in the presence of thermal noise, jammer, and clutter. After motivating the study, a set of clutter-free training (CFT) data is considered to assist radar detection in absence of conventional secondary data sharing the same spectral properties as the interference of the cells under test. To this end, a maximum likelihood (ML) estimate of the unknown parameters is derived under the alternative hypothesis by leveraging the primary data and the CFT data simultaneously. Subsequently, the ML estimate is used to design decision rules based on generalized likelihood ratio, complex parameter Wald, and complex parameter Gradient test criteria. Furthermore, conditions guaranteeing the constant false alarm rate (CFAR) property of the proposed detectors are discussed. At the analysis stage, numerical examples are presented to evaluate the effectiveness of the proposed detectors in comparison with other detection schemes available in the literature.

An Overview of Parametric Modeling and Methods for Radar Target Detection With Limited Data

This article provides a survey of recent results on exploiting parametric auto-regressive (AR) models for adaptive radar target detection. Specifically, three types of radar systems are considered, including phased-array radar with multiple co-located transmitters and receivers, distributed multi-input multi-output (MIMO) radar with widely and spatially separated transmitters and receivers, and passive radar which uses existing sources as illuminators of opportunity (IOs). These radar systems are of significant interest for a wide range of military and civilian applications. For each of the three types of radars, we discuss how AR processes can be employed to succinctly model the underlying signal correlation and efficiently estimate it from limited data, thus enabling effective target detection in complex non-homogeneous environments when training data is limited. We illustrate the performance of such parametric model assisted detectors relative to conventional non-parametric approaches by using computer simulated and experimental data.

Adaptive Radar Detection in the Presence of Multiple Alternative Hypotheses Using Kullback-Leibler Information Criterion-Part II: Applications

This paper deals with adaptive radar detection problems where several alternative hypotheses may be plausible. This kind of problems naturally extends the conventional binary tests that often occur in radar (as well as in other application fields) by including a further uncertainty degree related to the number of unknown signal parameters (model order). Such a modification consequently leads to multiple composite alternative hypotheses. In the companion paper (Addabbo et al., 2021), we have defined a new design framework which allows us to come up with decision schemes for these hypothesis testing problems by exploiting the Kullback-Leibler Information Criterion and without resorting to heuristic design criteria. The architectures devised within the proposed framework consist of the sum between the compressed log-likelihood ratio and a penalty term inherited from model order selection rules. Such a penalty term accounts for the number of unknown parameters to overcome the limitation of the generalized likelihood ratio test in the presence of nested hypotheses. In the present paper, we apply the new design framework to different detection problems related to both real aperture and (polarimetric) synthetic aperture radar. The analysis is carried out in comparison with suitable competitors (possibly based upon heuristic design criteria) and shows that the architectures devised within the proposed theoretically-founded design framework represent an effective means to deal with detection problems where the uncertainty on some parameters leads to multiple alternative hypotheses.

Adaptive Radar Detection and Classification Algorithms for Multiple Coherent Signals

In this paper, we address the problem of target detection in the presence of coherent (or fully correlated) signals, which can be due to multipath propagation effects or electronic attacks by smart jammers. To this end, we formulate the problem at hand as a multiple-hypothesis test that, besides the conventional radar alternative hypothesis, contains additional hypotheses accounting for the presence of an unknown number of interfering signals. In this context and leveraging the classification capabilities of the Model Order Selection rules, we devise penalized likelihood-ratio-based detection architectures that can establish, as a byproduct, which hypothesis is in force. Moreover, we propose a suboptimum procedure to estimate the angles of arrival of multiple coherent signals ensuring (at least for the considered parameters) almost the same performance as the exhaustive search. Finally, the performance assessment, conducted over simulated data and in comparison with conventional radar detectors, highlights that the proposed architectures can provide satisfactory performance in terms of probability of detection and correct classification.

Adaptive Detection of a Subspace Signal in Structured Random Interference Plus Thermal Noise

This paper studies adaptive radar detection of a subspace signal embedded in two disturbance sources. The former is thermal noise with known power. The latter is a Gaussian subspace interference with zero mean and unknown covariance matrix (CM). It is assumed that the signal and interference subspaces are known and partially related. As customary, several secondary data containing only interference and thermal noise are used to estimate this interference CM. This paper derives the generalized likelihood ratio test (GLRT), and theoretically deduces the probabilities of false alarm (PFA) and detection of the new detector. This PFA shows that the new detector has the constant false alarm rate (CFAR) property against the interference CM. Several numerical experiments are performed to evaluate the detection performance of the new detector. The results show that the performance of the new detector is better than the natural counterparts in some scenarios.

Diffuse Multipath Exploitation for Adaptive Detection of Range Distributed Targets

This paper studies adaptive radar detection of range distributed targets in the presence of Gaussian interference and possible diffuse multipath returns modeled as independent zero-mean complex circular Gaussian random vectors with unknown covariance matrices. For this problem, an adaptive constrained generalized likelihood ratio (ACGLR) test is devised, where in each range cell of the primary data the covariance matrix (due to both multipath and disturbance echoes) is forced to belong to a neighborhood of the secondary data sample covariance. The size of the uncertainty set is determined adaptively employing jointly a union-intersection test and an expectation likelihood (EL)-based estimate. Besides, an adaptive detector based on the complex parameter Rao test criterion is derived. Remarkably, both the two new architectures possess the desired constant false alarm rate (CFAR) property with respect to the disturbance covariance. Finally, their detection performance is assessed and validated via numerical examples.