Diffuse Multipath Exploitation for Adaptive Detection of Range Distributed Targets

This paper studies adaptive radar detection of pointlike targets in the presence of Gaussian interference and possible diffuse multipath. For this problem, a Tunable Adaptive Matched Filter (T-AMF) detector which contains a tuning parameter ruled by the strength of multipath effects was proposed. This paper develops a two-stage strategy for the selection of the tuning parameter by jointly using a two-step energy detector and the expected likelihood (EL)-based principle. Once plugged into the T-AMF, the selected parameter yields a fully adaptive detector free of any user parameter. Remarkably, the new architecture possesses the desired constant false alarm rate (CFAR) property with respect to the disturbance covariance. Finally, its detection performance is assessed and validated via numerical examples.

In the non-Gaussian clutter modeled as independent and identically distributed spherically invariant random vectors, three estimators of sample covariance matrix (SCM), normalized sample covariance matrix (NSCM) and the corresponding recursive estimator (NSCM-RE) are analyzed. Based on the uniform theorem, three corresponding adaptive normalized matched filters (ANMF) are evaluated from the standpoint of constant false alarm rate (CFAR) property. The theoretical results demonstrate that the SCM-ANMF is only CFAR to the normalized clutter covariance matrix (NCCM); the NSCM-ANMF is only CFAR to the clutter power level; and the NSCM-RE-ANMF with finite number of iterations is still not CFAR to the NCCM. To ensure CFAR property of ANMF, an adaptive estimator (AE) is devised. Moreover, with AE as the initialization matrix for the iterations, the AE-RE is proposed. With finite number of iterations, the corresponding AE-REANMF guarantees CFAR property to both of the NCCM and the clutter power level. Finally, the performance assessment conducted by Monte Carlo simulation confirms the effectiveness of the proposed detectors.

This paper deals with the adaptive detection problem of multidimensional subspace signals in compound-Gaussian clutter. Owing to the limited number of training data, this paper exploits the persymmetric structural information and uses the two-step generalized likelihood ratio test (GLRT) approach to propose a persymmetric adaptive detector. Theoretical analysis prove that the detector ensures the constant false alarm rate (CFAR) property with respect to the texture and covariance matrix. In addition, numerical simulation shows that the proposed detector has better performance than the traditional detectors. Meanwhile, the effects of the covariance matrix estimation approaches, signal subspace dimension and mismatched signal subspace on the detection performance is assessed.

In this two-part paper, we consider the problem of adaptive multidimensional/multichannel signal detection in Gaussian noise with unknown covariance matrix. The test data (primary data) is assumed as a collection of sample vectors, arranged as the columns of a rectangular data array. The rows and columns of the signal matrix are both assumed to lie in known subspaces, but with unknown coordinates. Due to this feature of the signal structure, we name this kind of signal as the double subspace signal. Part I of this paper focuses on the adaptive detection in homogeneous environments, while Part II deals with the adaptive detection in partially homogeneous environments. Precisely, in this part, we derive the generalized likelihood ratio test (GLRT), Rao test, Wald test, as well as their two-step variations, in homogeneous environments. Three types of spectral norm tests (SNTs) are also introduced. All these detectors are shown to possess the constant false alarm rate (CFAR) property. Moreover, we discuss the differences between them and show how they work. Another contribution is that we investigate various special cases of these detectors. Remarkably, some of them are well-known existing detectors, while some others are still new. At the stage of performance evaluation, conducted by Monte Carlo simulations, both matched and mismatched signals are dealt with. For each case, more than one scenario is considered.

Adaptive detectors for mismatched subspace target in clutter with lognormal texture

Adaptive detection of multidimensional signals in the presence of interference with unknown covariance matrix is an expanding topic in a variety of scenarios ranging from radar/sonar to digital communication systems. We attack the problem of detecting a multidimensional radar signal, modeled as an unknown N/spl times/H matrix, embedded in Gaussian noise with unknown covariance matrix, with the ambition of devising receivers which yield the constant false alarm rate (CFAR) property. We show that this aim can be achieved by resorting to the principle of invariance, namely restricting our attention to hypothesis testing problems which remain unaltered under a proper group of transformations. Several detectors based on the maximal invariant statistic are studied and, in particular, the generalized likelihood ratio test (GLRT) is shown to belong to the class of invariant tests.

Near-optimum coherent CFAR detection of radar targets in compound-Gaussian clutter with inverse Gaussian texture

This paper addresses adaptive high resolution radar detection of range-distributed target embedded in compound-Gaussian clutter modeled as a spherically invariant random process (SIRP). Echoes backscattered from range-distributed target are modeled as a subspace random signal by using multiple dominant scattering models. At the design stage we resort to the so-called two-step GLRT-based design procedure and assume that a set of secondary data is available. A sub-optimal adaptive detector for range-distributed target is devised. More over, the analytical closed-form expression for the probability of false alarm of the proposed detector is derived. The proposed adaptive detector ensures fully constant false alarm rate (CFAR) property in SIRP clutter. Performance of the proposed CFAR adaptive detector is assessed through Monte Carlo simulations and is shown to have better detection performance compared with existing similar detector.

The work presented here addresses adaptive range-spread target detection in spherically invariant random vector (SIRV) clutter based on two-step generalized likelihood ratio test (GLRT) design procedure. Firstly, with known normalized clutter covariance matrix (NCCM), a robust detector with constant false alarm rate (CFAR) property is proposed, which cascades a detector based on the GLRT for target scatterer in each range cell and two binary integrators. Subsequently, the optimum parameters for the cascaded detector are obtained. Furthermore, by replacing the ideal NCCM with the approximate maximum likelihood (ML) estimated matrix based on secondary data only, the corresponding adaptive detector with approximate CFAR property is also devised. Moreover, the formulas relating the probability of false alarm to the detection threshold are deduced. The performance assessment conducted by Monte Carlo simulation confirms the effectiveness and robustness of the proposed detectors.

Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter

We present the problem of polarization diversity detection in compound-Gaussian clutter with unknown covariance matrix. To this end we assume that a set of secondary data, free of signal components and with the same covariance structure of the cell under test, is available. Due to the lack of a uniformly most powerful (UMP) detector we resort to a design procedure based upon the Rao and the Wald tests. Specifically we first derive the Rao and the Wald tests assuming that the covariance matrix is known, and then we substitute into the derived decision rules a suitable estimate of the clutter covariance. Interestingly, the newly proposed detectors share the constant false alarm rate (CFAR) property with respect to the texture statistical characterization. Moreover simulation results have shown that the Wald test based detector ensures a performance level higher than the Rao test. We have also conducted a further performance analysis, in the presence of real clutter data and in comparison with the previously proposed generalized likelihood ratio test (GLRT) based receivers, which highlights that, in general, the Wald test receiver outperforms its counterparts. Finally, since the newly proposed decision rules as well as the previously designed GLRTs do not ensure the CFAR property with respect to the clutter covariance matrix, we have developed a sensitivity analysis on the probability of false alarm (P/sub fa/), based on simulated clutter with covariance matrix estimated from real radar data. The results have shown that (P/sub fa/) is only slightly affected by variations in the clutter correlation properties and hence the CFARness is substantially achieved.

Antenna beam pattern in MIMO radar systems, like any other radar, exhibits sidelobes. Naturally it is possible for clutter and other sort of interference to be detected accidently via these sidelobes. This has the effect of mis-aligning the main beam steering vector and reducing detection performance. In addition, the performance is dependent on the precision of the array calibration process. This paper examines the competing effects of both those components. Specifically, we examine adaptive processing that operates across the whole MIMO system aiming to reduce sidelobe clutter, restore the steering vector to its true position and hence maximize signal to clutter ratio and therefore detection performance. The MIMO version of an Adaptive Detector with Conic Acceptance and Rejection (ADCAR) is developed, resorting to a generalized likelihood ratio principle. Constant False Alarm Rate (CFAR) properties of the detector are demonstrated. The performance of the MIMO- ADCAR is numerically evaluated. The results show the proposed detector is capable of achieving a compromise between sensitivity of mainlobe signal and rejection of sidelobe signal.

In this paper, we deal with the problem of adaptive detection of distributed targets in Gaussian disturbance with unknown but persymmetric structured covariance matrix. A homogeneous environment is considered at the design stage, and a receiver based on the generalized likelihood ratio test (GLRT) is derived. Remarkably the new receiver ensures the constant false alarm rate (CFAR) property with respect to the covariance matrix of the disturbance. Finally, a performance assessment, conducted by Monte Carlo simulation, has shown that the new receiver can significantly outperform its unstructured counterpart due to embedded a priori knowledge about the structure of the disturbance covariance matrix, especially in a severely heterogeneous scenario where a very small number of training data is available.

In this paper, we consider polarimetric adaptive detection in compound Gaussian clutter whose covariance matrix (CM) has a Kronecker structure. We derive the Cramer–Rao bound of a Kronecker structured CM estimate and analyze the constant false alarm rate property of the adaptive subspace matched filter detector that uses Kronecker structured estimates. We provide a general expression for the average signal-to-clutter ratio loss (SCRL) as a function of the mean square error of the covariance estimate. The aforementioned expression is helpful in determining how many samples are required in order to achieve a desired average SCRL level in practical scenarios. Based on that expression, we show that the required sample size can be effectively reduced by exploiting the Kronecker structure of the clutter CM. We also derive the asymptotic detection performance of the adaptive subspace matched filter. The analysis of SCRL and detection performance can be extended to more general scenarios, especially when the maximum-likelihood estimate of the structured CM involves solving fixed point equations. Numerical simulations validate the merits of the proposed methods.

We focus on the problem of detecting a signal in compound-Gaussian clutter, where the texture is a random variable with Gamma or inverse Gamma distribution. The persymmetric structure of the covariance matrix is exploited and a persymmetric generalized likelihood ratio test (Per-GLRT) using a three-step procedure is proposed. In addition, we prove that the Per-GLRT ensures constant false alarm rate (CFAR) property with respect to the covariance matrix. Finally, the detector is assessed by Monte Carlo simulations. Performance comparison of the Per-GLRT with the traditional GLRT shows that the former improves the detection performance in training-limited scenarios.