Adaptive direction detection of extended targets in noise plus unknown subspace interference

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.

Abstract. This study addresses the question of the existence of a parent flood frequency distribution on a European scale. A new database of L-moment ratios of flood annual maximum series (AMS) from 4105 catchments was compiled by joining 13 national data sets. Simple exploration of the database presents the generalized extreme value (GEV) distribution as a potential pan-European flood frequency distribution, being the three-parameter statistical model that with the closest resemblance to the estimated average of the sample L-moment ratios. Additional Monte Carlo simulations show that the variability in terms of sample skewness and kurtosis present in the data is larger than in a hypothetical scenario where all the samples were drawn from a GEV model. Overall, the generalized extreme value distribution fails to represent the kurtosis dispersion, especially for the longer sample lengths and medium to high skewness values, and therefore may be rejected in a statistical hypothesis testing framework as a single pan-European parent distribution for annual flood maxima. The results presented in this paper suggest that one single statistical model may not be able to fit the entire variety of flood processes present at a European scale, and presents an opportunity to further investigate the catchment and climatic factors controlling European flood regimes and their effects on the underlying flood frequency distributions.

In this paper the problem of image feature extraction is considered with emphasis on developing methods which are resilient in the presence of data contamination. The issue of robustness of estimation procedures has received considerable attention in the statistics community [1–3] but its results are only recently being applied to specific image analysis tasks [4–7]. In this paper we show how the design of robust methods applies to image description tasks posed within a statistical hypothesis testing and parameter estimation framework. The methodology is illustrated by applying it to finding robust, optimal estimation kernels for line detection and edge detection. We then discuss the relationship of these optimal solutions to both the well established Hough Transform technique and the standard estimation kernels developed in the statistics literature. The application of standard robust kernels to image analysis tasks is illustrated by two examples which involve circular arc detection in gray-level imagery and planar surface segmentation in depth data. Robust methods are found to be effective general tools for generating 2D and 3D image descriptions.

Correntropy includes not only the second-order statistics of the signal, but also the higher-order statistics of the signal. Therefore, correntropy is an effective tool to deal with nonlinear and non-Gaussian signals. In order to solve the problem that it is difficult to select the optimal frequency band of bearing fault vibration signal under the interference of Gaussian and non-Gaussian Noise, a new optimal frequency band selection method is proposed, which is named as Correntrogram. Firstly, the correntropy of the signal is calculated. Then correntropy is decomposed into multiple frequency bands using the 1/3-binary tree structure and the optimal frequency band is selected according to the L2/L1 norm. Finally, the squared envelope spectrum of the optimal frequency band is calculated and bearing fault characteristics frequency can be accurately identified. The results of simulation and experiment show that Correntrogram can correctly select the optimal frequency band of bearing fault vibration signal under the interference of Gaussian and non-Gaussian noise, which has good robustness, and its performance is better than that of traditional Kurtogram.KeywordsCorrentropyBearingFault detectionKurtogramOptimal frequency band

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.

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.

A deterministic model of admixture and genetic introgression: The case of Neanderthal and Cro-Magnon

In this paper, two noniterative direction-of-arrival (DOA) estimation algorithms of noncircular signals in nonuniform noise environment are proposed. Different from the mainstream nonuniform iterative algorithm, the algorithms we proposed in this paper could attain DOA estimation effectively in nonuniform noise environment without iterative and convex optimization processing. In the direct removal of nonuniform noise (DRONN) method, the noise subspace is estimated by using special processing of the array output covariance matrix, moreover, it does not require to estimate the noise covariance matrix. On the other hand, the piecewise estimation of nonuniform noise (PEONN) method first estimates the noise covariance matrix, and the noise subspace used in this process is estimated by using the DRONN method, then the generalized eigendecomposition (GED) is used to estimate the noise covariance matrix. The above two proposed methods are able to suppress the interference of nonuniform noise effectively, and accurately estimate DOA without iterative processing. In addition, the two proposed methods use the reduced-dimensional noncircular multiple signal classification (RD-NC-MUSIC) algorithm to estimate DOA without complex two-dimensional spatial search, and they can effectively reduce the computational complexity. The effectiveness of the two proposed methods are proved via the simulation results.

In this paper, we suppress the intentional coherent interferences for target detection purposes in presence of coupling Gaussian clutter with unknown statistical properties. To this end, we propose a knowledge-assisted sparse detector based on the generalized likelihood ratio test criterion, which ensures an enhanced capability of background information learning. First, we extend the conventional 1-D sparsity model (angular dimension) of the target/jammer to 2-D (angular-Doppler) by further exploiting the underlying sparse nature in Doppler domain. Remarkably, the obtained Doppler information can be suitably exploited to distinguish the coherent signals located in the same angular sector with different Doppler frequencies for further tracking or filtering. Besides, we resort to the Bayesian framework to improve the quality of the estimates of the clutter covariance matrix (CCM) by deriving the posterior information of the CCM. The illustrative examples conducted on synthetic data corroborate the enhanced discrimination for coherent signals and improved detection performance under sample-starved circumstances of the proposed detectors with respect to the counterparts.

A new rail crack detection method using LSTM network for actual application based on AE technology

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

Adaptive detection of distributed targets in noise and interference which is partially related with targets

Robust adaptive beamforming (RAB) can be used to suppress interference signals while retaining the desired signals received by a sensor array. However, desired signal self-cancellation and model mismatch can affect RAB performance. In this paper, a novel interference-plus-noise covariance matrix (INCM) reconstruction method is proposed for RAB to solve these problems. The proposed method divides the desired signal into two ranges according to the input signal-to-noise ratio (SNR), namely low SNR and high SNR. In the low SNR range, INCM reconstruction directly uses the same sample covariance matrix as the sample matrix inversion (SMI) beamformer to retain the advantages of the traditional SMI algorithm. In the high SNR range, the eigenvalues of the sample covariance matrix are used to estimate the interference power and noise power. The optimized interference steering vector (SV) is obtained by solving a quadratic convex optimization problem in an interference subspace. The INCM is reconstructed from the interference SVs, interference power, and noise power. The reconstructed INCM is then used to correct the desired signal SV via maximizing the beamformer output power. This is achieved by solving a quadratically constrained quadratic programming (QCQP) problem. Analysis and simulation results are presented which demonstrate that the proposed method performs well under a variety of mismatch conditions.

A nonlinear group-blind technique is developed for joint detection of some given users' data in a CDMA uplink environment with the presence of unknown interference. This method performs the so-called "slowest-descent search" over a likelihood function of the desired users, starting from the estimate closest to the unconstrained maximizer of the likelihood function, and along mutually orthogonal directions where this likelihood function drops to the slowest. Simulation results show that this new nonlinear technique offers substantial performance improvement over the previously proposed linear group-blind multiuser detectors with little attendant increase in computational complexity. The problem of group-blind multiuser detection in the presence of both unknown interference and impulsive ambient noise is also treated under the framework of slowest-descent search, with the aid of a novel subspace-based robust interference cancellation scheme. It is seen that this robust group-blind method significantly outperforms the robust blind multiuser detection scheme proposed previously.

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.