Adaptive Target Detection Research Articles

Adaptive detection of point-like targets based on a reduced-dimensional data model

Abstract This paper approaches the problem of detecting a point-like target in Gaussian background. The useful signal lies in an one-dimensional subspace spanned by a known steering vector. The Gaussian disturbance fills the whole observation space. Since the disturbance outside the signal subspace is unrelated to the decision result, it is eliminated by pre-multiplying the recorded data by the conjugate transpose of the steering vector in this paper. This preprocessing is not applied in the classical detectors, such as the generalized likelihood ratio test (GLRT), the adaptive matched filter (AMF), etc. This preprocessing results in a new scalar data model where the useful signal and disturbance lie in the same space. Based on this scalar data model, two new detectors are designed through the one-step and two-step GLRT, respectively. Their probabilities of false alarms and detections are derived analytically. The one- (two-) step GLRT-based detector has the (approximately) constant false alarm rate feature in the Gaussian background. Simulation results show that the proposed detectors outperform those classical ones when finite training data is available. The one-step GLRT-based detector is more robust to mismatched signals than those classical detectors.

OFDM chirp radar for adaptive target detection in low grazing angle

Low-altitude target detection is of great importance in radar applications, especially for military radar to counter the targets penetrated from low grazing angles. However, it is a challenge to detect the targets in low grazing angle area due to severe multipath reflection effects. In this study, the authors propose orthogonal frequency division multiplexing (OFDM) chirp waveform for target detection in low grazing angle region. The OFDM chirp waveform has better peak-to-average-power ratio level and provides larger time-bandwidth product than that of conventional OFDM waveform, which is beneficial for target detection. Two different radar schemes, namely, OFDM chirp radar and OFDM chirp multiple-input multiple-output (MIMO) radar, are studied under the Earth's curvature geometry model with the consideration of realistic multipath reflection effects. The generalised likelihood ratio test algorithm is developed by jointly exploiting the advantages of OFDM chirp waveform and MIMO configuration in increased degrees of freedom. In addition, an adaptive algorithm is further applied to enhance the target detection performance. All proposed schemes are verified by numerical results.

Tunable Adaptive Detection in Colocated MIMO Radar

We consider the adaptive target detection problem in colocated MIMO radar where disturbance covariance matrix is unknown, and no training data are available. A tunable detector is proposed, which includes the Rao and Wald tests as special cases. The selectivity or robustness of the proposed detector to mismatched signals can be flexibly tuned by adjusting the tuning parameter. An equivalent form of the proposed detector is obtained to provide a deeper insight into how the selectivity or robustness is adjusted by selecting the tuning parameter. We derive analytical expressions for the probability of false alarm and the detection probability to evaluate its detection performance in both matched and mismatched steering vector cases. It is shown that the proposed detector bears a constant false alarm rate property with respect to the disturbance covariance matrix. Numerical results reveal that the selectivity of the proposed detector can be enhanced by increasing the value of the tuning parameter.

Adaptive Detection of Low-Signature Targets in Forward-Looking GPR Imagery

We present an image-domain adaptive likelihood ratio tests (LRT) detector for low-signature target detection in forward-looking ground-penetrating radar. We exploit multiview tomographic images of the scene under investigation to iteratively adapt the statistics of the targets and clutter arising from the interface roughness. Using numerical electromagnetic data, it is shown that the proposed adaptive LRT detector provides significantly lower false-alarm rates compared with its nonadaptive counterpart while providing comparable detection performance.

Fast target detection in radar images using Rayleigh Mixtures and Summed Area Tables

As the first step of automatic image interpretation systems, automatic detection of targets should be accurate and fast. For Synthetic Aperture Radar (SAR) images, Constant False Alarm Rate (CFAR) is the most popular framework used for target detection. In CFAR, modeling of the clutter is crucial since the decision threshold is calculated based on this model. In this study, we propose to model the background statistics using a Rayleigh Mixture (RM) model. Such an approach facilitates modeling of complex statistics, including but not limited to those involved in heavy tailed distributions, which are shown to be good fits especially for high resolution SAR images. We also propose an efficient method to evaluate CFAR thresholds according to the proposed model by use of Summed Area Tables (SAT). SAT provides a remarkable efficiency as the Rayleigh distribution is represented by only one parameter that can be estimated using simple moments. Tiling and parallel implementation is also utilized for fast computation of results. The outcome is a highly-accurate, extremely fast, and adaptive target detection approach that can be seamlessly used with a variety of complex SAR scenes. Our experiments compare the proposed approach with existing target detection methods and demonstrate its effectiveness as well as the benefits it provides.

Adaptive Target Detection Techniques for OFDM-Based Passive Radar Exploiting Spatial Diversity

Recently, the benefit of using a channel-based detector (CHAD) with a passive radar exploiting orthogonal frequency division multiplexing waveforms radiated, e.g., by DVB-T transmitters of opportunity has been studied. When multiple antennas are available on receive, we state that CHAD can be seen as space–time array-processing performing on a particular coherent frequency datacube. Building on this new interpretation, we propose an improved version of CHAD in the form of fully dimensional space–time adaptive processing (STAP). Optimization of the signal-to-interference-plus-noise ratio is obtained combining a linearly constrained minimum variance space–time adaptive beamforming and a least squares spatial adaptive filtering. Unlike classical STAP approaches, no training data are used here and only one space–time sample matrix inversion is required. The computational load is then highly reduced allowing a practical deployment. Moreover, since no beamscan in space is performed, the knowledge of array characteristics is not required and the performance shall not be impacted by any calibration errors. Finally, all the target returns being located in a single 2D (range-Doppler) surveillance map, the detection process is then simplified. Results on experimental data show the interests of this new surveillance scheme: No need for a prior rejection of the dominant interference, systematic reduction of secondary lobes, and discrimination of slow moving targets.

Adaptive Detection and Range Estimation of Point-Like Targets With Symmetric Spectrum

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.

Knowledge-based adaptive detection of radar targets in generalized Pareto clutter

This paper studies adaptive detection of radar targets embedded in generalized Pareto clutter on the condition with the limited secondary data. In order to alleviate the effects of the non-Gaussian characteristic of the clutter, a-priori knowledge of the non-Gaussian clutter is considered in the designed detector. More precisely, we consider that the texture of clutter obeys the inverse gamma distribution and the inverse covariance matrix of speckle is a combination of multiple a-priori spectral models. Within these considerations, we obtain an adaptive detector based on the generalized likelihood ratio test. Finally, the performance of the proposed detector is evaluated via the Monte-Carlo technique. The experiments results indicate that the proposed detector outperforms the 1S-GLRT detector in limited secondary data scenarios.

Adaptive Detection of Point-Like Targets in Partially Homogeneous Clutter With Symmetric Spectrum

In this correspondence, we deal with the problem of detecting the signal of interest in the presence of Gaussian clutter with symmetric spectrum. In particular, we consider the so-called partially homogeneous environment (PHE), 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 exploit the spectral properties of the clutter to transfer the data from the complex to the real domain. Then, we derive two adaptive detectors relying on the Rao test and a suitable modification of the generalized likelihood ratio test. The performance assessments, conducted on both simulated data and real recorded datasets demonstrate the effectiveness of the newly proposed detectors compared with the traditional state-of-the-art counterparts, which ignore the spectrum symmetry. Finally, they can ensure better detection performance than the existing symmetric spectrum detector derived for PHE.

Model-based Rao test for adaptive range-spread target detection

This paper deals with the problem of detecting range-spread target in Gaussian disturbance with unknown covariance matrix. A model-based Rao detector is derived by modeling the disturbance as an autoregressive (AR) process with unknown parameters. Meanwhile, the asymptotic expressions for the probabilities of false alarm and detection are derived in closed form, which show that the newly proposed detector is asymptotically constant false alarm rate with respect to the disturbance covariance matrix. The performance assessment conducted by resorting to the simulated data, also in comparison to the previously proposed detectors, has confirmed the effectiveness of the newly proposed detectors.

Invariant Adaptive Detection of Range-Spread Targets Under Structured Noise Covariance

The invariance principle is adopted to develop an exhaustive study for adaptive detection of range-spread targets in Gaussian noise sharing a block-diagonal covariance structure. For this problem, the usual generalized likelihood ratio principle is intractable. In this paper, we first determine the largest group of affine transformations that does not alter the decision problem. Then, a maximal invariant identified by this group is derived, which can characterize the totality of the invariant detectors and extends the existing results for the point-target case. A theoretical performance analysis of the maximal invariant is also given. Finally, we propose two classes of invariant detectors, which are distinguished by whether a constant false alarm rate (CFAR) is maintained. Numerical experiments are provided for a comparison of the proposed detectors, where a tradeoff between the CFARness and the improved performance has been observed and studied.

Adaptive Radar Detectors for Point-Like Gaussian Targets in Gaussian Noise

We address the problem of adaptive radar detection of point-like targets in presence of Gaussian noise with unknown spectral properties, adopting a “second-order approach” to target modeling. In fact, the presence of a useful signal is modeled in terms of a rank-1 modification of the noise covariance matrix, similarly for the possible presence of a fictitious signal under the null hypothesis, aimed at increasing the selectivity of the detector. A set of homogeneous training data is assumed to be available. Results show that the proposed detectors can outperform natural competitors, especially assuming a limited number of training data.

Adaptive Target Detection in Passive Bistatic Radars: A Theoretical Analysis

This paper provides a theoretical analysis to consolidate the theory of “adaptive” target detection in the surveillance channel of passive bistatic radars where noise, strong returns of clutter and target returns are present. It is known in the literature that by generating the mixing product for each bistatic sample delay in the surveillance channel using the preprocessed and multipath-free reference channel signal, adaptivity can be realized. However, the theoretical background of this idea is not strong. In this paper, the statistical characteristics and the distribution of the mixing product are analyzed leading to the hypothesis test applicable to the “adaptive” detection problem. We investigate the iid requirements of secondary data, and a suitable scheme for secondary data generation in which these requirements are approximately satisfied is proposed. Several adaptive target detectors from active pulse radars’ literature based on the derived hypothesis test are generalized for use in passive radars and their performances are assessed by simulations and theoretical analysis. The simulation results indicate that these detectors are generally preferred compared to the conventional cross ambiguity function processing. It is also shown that the simulation-based and theoretical detection performances validate each other.

Invariance Theory for Adaptive Detection in Interference With Group Symmetric Covariance Matrix

This paper describes the adaptive detection of point-like targets in a Gaussian environment assuming a group symmetric structure for the interference covariance matrix. This special configuration enforces block-sparsity and permits splitting of the original observation space into lower-dimensional subspaces, each characterized by its own nuisance parameters. Hence, the Principle of Invariance is invoked to select decision rules enjoying some symmetry defined by a suitable group of transformations which leave the decision problem unaltered. All the invariant tests can be expressed as functions of a maximal invariant statistic whose derivation is among the key results of this paper. Finally, some common design criteria are applied to come up with adaptive architectures that share invariance, and their behavior is assessed to highlight the interplay between sample size and detection performance in comparison with conventional decision schemes.

Knowledge-Based Adaptive Detection: Joint Exploitation of Clutter and System Symmetry Properties

We address adaptive radar detection of targets embedded in clutter characterized by a symmetrically structublack power spectral density (PSD) and persymmetric covariance matrix. At the design stage, such properties are jointly exploited to come up with decision schemes capable of guaranteeing superior detection performances with respect to architectures which incorporate either persymmetry or clutter PSD symmetry. The performance analysis, both on simulated and on real radar data, confirms the superiority of the newly proposed architectures over their natural counterparts which do not take advantage of both the sources of a priori information.

Adaptive detection of range-spread targets in compound Gaussian clutter with the square root of inverse Gaussian texture

The compound Gaussian clutter with the square root of inverse Gaussian texture component has been successfully used for modeling the heavy-tailed non-Gaussian clutter measured by high-resolution radars. In high-resolution radars, the targets may extend along multiple consecutive range cells, which are called range-spread targets. In this paper, we consider the range-spread target detection problem in the compound Gaussian clutter with the square root of inverse Gaussian texture. Three adaptive detectors are proposed based on Bayesian one-step generalized likelihood ratio test, maximum a posteriori generalized likelihood ratio test and Bayesian two-step generalized likelihood ratio test, respectively. Finally, the detection performances of the proposed detectors are evaluated by the Monte Carlo simulation. The simulation results show that the proposed detectors have better detection performance of range-spread target than the conventional generalized likelihood ratio test detector.

Coincidence of Maximal Invariants for Two Adaptive Radar Detection Problems

This letter deals with adaptive radar detection of targets embedded in Gaussian clutter plus range-distributed subspace-structured jamming by exploiting the invariance theory. The class of invariant detectors which ensure the constant false alarm rate property has been characterized by designing the maximal invariant statistic for the studied detection problem. The achievement of this letter is the coincidence of the obtained maximal invariant with that derived assuming range-concentrated jamming.

Adaptive Detection of Point-Like Targets in Spectrally Symmetric Interference

We address adaptive radar detection of targets embedded in ground clutter dominated environments characterized by a symmetrically structured power spectral density. At the design stage, we leverage on the spectrum symmetry for the interference to come up with decision schemes capable of capitalizing the a-priori information on the covariance structure. To this end, we prove that the detection problem at hand can be formulated in terms of real variables and, then, we apply design procedures relying on the GLRT, the Rao test, and the Wald test. Specifically, the estimates of the unknown parameters under the target presence hypothesis are obtained through an iterative optimization algorithm whose convergence and quality guarantee is thoroughly proved. The performance analysis, both on simulated and on real radar data, confirms the superiority of the considered architectures over their conventional counterparts which do not take advantage of the clutter spectral symmetry.

ABORT-Like Detectors: A Bayesian Approach

In this paper, we deal with the problem of adaptive radar detection of point-like targets in presence of noise with unknown spectral properties. As customary, we assume that a set of data sharing the same properties of the noise in the cell under test is available. To cope with a limited number of training data, a Bayesian framework is adopted at the design stage. In order to come up with detectors with good rejection capabilities, the possible presence of a fictitious signal under the null hypothesis is modeled probabilistically, as opposite to the conventional ABORT-like approach. Several detectors are devised for the problem at hand, with different complexities. The performance assessment, conducted by means of Monte Carlo simulations, reveals that a good trade-off between detection power and selectivity can be achieved, even assuming a limited number of training data.

Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map.

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate.