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This paper focuses on the method of an adaptive detector which works against Gaussian background with unknown parameters, such as covariance matrix. The authors design a tunable detector for point‐like targets by mixing the Kelly's generalized likelihood ratio test and the enhanced Rao test. The analytical expression of the false alarm probability and detection probability of the proposed detector are derived. The performances of the new scheme are assessed, and analysed in comparison with its natural counterparts. The results show that it can provide enhanced rejection capabilities of mismatched signals, at the price of a limited detection loss for matched signals.

This letter focuses on adaptive target detection in the presence of multiple interference sources, which comprise clutter, thermal noise, noise-like jammers, and fully-correlated (or coherent) signals. In order to account for different operating scenarios, we formulate the problem at hand in terms of a multiple hypothesis test with several alternative hypotheses representative of each considered scenario. In this context, we devise a family of two-stage detection architectures capable of classifying the specific scenario and, hence, of working under different operating conditions. The performance analysis shows the effectiveness of the detector based upon the Generalized Information Criterion also in comparison with traditional adaptive decision schemes.

This letter addresses the detection problem of dim maneuvering targets in the presence of range cell migration. Specifically, it is assumed that the moving target can appear in more than one range cell within the transmitted pulse train. Then, the Bayesian information criterion and the generalized likelihood ratio test design procedure are jointly exploited to come up with six adaptive decision schemes capable of estimating the range indices related to the target migration. The computational complexity of the proposed detectors is also studied and suitably reduced. Simulation results show the effectiveness of the newly proposed solutions also for a limited set of training data and in comparison with suitable counterparts., 5 pages

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., Comment: 13 pages

In this article, a target detection problem in homogeneous Gaussian noise with unknown covariance matrix is examined using multiple observations, which may be collected from multiple range cells, bands, and/or coherent processing intervals. In order to take into consideration mismatches of the target steering vector, we adopt a subspace model where the target steering vector is assumed to lie in a subspace spanned by the column vectors of a known matrix with unknown target coordinates. By exploiting persymmetric structures, we propose several adaptive detectors according to ad hoc modifications of generalized likelihood ratio test (GLRT), Rao test, and Wald test. It is found that the Rao test does not exist, whereas the two-step Wald test shares the same form as the two-step GLRT. Numerical examples show that the robustness of the proposed detectors is better than that of their counterparts in general. In particular, the proposed one-step GLRT is the most robust in most cases, and the proposed two-step GLRT and one-step Wald test can be more robust than the one-step GLRT in the case where the number of training data is small.

In this paper, we deal with the problem of detecting point-like targets in the presence of Gaussian disturbance with unknown covariance matrix. In particular, we consider the so-called partially homogeneous environment, where the disturbances in both the cell under test (CUT) and the secondary data share the same covariance matrix up to an unknown power scaling factor. Specifically, we model the disturbance as a multichannel autoregressive Gaussian process and exploit the spillover of target energy to consecutive range samples, in order to improve the performances of detection and range estimation. In this context, we come up with three adaptive architectures relying on the ad hoc modifications of the generalized likelihood ratio test and Wald test. The performance assessment, conducted resorting to both simulated data and recorded live data, highlights that the proposed decision schemes can provide accurate estimates of the target position within the CUT and ensure enhanced detection performance compared with their natural competitors.

This paper investigates the statistical performance of three adaptive detectors in the presence of subspace interference and Gaussian noise. The interference is deterministic and lies in a known subspace but with unknown coordinates, while the noise is Gaussian distributed with unknown covariance matrix. For performance evaluation, we consider a more general case, namely, the case of subspace signal mismatch, where the actual signal does not completely lie in the presumed signal subspace. We derive the exact statistical distributions of the detectors, and then obtain analytical expressions for probabilities of detection and false alarm. The theoretical study reveals that the interference and signal mismatch affects the detection performance through two angles, which are the angle between the interference subspace and actual signal, and the angle between the actual signal and presumed signal subspace after they are both projected onto the interference-orthogonalized subspace. Numerical examples are provided to verify the theoretical results.

In this letter, we address adaptive radar detection of point-like targets in Gaussian clutter with an unknown covariance matrix. To this end, we first exploit the symmetrically structured power spectral density of the clutter to transfer data from the complex to the real domain. Then, the spillover of target energy is incorporated into the design criteria to come up with two architectures capable of guaranteeing improved detection performances and range estimation. The performance assessments, conducted on both simulated data and real recorded datasets, demonstrate the effectiveness of the newly proposed detectors compared with the state-of-the-art counterparts, which ignore either the clutter spectral symmetry or the energy spillover.

In this paper, we address the problem of detecting a point-like target embedded in clutter characterized by a symmetrically structured power spectral density and persymmetric covariance matrix. In particular, we consider the so-called partially homogeneous environment, where the cell under test and the training samples share the same covariance matrix up to an unknown power scaling factor. At the design stage, we jointly exploit the spectral properties of the clutter and the persymmetric structure of the clutter covariance matrix to reformulate the decision problem in terms of real variables with an increased number of training samples. Then, we derive two adaptive detectors relying on the Rao test and a suitable modification of the generalized likelihood ratio test (GLRT). The performance analysis, conducted on both simulated and real radar data, confirms the superiority of the newly proposed receivers over the traditional state-of-the-art counterparts which ignore either the persymmetry or the symmetric spectrum.

In this paper, we propose an intelligent constant false alarm rate detector, which uses support vector machine (SVM) techniques to improve the radar detection performance in different background environments. The proposed detector uses the variability index statistic as a feature to train a SVM and recognizes the current operational environment based on the classification results. The proposed detector has the intelligence to select the proper detector threshold adaptive to the current operational environment. This detector provides a low loss performance in homogeneous backgrounds and also performs robustly in nonhomogeneous environments including multiple targets and clutter edges.

We exploit persymmetry to study the adaptive detection problem with multiple observations in partially homogeneous environments where noise shares the same covariance matrix up to different power levels between the test and training data. A persymmetric subspace model is designed for taking into account steering vector mismatches. Based on the persymmetric subspace model, we propose a generalized likelihood ratio test in partially homogeneous environments. The proposed detector is proved to exhibit a constant false alarm rate property against both the covariance matrix structure and the scaling factor. Numerical examples show that the proposed detector, compared to its counterparts, is more robust to steering vector mismatches.

Space-time adaptive detection (STAD) is an important research aspect of the bistatic multiple-input multiple-output (MIMO) radar system. The signal and clutter space-time models of the bistatic MIMO radar are described in this study. The knowledge-aided (KA) transform, which utilises the structural properties of the target-free data and the sample covariance matrix, is applied to increase the independent secondary data fourfold in the STAD frame. The performance assessment of the KA–STAD detectors with generalised likelihood ratio test (GLRT), adaptive matched filter (AMF), and Rao rules is presented. The simulation experiments show that the detection and constant false alarm rate performances of the presented KA–GLRT, KA–AMF, and KA–Rao detectors outperform the GLRT, AMF, and Rao detectors for the bistatic MIMO radar system STAD frame in clutter background with limited secondary data.

In this paper, we deal with the problem of detecting a point-like target in a homogeneous interference environment characterized by the presence of clutter, noise-like jamming, and radar internal noise. To this end, we assume that two sets of training data which contain different interference components are available. Within this context, we propose a two-step estimation procedure to provide an accurate estimate of the interference covariance matrix. The latter is then used to construct an adaptive detector resorting to the two-step modification of the generalized likelihood ratio test. Finally, a preliminary performance assessment demonstrates the effectiveness of the proposed method achieving better performance with respect to the other state-of-the-art detector in the case of sample starved scenarios.

We exploit persymmetry to study the adaptive detection problem with multiple observations in partially homogeneous environments where noise shares the same covariance matrix up to different power levels between the test and training data. A persymmetric subspace model is designed for taking into account steering vector mismatches. Based on the persymmetric subspace model, we propose adaptive detectors in partially homogeneous environments, according to the criteria of two-step generalized likelihood ratio test (GLRT), Wald test, and Rao test. It is found that the proposed GLRT and Wald test coincide, while the Rao test does not exist. The proposed detector is proved to exhibit a constant false alarm rate property against both the covariance matrix structure and the scaling factor. Numerical examples show that the proposed detector, compared to its counterparts, is more robust to steering vector mismatches.

In this paper, we deal with the problem of polarimetric diversity detection for point-like targets in the presence of Gaussian clutter with unknown covariance matrix. To this end, we jointly exploit the polarization diversity and the spillover of target energy to consecutive range samples to improve the performances of detection and range localization. For estimation purposes, we assume that a set of secondary data (free of signal components) is available with the same covariance matrix as the clutter in the cells under test. Because the uniformly most powerful test does not exist for this problem, we derive two adaptive detectors: the generalized likelihood ratio test and the Wald test. Interestingly, these new receivers ensure the constant false alarm rate property with respect to covariance matrix of the clutter. The performance assessments conducted on both simulated data and real recorded dataset reveal that the proposed detectors outperform, in both detection and localization, the traditional state-of-the-art counterparts that ignore either the polarimetry or the spillover.

This paper deals with the distributed Constant false alarm rate (CFAR) detection system in heavy tailed reverberation modeled by Pearson distribution. A kind of distributed fuzzy greatest of (FGO) CFAR detection scheme is studied here. The detection system consists of multi-sensors and a fusion center. Each sensor computes the value of membership function, and send it to the fusion center. Fusion center will combine the values to produce a global value according to fusion rules, and compare it to a threshold to decide whether a target is present or not. We consider four fusion rules, max, min, algebraic sum and algebraic product. From the Monte-Carlo simulations, the results indicate that the detection performance of distributed FGO-CFAR detectors based on max fusion rule is more robust in homogeneous environment than that detectors based on the other rules. And the probability of detection increases with the number of detectors.

In this paper we present an algorithm combined underwater space-time adaptive processing (STAP) model with dynamic programming based track-before-detect (DP-TBD) strategy to solve weak target detection problem for the moving active sonar. As a preliminary step the STAP model for underwater environment is introduced to coherently suppress the reverberation data caused by platform motion and to improve the signal-to-reverberation ratio (SRR). Then, resorting to GLRT and ad hoc procedures, we derive adaptive constant false-alarm rate (CFAR) detectors for the homogeneous underwater environment. Finally, we use the DP-TBD strategy to detect the presence of target by taking the test statistics as the scoring function of strategy. Simulation results have demonstrated that the proposed STAP-DP-TBD method has a better detection performance compared to the traditional reverberation - suppressing method.

In this paper, the performances of two modified censored mean-level (CM) Constant False Alarm Rate (CFAR) detectors in Pearson-distributed (PD) reverberation are studied. Both of the detectors generate two local estimations with CM and cell averaging (CA) methods according to the leading and lagging reference windows respectively, and take the greater value (CMCAGO) or smaller value (CMCASO) of the two local estimations as the reverberation level estimation. We derive the analytical expressions of the two detectors' false alarm probabilities and detection probabilities in homogeneous PD reverberation, and prove that both of the detectors have the CFAR property with respect to the reverberation level. In strong multi-target interference environment, we analyze and derive the detection probability expressions of both of the detectors. Through simulations, the performances of the two detectors are comprehensively analyzed and compared with traditional CFAR detectors like the OS, SO and GO. The results show that the detection performance of the CMCASO is close to that of the OS, and the CMCAGO has a very stable detection performance in multi-target interference environment.

In the present letter we investigate the problem of adaptive detection and range estimation for point-like targets buried in partially homogeneous Gaussian disturbance with unknown covariance matrix. To this end, we jointly exploit the spillover of target energy to consecutive range samples and the oversampling of the received signal. In this context, we design a detector relying on the Generalized Likelihood Ratio Test (GLRT). Remarkably, the new decision scheme ensures the Constant False Alarm Rate (CFAR) property with respect to the unknown disturbance parameters. The performance analysis reveals that it can provide enhanced detection performance compared with its state-of-art counterpart while retaining accurate estimation capabilities of the target position.

In this paper we deal with the problem of detecting a multi-channel signal of range-spread target in the presence of Gaussian disturbance with an unknown covariance matrix. In particular, we consider the so-called partially homogeneous environment, where the disturbances in both the cells under test (primary data) and the training samples (secondary data) share the same covariance matrix up to an unknown power scaling factor. To this end, we first model the disturbance as a multichannel autoregressive (AR) process, and then develop an adaptive detector resorting to the Rao test. Remarkably, the proposed detector attains asymptotically a constant false alarm rate (CFAR) independent of the disturbance covariance matrix as well as the power scaling factor. The performance assessment conducted by Monte Carlo simulation highlights that the new receiver significantly outperforms their traditional covariance matrix-based counterparts both in AR and non-AR modeled disturbance backgrounds. Meanwhile, it requires less secondary data and is computationally more efficient. HighlightsThe proposed detector requires less training data, and is more computational efficient.The proposed detector is scaling invariant to the power scaling factor.The proposed detector has an asymptotic CFAR property with respect to the disturbance structure.

A new constant false-alarm rate (CFAR) detector for non-Rayleigh data, based on fuzzy statistical normalization, is proposed. The proposed detector carries out the detection with two stages. The first stage of the fuzzy statistical normalization CFAR processor is background level estimation, based on fuzzy statistical normalization. The second stage is signal detection, based on the original data and the defuzzification normalized data. Performance comparisons are carried out to validate the superiority of the proposed CFAR detector.

In this study, the authors deal with the problem of adaptive detection of point-like targets in Gaussian disturbance with unknown but persymmetric structured covariance matrix induced by the space and/or time symmetry of the sensing system. In this framework, they devise and assess two selective receivers exploiting the Rao test and the generalised likelihood ratio test design criteria. The performance assessment, conducted by Monte Carlo simulation, has shown that the proposed receivers can significantly outperform their unstructured counterparts and guarantee enhanced rejection performance of unwanted signals with respect to their natural competitors.

In this work, we devise an adaptive decision scheme with range estimation capabilities for point-like targets in partially homogeneous environments. To this end, we exploit the spillover of target energy to consecutive range samples and synthesize the Generalized Likelihood Ratio Test. The performance analysis, conducted resorting to both simulated data and real recorded datasets, highlights that the newly proposed architecture can guarantee superior detection performance with respect to its competitors while retaining accurate estimation capabilities of the target position.

In this paper we deal with the problem of detecting distributed targets in the presence of Gaussian noise with unknown but persymmetric structured covariance matrix. In particular, we consider the so-called partially-homogeneous environment, where the cells under test (primary data) and the training samples (secondary data), which are free of signal components, share the same structure of the interference covariance matrix but different power levels. Under the above assumptions, we derive the generalized likelihood ratio test (GLRT) and the so-called two-step GLRT. Remarkably, the new receivers ensure the constant false alarm rate property with respect to both the structure of the covariance matrix as well as the power level. The performance assessment, conducted by resorting to both simulated data and real recorded dataset, highlights that the proposed detectors can significantly outperform their unstructured counterparts, especially in a severely heterogeneous scenario where a very small number of secondary data is available.

In this paper, we deal with the problem of detecting the signal of interest in the presence of Gaussian disturbance with symmetric spectrum and assuming that the cell under test (primary data) and the training samples (secondary data) share the same covariance matrix up to an unknown power scaling factor. Moreover, we exploit the symmetric spectral property of the disturbance to transfer the binary hypothesis testing problem from the complex to the real domain and derive an adaptive detector relying on the two-step Generalized Likelihood Ratio Test design procedure. A preliminary performance assessment, conducted by Monte Carlo simulation, has confirmed the effectiveness of the newly proposed detector compared with the traditional state-of-the-art counterpart which ignores the spectrum symmetry.

In this paper, we address the problem of detecting the signal of interest in the presence of Gaussian clutter with symmetric spectrum. To this end, we exploit the spectral properties of the clutter to transfer the binary hypothesis test problem from complex domain to real domain. Then, we devise and assess a detection strategy based on the so-called two-step Generalized Likelihood Ratio Test (GLRT) design procedure. Finally, a preliminary performance assessment, conducted by resorting to simulated data, has confirmed the effectiveness of the newly proposed detector compared with the traditional state-of-the-art counterparts which ignore the spectrum symmetry.

This chapter has provided a survey on the two-stage detection of point-like targets embedded in homogeneous Gaussian disturbance. The family of two-stage detectors belongs to the more general class of tunable receivers, which allow to modify their directivity tuning proper parameters. They are obtained cascading two detectors with opposite behaviors in terms of directivity. The presence of a signal is declared if and only if each stage is above the respective threshold.

This letter deals with the problem of adaptive detection in partially-homogeneous Gaussian disturbance with unknown but persymmetric structured covariance matrix. Since no uniformly most powerful test exists for the problem at hand, we devise and assess two detection strategies based on the Rao test and the Wald test design criteria. Remarkably, both detectors ensure the constant false alarm rate property with respect to both the structure of the covariance matrix as well as the power level. The preliminary performance assessment, conducted by resorting to simulated data, has confirmed the effectiveness of the newly proposed detectors.

This study deals with the problem of detecting distributed targets in the presence of homogeneous and partially homogeneous Gaussian disturbance with unknown covariance matrix. The proposed detectors improve the adaptive beamformer orthogonal rejection test (ABORT) idea to address detection of distributed targets, which makes it possible to decide whether some observations contain a useful target or a signal belonging to the orthogonal complement of the useful subspace. At the design stage, the authors resort to either the plain generalised likelihood ratio test (GLRT) or ad hoc design procedures. Remarkably, the considered criteria lead to receivers ensuring the constant false alarm rate (CFAR) property with respect to the unknown quantities. Moreover, authors’ derivations show that the ad hoc detector for a partially homogeneous environment coincides with the generalised adaptive subspace detector. The performance assessment conducted by Monte Carlo simulation has confirmed the effectiveness of the newly proposed detection algorithms.

This paper deals with the problem of detecting distributed targets in the presence of partially homogeneous Gaussian disturbance with unknown covariance matrix. Since no uniformly most powerful test exists for the problem at hand, we devise and assess two detection strategies based on the Rao test, and the Wald test respectively. Remarkably both tests ensure the constant false alarm rate (CFAR) property with respect to both the structure of the covariance matrix as well as the power level. A preliminary performance assessment, conducted by resorting to simulated data, also in comparison to previously proposed detectors, has confirmed the effectiveness of the newly proposed detection algorithms.

This paper addresses the problem of detecting multiple point-like targets in the presence of steering vector mismatches and Gaussian disturbance with unknown covariance matrix. To this end, we flrst model the actual useful signal as a vector belonging to a proper cone whose axis coincides with the whitened direction of the nominal array response. Then we develop two robust adaptive detectors resort- ing to the two-step GLRT-based design procedure without assignment of a distinct set of secondary data. The performance assessment has been conducted by Monte Carlo simulation, also in comparison to pre- viously proposed detectors, and conflrms the efiectiveness of the newly proposed ones. In the last part of the work, in order to restore the detection performance of the newly proposed detectors in the presence of a large number of range cells contaminated by useful signals, we consider two adaptive detectors which resort to the structure informa- tion of the disturbance covariance matrix, and show that the a-priori information on the covariance structure can lead to a noticeable per- formance improvement.

This paper deals with the problem of adaptive signal detection in the presence of Gaussian disturbance with unknown covariance matrix. A new two-stage Rao test detector is proposed, which is obtained by cascading a GLRT-based subspace detector (SD) and the Rao test. The statistical characterization for the proposed two-stage test statistic is provided, under both noise-only and signal-plus-noise hypotheses. The associated probability of false alarm (P"f"a) and probability of detection (P"d) are derived in closed form. Performance of the proposed detector is demonstrated by simulation studies, wherein recently proposed detectors are involved for performance comparison. The results show that our detector can achieve better robustness with respect to (w.r.t.) the existing two-stage Rao test detector (AMF-RAO), and has better selectivity than the improved adaptive sidelobe blankers (WAS-ASB and KWAS-ASB) for small values of system parameters.

In this paper, we devise a near-optimal fast multiple-input multiple-output (MIMO) detector. Our proposed approach is a parallel architecture of QRD in tandem with Markov-chain Monte-Carlo (MCMC) processing, referred to as hybrid parallel QRD-MCMC (HPQRD-MCMC). Simulation results show that for a 4 × 4 MIMO system, HPQRD-MCMC can reduce the detection delay by 45%, compared with some existing MCMC detectors, and the reduction is more for higher dimensional systems, without compromising the error probability performance. With stringent latency requirements, the proposed detector outperforms significantly existing MCMC detectors.

In this paper we propose and assess a two-stage detector consisting of Kelly's GLRT followed by the whitened adaptive beamformer orthogonal rejection test. The performance analysis shows that it possesses the constant false alarm rate property with respect to the unknown covariance matrix of the noise; moreover, it can guarantee enhanced rejection capabilities of sidelobe signals and, at the same time, a limited performance degradation under perfect matching condition. The probability of false alarm and the probability of detection (for both matched and mismatched signals) have been derived in closed form and evaluated by means of numerical integration techniques.

In this work, we propose two adaptive receivers achieving enhanced range estimation capability through a joint exploitation of the oversampling and the spillover of target energy in adjacent range samples. To this end, a proper discrete-time model for the received signal is introduced. Then, the generalized likelihood ratio test (GLRT) and the so-called two-step GLRT are derived and assessed. The performance analysis, conducted using both simulated data and real recorded datasets, is aimed at assessing the effectiveness of proposed solutions, also in comparison with existing detectors sharing range estimation capabilities. The illustrative examples highlight that better detection performance and increased range estimation accuracy can be achieved by exploiting the oversampling at the price of an additional processing cost.

In this study, the authors derive an adaptive decision scheme with enhanced range estimation capabilities for point-like targets in partially homogeneous environments. To this end, the authors jointly exploit the oversampling of the received signal and the spillover of target energy to consecutive range samples. A suitable discrete-time model for the received signal is introduced and the so-called two-step generalised likelihood ratio test is derived and assessed. The performance analysis, conducted resorting to both simulated data and real recorded datasets, is aimed at assessing the effectiveness of the proposed solution, also in comparison with the existing detectors with range estimation capabilities. The illustrative examples highlight that better detection performances and increased range estimation accuracy can be achieved exploiting oversampling at the price of an additional processing cost.

In this paper, we deal with the problem of adaptive detection of point-like targets in Gaussian disturbance with unknown but persymmetric structured covariance matrix induced by a symmetric antenna geometry. In particular, at the design stage we modify the noise-only hypothesis assuming the presence of a fictitious signal orthogonal to the nominal one. The performance assessment, conducted by Monte Carlo simulation, has shown that the proposed receiver can significantly outperform its unstructured counterpart and guarantee enhanced rejection performance of unwanted signals.

This letter focuses on the design of selective receivers for homogeneous scenarios where a very small number of secondary data are available. To this end, at the design stage it is assumed that the cell under test (CUT) contains a fictitious signal orthogonal to the nominal steering vector under the null hypothesis; the clutter covariance matrix is modeled as a random matrix with an inverse complex Wishart distribution. Under the above assumptions, we devise two Bayesian detectors based on the GLRT criterion, both one-step and two-step. It is shown that the proposed detectors have the same detection structure as their non-Bayesian counterparts, substituting the colored diagonal sample covariance matrix (SCM) for the classic one. Finally, a performance assessment, conducted by Monte Carlo simulations, has shown that our detectors ensure better rejection capabilities of mismatched signals than the existing Bayesian detectors, at the price of a certain loss in terms of detection of matched signals.

In this paper we consider the problem of detecting multiple point-like targets in the presence of steering vector mismatches and Gaussian disturbance with unknown covariance matrix. To this end, we first model the actual useful signal as a vector belonging to a proper cone whose axis coincides with the whitened direction of the nominal array response. Then we develop two new robust adaptive detectors resorting to the two-step generalized-likelihood ratio test (GLRT) design procedure without assignment of a distinct set of secondary data. Finally, a performance assessment, conducted by Monte Carlo simulation, show that the proposed detectors achieve a visible performance improvement over their natural counterparts.

We consider the problem of adaptive signal detection in the presence of Gaussian noise with unknown covariance matrix. We propose a parametric radar detector by introducing a design parameter to trade off the target sensitivity with sidelobes energy rejection. The resulting detector merges the statistics of Kelly's GLRT and of the Rao test and so covers Kelly's GLRT and the Rao test as special cases. Both invariance properties and constant false alarm rate (CFAR) behavior for this detector are studied. At the analysis stage, the performance of the new receiver is assessed and compared with several traditional adaptive detectors. The results highlight better rejection capabilities of this proposed detector for mismatched signals. Further, we develop two two-stage detectors, one of which consists of an adaptive matched filter (AMF) followed by the aforementioned detector, and the other is obtained by cascading a GLRT-based Subspace Detector (SD) and the proposed adaptive detector. We show that the former two-stage detector outperforms traditional two-stage detectors in terms of selectivity, and the latter yields more robustness.

In this paper, we propose a new two-stage Rao test detector, which is obtained by cascading a subspace detector (SD) and the Rao test. A set of secondary data is available and noise returns in primary and secondary data share the same statistical characterization. Analytical expressions for the probability of false alarm (Pf a ) and the probability of detection (Pd) are provided in homogeneous environment. Remarkably, the proposed detector possesses the constant false alarm rate (CFAR) property with respect to the noise covariance matrix and the considered examples have highlighted that our detector can ensure better robustness with respect to the counterparts.

The OSAP-CFAR employs soft rule based on fuzzy logic to give better detection probability than that of OSGO and OSSO-CFAR detectors for Weibull clutter. However, studies show that the K distribution provides a better fit for many clutter situations. The performance of the OSAP -CFAR is studied and compared with OSGO and OSSO-CFAR for the Swerling II target model in K distributed clutter. The results represent that the detection performance of OSAP-CFAR detector is superior to that of OSGO and OSSO-CFAR detectors both in the homogenous background and in multiple interfering targets situations.

In order to improve radar's detection performance under non-Gaussian background clutter. We design a new distributed fuzzy CAGO/SO-CFAR detection system including two sensors. Both sensors compute the value of the membership function mapping to the false alarm space according to the samples of the reference cells, in the fusion centre two values are combined according to four kinds of fusion rule to estimate the background clutter power. The simulation results prove the detection improvement of CASO based on algebraic-sum rule under multi-target environment, and present better false-alarm-control capacity of CAGO based on MIN rule under clutter-edge environment.

In this paper, the detection performance of OSGO and OSSO CFAR detectors embedded in heavy-tailed Pearson distributed clutter is analyzed. We derive the closed mathematic form expressions of probability of false alarm rate and detection probability of the two CFAR schemes. While the processing speed of OSGO and OSSO CFAR is two times of that of OS CFAR, simulation results indicate that OSGO CFAR detector's performance is very close to OS, and OSSO CFAR offers better and robust detection performance than OS CFAR in multiple-target situation.

In order to improve the radar’s detection performance under Pearson distributed background clutter, the problem of designing CA-CFAR detector based on fuzzy logic is studied in this paper. A kind of distributed fuzzy CA-CFAR detection scheme including two detectors is proposed here. Two detectors compute the values of the membership function to the false alarm space from the samples of the reference cells, in the fusion centre two values are combined according to four kinds of fuzzy fusion rule to produce a global member function to estimate the background clutter power for decision. The simulation results indicate that the fuzzy CA-CFAR detector based on algebraic sum fusion rule provides more robust detection performance than conventional distributed CA CFAR based on AND or OR logic.

In this article, we consider the problem of adaptive detection for a multichannel signal in the presence of spatially and temporally colored compound-Gaussian disturbance. By modeling the disturbance as a multichannel autoregressive (AR) process, we first derive a parametric generalized likelihood ratio test against compound-Gaussian disturbance (CG-PGLRT) assuming that the true multichannel AR parameters are perfectly known. For the two-step GLRT design criterion, we combine the multichannel AR parameter estimation algorithm with three covariance matrix estimation strategies for compound-Gaussian environment, then obtain three adaptive CG-PGLRT detectors by replacing the ideal multichannel AR parameters with their estimates. Owing to treating the random texture components of disturbance as deterministic unknown parameters, all of the proposed detectors require no a priori knowledge about the disturbance statistics. The performance assessments are conducted by means of Monte Carlo trials. We focus on the issues of constant false alarm rate (CFAR) behavior, detection and false alarm probabilities. Numerical results show that the proposed adaptive CG-PGLRT detectors have dramatically ease the training and computational burden compared to the generalized likelihood ratio test-linear quadratic (GLRT-LQ) which is referred to as covariance matrix based detector and relies more heavily on training.