Adaptive Detection of Radar Range-Doppler Dual-Spread Targets in Lognormal-Texture Clutter

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.

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.

A fundamental task of radar is the detection of targets in noise and clutter. It is very difficult to detect weak targets in heavy sea clutter. In this paper we apply the discrete wavelet transform (WT), along with multilayer feedforward neural networks, to the detection of radar weak targets. The wavelet transform employs the Haar scaling function, which is well matched to the signals from targets. The hard threshold filter is adopted to remove sea clutter. The neural networks are trained with the back-propagation rule, which are used to detect weak targets. The simulated results show that the proposed method is very effective for radar detection of weak targets.

This paper addresses adaptive detection of range-spread target in spherically invariant random vector clutter. Based on the nonadaptive detectors of NSDD-GLRT and SDD-GLRT, two adaptive detectors named ANSDD-GLRT and ASDD-GLRT are devised by replacing the unknown normalized clutter covariance matrix with the sample covariance matrix based on the secondary data. The formulas of detection probability and false alarm probability are deduced. Moreover, the constant false alarm rate properties of both adaptive detectors are assessed and the influence of mismatched steering vector on detection probability is analyzed. The experimental results show that the adaptive detectors have an acceptable loss with respect to the nonadaptive counterparts. Furthermore, the ANSDD-GLRT converges much faster than the ASDD-GLRT, however, the latter rejects the misaligned signals more effectively. Both detectors perform well for the uniformly-distributed-energy target, but degenerate for the fluctuating targets. In addition, the correlation between target scatterers results in an additional gain for low signal-to-clutter power ratio.

Adaptive detection of wideband radar targets embedded in homogeneous Gaussian clutter is discussed, by utilizing the persymmetric clutter covariance matrix. Relying on the decision schemes of the one-step and two-step generalized likelihood ratio tests, two adaptive persymmetric detectors are introduced to mitigate the demanding requirement of secondary data. The performance assessment conducted by Monte Carlo simulation confirms their constant false alarm rate properties. And the simulation results furthermore shows that, both aforementioned persymmetric detectors can effectively avoid the collapsing loss resulted from no target scattering in some of the range cells under test.

The paper addresses adaptive detection of extended targets in Gaussian noise with unknown statistics. It is assumed that the radar can change the transmitted signal in azimuth. More precisely, it makes use of N N-dimensional signatures; the possible useful signal from each radar cell is a coherent pulse train while the disturbance is a Gaussian process, independent from cell to cell, but with the same (unknown) covariance matrix regardless of the illuminated one. Based on the above model, we propose an adaptive detector designed according to a two-step procedure. Its performance assessment and the comparison with a previously-proposed detector show that the proposed one can be a viable means to cope with uncertain scenarios.

In this paper the problem of target detection using coherent pulse terrain was considered for MIMO radars. We have formulated this problem as a hypothesis test. Using the generalized likelihood ratio test (GLRT) two adaptive decision schemes have been developed for clutter with unknown statistics. The performance of the proposed detectors have been evaluated using Monte-Carlo simulations. The results show the superiority of the MIMO radaars with temporal coherent processing over the conventional phased arrays.

In this chapter, we have established a well-defined framework, provided by the likelihood ratio concept and the statistical models developed in Chapters 3 and 6, within which useful detection schemes can be developed in a systematic way. The closely related problem of parameter estimation is also considered: maximum likelihood techniques derived from Bayes' theorem prove to be quite tractable for simple Gaussian clutter models, and can be incorporated into generalised likelihood ratio-based detection methods. Some relatively simple examples of the application of these principles have been discussed in detail, to the point where contact is made with the small target detection strategies discussed in Chapters 12 and 13. The principles demonstrated here can then be applied to other detection scenarios. The estimation of parameters characterising non-Gaussian clutter is more problematic: the maximum likelihood derived equations are now much less easy to solve. Nonetheless, useful estimation methods have been derived that can be applied to gamma, Weibull and K-distributed data. The compound representation of clutter developed in Chapters 3 and 6 plays a central, but rather subtle, role in this work. Small target detection procedures are applied to localised samples of data, to which the relatively simple and tractable Gaussian derived methods can be applied. Consequently, we should expect the methods described in Section 10.7 to be relatively effective even in spiky, non-Gaussian clutter; their performance can then be improved incrementally and relatively straightforwardly, when prior knowledge of the gamma distribution of local power can then be brought to bear, as is described in Section 10.10.

The ability to detect and track dim unresolved targets in heavy clutter can be improved by the inclusion of the spectral dimension. Because of the great variation in targets, operating conditions and environments factors the spectral signature of the target is typically unknown. This paper present a fully adaptive matched filter and tracking paradigm which assumes no a priori information about the spectral signature of the target. It is shown that the full SCR gain can be realized in the absence of the spectral signature of the target. The ROC curve of the detector is used to show that performance loss due to the absence of spectral information is entirely due to an increase in the false alarm probability. This increase in PFA adversely effects tracker performance. The SCR track feature is developed to mitigate these effects. Track features provide an information shunt around the detection threshold nonlinearity that would otherwise block the flow of useful information to the tracker.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Many radar detection algorithms that assume a stationary environment (clutter) have been proposed and analyzed over the years. However, in practice, changes in the nonstationary environment can perturb the parameters of the clutter distribution, or even alter the clutter distribution family, which can greatly deteriorate the target detection capability. To avoid such potential performance degradation, cognitive radar systems are envisioned which are required to rapidly realize the nonstationarity, accurately learn the new characteristics of the environments, and adaptively update the detector. In this paper, aiming to develop a fully cognitive radar for target detection in nonstationary environments, we propose a unifying framework that integrates (i) change-point detection of clutter distributions by using a data-driven cumulative sum (CUSUM) algorithm and its extended version, (ii) learning/identification of clutter distribution by applying sparse theory and kernel density estimation methods, and (iii) adaptive target detection by automatically modifying the likelihood-ratio test and corresponding detection threshold. Further, with extensive numerical examples, we demonstrate the achieved improvements in detection performance due to the proposed framework in comparison with a nonadaptive case, an adaptive matched filter method, and the clairvoyant case. We also use Wilcoxon rank-sum tests to evaluate the statistical significance of the performance improvements.

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

The UWB through-wall radar plays an unique role in disaster rescue operation, by providing the identification and location of the on-site survivors under fire, earthquake, collapse and other disaster conditions, as well as anti-terrorism and explosive ordnance disposal scenarios. However, the scenario of the disaster site may be complex, and the internal structure and furnishings inside the building could not be predicted in advance. A special scene with metal obstacles in the detection space is investigated in this paper. The UWB through wall radar test system and its composition is presented, and then the algorithms for human target adaptive motion filter detection and obstacle interference suppression are discussed. Finally, a tracking trace aggregation algorithm for strong offset targets is proposed. By analyzing the test data., the results show that the adaptive target detection method proposed in this paper could effectively eliminate the interference of metal obstacles, achieving enough signal-to-noise ratio and signal-to-clutter ratio. The improved point trace aggregation algorithm is also verified in target tracking test, achieving ideal tracking and detection results are obtained.

We address adaptive detection of extended radar targets embedded in homogeneous Gaussian noise plus subspace interference. We assume that the actual steering vector lies in a fixed and unknown direction of a preassigned and known subspace, while interfering signals are supposed to belong to an unknown subspace, with directions possibly varying from one resolution cell to another. The resulting detection problem is formulated in the framework of statistical hypothesis testing and solved using an ad hoc algorithm strongly related to the GLRT. The performance analysis, carried out also in comparison to natural benchmarks, shows that the proposed detector is an effective solution to the problem of detecting a one-dimensional signal embedded in noise and unknown subspace interference.

In this paper, we devise adaptive decision schemes to detect targets competing against clutter and smart noise-like jammers (NLJ) which illuminate the radar system from the sidelobes. Specifically, the considered class of NLJs generates a pulse of noise (noise cover pulse) that is triggered by and concurrent with the received uncompressed pulse in order to mask the skin echo and, hence, to hide the true target range. The detection problem is formulated as a binary hypothesis test and two different models for the NLJ are considered. Then, ad hoc modifications of the generalized likelihood ratio test are exploited where the unknown parameters are estimated by means of cyclic optimization procedures. The performance analysis is carried out using simulated data and proves the effectiveness of the proposed approach for both situations where the NLJ is either active or switched off.

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.