Adaptive Beamformer Orthogonal Rejection Test Research Articles

A Priori Knowledge Based Ground Moving Target Indication Technique Applied to Distributed Spaceborne SAR System

Through formation flying, the distributed spaceborne SAR(synthetic aperture radar) system can increase the number of spatial degree of freedoms (DOFs) and provide flexible multi-baselines for SAR-GMTI (ground moving target indication), which improves the system performance. This paper proposes an a priori knowledge-based adaptive clutter cancellation and moving target detection technique applied to the distributed spaceborne SAR-GMTI systems. Firstly, the adaptive clutter cancellation technique is exploited to suppress the ground clutter. A priori knowledge, such as road network information, is integrated to the adaptive clutter cancellation processor to reduce any moving target steering vector mismatch. Secondly, adaptive matched filter (AMF) and adaptive beamformer orthogonal rejection test (ABORT) are exploited as adaptive detection techniques for moving target detection. Due to the dense road network, the moving target steering vector estimation may be ambiguous for the different position and orientation of the roads. The multiple hypothesis testing (MHT) technique is proposed to detect the moving targets and resolve the potential ambiguities. A scheme is exploited to detect, classify, and relocate the moving targets. Finally, simulation experiments and performance analysis have demonstrated the effectiveness and robustness of the proposed technique.

Adaptive CFAR Detectors for Mismatched Signal in Compound Gaussian Sea Clutter With Inverse Gaussian Texture

This letter deals with the adaptive detection problem in compound Gaussian sea clutter with inverse Gaussian texture when the mismatched signal occurs. In order to reject the mismatched signal, we introduce a fictitious signal to the null hypothesis based on the adaptive beamformer orthogonal rejection test (ABORT). We adopt the two-step generalized likelihood ratio test (GLRT) criterion and develop two selective detectors, i.e., the ABORT-like compound Gaussian with inverse Gaussian (A-IGCG) detector and ABORT-like maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> (MAP) compound Gaussian with inverse Gaussian (AM-IGCG) detector. Both the proposed detectors are proved to be constant false alarm rate (CFAR) detectors. The several experiments that compare the proposed detectors with other selective detectors and compound Gaussian detectors are conducted by using two types of real sea data, i.e., IPIX 1998 and 2019 real sea data. The numerical results indicate that the novel detectors exhibit significant tradeoffs between performances of detection and selectivity property.

Robust Polarimetric Adaptive Detector Against Target Steering Matrix Mismatch

A polarimetric scheme is presented for the detection of a range-spread target when the presumed target steering matrix is deviated from the actual one. Following the adaptive beamformer orthogonal rejection test model, the decision statistic of the proposed detector involves the polarimetric clutter-plus-noise covariance matrix, the target component after noise whitening and rotation, as well as the sidelobe interference vectors of multiple range cells. In the practical realization of the detector, the polarimetric clutter-plus-noise covariance matrix is estimated by using the minimum mean-square-error regularized secondary data sample averaging. Taking into account the mismatch involved in the presumed target steering matrix, the target component is obtained by least square fitting subject to multiple separate conic uncertainty set constraints. The estimation of the sidelobe interference vectors is also based on the Bayesian statistics, and the involved polarimetric clutter-plus-noise covariance matrix is ultimately replaced by its Bayesian estimate previously obtained. The performance of the proposed detector has been evaluated and compared with the existing popular polarimetric detectors using both synthetic and real data. The results show that in the presence of target steering matrix mismatch caused by pointing errors, sensor position errors, channel calibration errors, multipath propagation, and random errors, the proposed detector has a superior performance to the current detectors in terms of the probability of detection.

ABORT-like detector to combat active deceptive jamming in a network of LFM radars

This paper studies an electronic counter-counter measures (ECCM) scheme combating against deceptive electronic counter measure (ECM) techniques. An adaptive detector exploiting generalized likelihood ratio test (GRLT) criterion is applied to detect the presence of deceptive jamming in fractional Fourier transform (FrFT) domain. First, the generating mechanism of spurious frequencies is analyzed based on the Volterra serial. The proposed nonlinear distortion model based on power amplifier behavior is robust in distortion analysis when the memory effect is considered. Second, a modified adaptive beamformer orthogonal rejection test (ABORT) like detector in closed form is built. The proposed detector can discriminate the echo and deceptive jamming adaptively by exploiting primary data and secondary data. This ECCM scheme is capable of guaranteeing the performance without the restriction of orthogonality, which is essential for the ABORT detectors. The expansion to radar network is discussed as a special case at the final part of this paper. Numerical simulations demonstrate the effectiveness of the proposed method.

Computationally Efficient Adaptive Sidelobe Blanker with Improved Mismatched Signals Rejection

This paper deals with the problem of adaptive signal detection in the presence of Gaussian noise with unknown covariance matrix. An improved adaptive sidelobe blanker (ASB) is proposed, which consists of an adaptive matched filter (AMF) followed by a whitened adaptive beamformer orthogonal rejection test (W-ABORT). A 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 (Pfa) and probability of detection (Pd) are derived in closed form. The performance assessment confirms the effectiveness of the newly-proposed detection algorithms also in comparison to previously-proposed ones.

Parametric detector in the situation of mismatched signals

This study deals with the problem of adaptive detection in the presence of signal mismatch. An effective parametric detector is proposed, which encompasses Kelly's generalised likelihood ratio test (GLRT), adaptive matched filter (AMF) and adaptive beamformer orthogonal rejection test (ABORT) as its three special cases. The novel detector can control the degree to which the mismatched signals are rejected. A remarkable feature is that the novel detector can achieve slightly higher probability of detection, for matched signals than those of Kelly's GLRT, AMF and ABORT. Furthermore, it can also provide both improved robustness to the mismatched signals and enhanced rejection of the mismatched signals than its natural competitors.

Adaptive detection of distributed targets with orthogonal rejection

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.

Adaptive subspace detector for multi-input multi-output radar in the presence of steering vector mismatch

This study studies the problem of adaptive target detection by multi-input multi-output (MIMO) radar with multiple antennas at each of the receiver sites. MIMO radar has been postulated to have a number of advantages over its monostatic counterpart. However, many previous publications have not necessarily made valid assumptions in assessing performance potential. Here, the authors consider the issue of the overall MIMO radar system steering vector which should be aligned with the desired pointing direction. However, misalignment can occur because of calibration errors, receiver channel imperfections and beam pointing errors. To account for this misalignment, the steering vector is assumed to belong to a known linear subspace. At the design stage, the authors resort to a generalised likelihood ratio principle and Rao test criterion. Subsequently, MIMO versions of the subspace generalised likelihood ratio test (MIMO-SGLRT), the subspace adaptive beam-former orthogonal rejection test (MIMO-SABORT) and the subspace Rao (MIMO-SRao) detector are developed to improve the robustness of MIMO radar detection performance for the case of steering vector mismatch. The constant false alarm rate properties of the three detectors are demonstrated. Finally, the performance of the MIMO-SGLRT, MIMO-SABORT and MIMO-SRao detectors in both the matched and mismatched steering vector cases are numerically evaluated. The results show the robustness of the proposed detectors to steering vector mismatch.

Detection Algorithms to Discriminate Between Radar Targets and ECM Signals

We address adaptive detection of coherent signals backscattered by possible point-like targets or originated from electronic countermeasure (ECM) systems in presence of thermal noise, clutter, and possible noise-like interferers. In order to come up with a class of decision schemes capable of discriminating between targets and ECM signals, we resort to generalized likelihood ratio test (GLRT) implementations of a generalized Neyman-Pearson rule (i.e., for multiple hypotheses). The adaptive detectors rely on secondary data, free of signal components, but sharing the statistical characterization of the noise in the cell under test. The performance assessment focuses on an adaptive beamformer orthogonal rejection test (ABORT)-like detector; analytical expressions for the probability of false alarm, the probability of detection of the target, and the probability of blanking the ECM (coherent) signal are given. More remarkably, it guarantees the constant false alarm rate (CFAR) property. The performance assessment shows that it can outperform the adaptive sidelobe blanker (ASB) in presence of ECM systems.

An Improved Adaptive Sidelobe Blanker

We propose a two-stage detector consisting of a subspace detector 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 and that it can guarantee a wider range of directivity values with respect to previously proposed two-stage detectors. The probability of false alarm and the probability of detection (for both matched and mismatched signals) have been evaluated by means of numerical integration techniques.

Theoretical Performance Analysis of the W-ABORT Detector

In a recent paper we introduced a modification of the adaptive beaniformer orthogonal rejection test (ABORT) for adaptive detection of signals in unknown noise, by supposing under the null hypothesis the presence of signals orthogonal to the nominal steering vector in the whitened observation space. We will refer to this new receiver as the whitened adaptive beamformer orthogonal rejection test (W-ABORT). Through Monte Carlo simulations this new detector was shown to provide better rejection capabilities of mismatched (e.g., sidelobe) signals than existing ones, like ABORT or the adaptive coherence estimator (ACE), but at the price of a certain loss in terms of detection of matched (i.e., mainlobe) signals. The aim of this paper is to provide a theoretical validation of this fact. We consider both the case of distributed targets and point-like targets. We provide a statistical characterization of the W-ABORT test statistic, under the null hypothesis, and for matched and mismatched signals under the alternative hypothesis. For distributed targets, the probability of false alarm and the probability of detection can only be expressed in terms of multi-dimensional integrals, and are thus very complicated to obtain; in contrast, for point-like targets, such probabilities can be easily calculated by numerical integration techniques. The theoretical expressions derived herein corroborate the simulation results obtained previously.

Adaptive Detection and Interference Rejection of Multiple Point-Like Radar Targets

We present an adaptive algorithm aimed at detecting multiple point-like radar targets embedded in correlated Gaussian noise. The proposed detector modifies and improves the adaptive beamformer orthogonal rejection test (ABORT) idea to address detection of multiple targets. More precisely, it relies on the so-called two-step generalized likelihood ratio test (GLRT) design procedure implemented without assignment of a distinct set of secondary data. The newly proposed detector can guarantee the constant false alarm rate (CFAR) property and the performance assessment, conducted resorting to simulated data, has shown that it exhibits better rejection capabilities of mismatched signals than previously proposed detectors, at the price of an acceptable performance loss for matched signals

Adaptive beamformer orthogonal rejection test

Research in the area of signal detection in the presence of unknown interference has resulted in a number of adaptive detection algorithms. Examples of such algorithms include the adaptive matched filter (AMF), the generalized likelihood ratio test (GLRT), and the adaptive coherence estimator (ACE). Each of these algorithms results in a tradeoff between detection performance for matched signals and rejection performance for mismatch signals. This paper introduces a new detection algorithm we call the adaptive beamformer orthogonal rejection test (ABORT). Our test decides if an observation contains a multidimensional signal belonging to one subspace or if it contains a multidimensional signal belonging to an orthogonal subspace when unknown complex Gaussian noise is present. In our analysis, we use a statistical hypothesis testing framework to develop a generalized likelihood ratio decision rule. We evaluate the performance of this decision rule in both the matched and mismatched signal cases. Our results show that for constant power complex Gaussian noise, if the signal is matched to the steering vector, ABORT, GLRT, and AMF give approximately equivalent probability of detection, higher than that of ACE, which trades detection probability for an extra invariance to scale mismatch between training and test data. Of these four tests, ACE is most selective and, therefore, least tolerant of mismatch, whereas AMF is most tolerant of mismatch and, therefore, least selective, ABORT and GLRT offer compromises between these extremes, with ABORT more like ACE and with GLRT more like AMF.