Achieving accurate clock synchronization may not be feasible for distributed multiple-input multiple-output (MIMO) radar systems with non-ideal clocks of the sensors. In this paper, the problem of target localization and clock synchronization in distributed MIMO radar systems in presence of time synchronization errors is addressed by utilizing time delay measurements. Firstly, the necessity to consider time synchronization errors when locating a target is illustrated by analyzing the increase in Cramer-Rao lower bound (CRLB) of the target position due to the time synchronization error. Then, a self-synchronized localization framework based on the hybrid maximum likelihood and maximum a posteriori estimator is proposed to estimate the target position and time synchronization error sequentially. Specifically, a method based on the levenberg marquardt algorithm and an analytical solution are developed respectively to estimate the target position, and a combination strategy is recommended further based on the two methods to achieve a more accurate target position estimate. The time synchronization error is figured out then by utilizing the target position estimate. Meanwhile, the closed-form solution of estimating the target position and time synchronization error is analytically proved to be approximately unbiased and can attain the CRLB under weak noise conditions. It is demonstrated through numerical simulations that the combination strategy has better estimation performance than the two methods. Simultaneously, simulation results show that the proposed self-synchronized localization method outperforms the state-of-the-art algorithms in the target position and time synchronization error estimation accuracy.

Anomaly detection is an important task in hyperspectral imagery (HSI) processing. It provides a new way to find targets that have significant spectral differences from the majority of the dataset. Recently, the representation-based methods have been proposed for detecting anomaly targets in HSIs. It is essential for this type of method to construct a valid background dictionary to distinguish anomaly and background accurately. In this paper, a novel hyperspectral anomaly detection method based on background estimation and adaptive weighted sparse representation has been proposed. Firstly, to obtain the effective background dictionary without anomaly information, a new background dictionary construction strategy is designed. Secondly, the sparse representation based on the constructed background dictionary is utilized on the dataset. Anomalies and background are distinguished through the response of the residual matrix. Thirdly, the residual matrix is weighted adaptively from global and local domains, which makes anomalies and background more discriminative. An important advantage of the proposed method is that it considers the properties of anomalies in both spectral and spatial domains. Experiments on three HSI datasets reveal that our proposed method achieves an outstanding detection performance compared with the other anomaly detection algorithms.

In general, target localization in distributed multiple-input multiple-output (MIMO) radar systems requires synchronization among the sensors, which may not be feasible when the sensors are widely separated. In this paper, the target localization problem is addressed under asynchronous distributed MIMO radar systems in the presence of clock and frequency offsets with the aid of a cooperative target, whose position and velocity are known with measurement errors. Specifically, the influence of the cooperative target position and velocity errors on the unknown target localization accuracy is analyzed through the Cramer-Rao lower bound (CRLB) derivation firstly. Then two methods, namely, the offset estimation based localization method and the offset elimination based localization method, are proposed to estimate the unknown target location. The former estimates the clock and frequency offsets firstly and then locates the unknown target based on the offset estimates, and the latter achieves the unknown target location estimate based on the differential measurements of the two targets. Through the theoretical derivation and numerical simulations, the two proposed methods are demonstrated to be approximately unbiased and can attain the CRLB under mild noise. Moreover, the simulation results show that the proposed methods enjoy higher localization accuracy than the state-of-the-art algorithms.

Hyperspectral anomaly detection plays an important role in the field of remote sensing. It provides a way to distinguish interested targets from the background without any prior knowledge. The majority of pixels in the hyperspectral dataset belong to the background, and they can be well represented by several endmembers, so the background has a low-rank property. Anomalous targets usually account for a tiny part of the dataset, and they are considered to have a sparse property. Recently, the low-rank and sparse matrix decomposition (LRaSMD) technique has drawn great attention as a method for solving anomaly detection problems. In this letter, a new anomaly detection method based on LRaSMD and cluster weighting is proposed. We concentrate on the sparse part, which contains most of anomaly information, and calculate the initial anomaly matrix based on this part. To suppress background regions and discriminate anomalies from the background more distinctly, a weighting strategy in terms of the clustering result is used, and then the anomaly matrix is updated. The judgement of anomalies is made according to the responses on the matrix. Our proposed method considers the characteristics of anomalies from the spectral dimension and the spatial distribution simultaneously. Experiments on three hyperspectral datasets demonstrate the outstanding performance of the proposed method.

In this paper, a novel algebraic closed-form method is proposed to estimate the position and velocity of a moving target in distributed multiple-input multiple-output radar systems with erroneous sensor locations by utilizing time delay and Doppler shift measurements. Unlike the existing methods that introduce nuisance parameters to build the pseudo-linear equations, the proposed method uses the singular value decomposition approach to establish new linear equations with regard to the target location with no nuisance parameters, and then derives a weighted least-squares (WLS) solution. To further improve the localization accuracy, the solution is refined through estimating the error by another WLS estimator. Based on the theoretical derivation and numerical simulations, the proposed estimator is demonstrated to be approximately unbiased and can attain the CRLB under small noise conditions. Moreover, the simulation results show that the proposed method achieves better target location accuracy than the state-of-the-art algorithms.

Numerous hyperspectral anomaly detection (AD) methods suffer from complex background compositions and subpixel objects due to their inadequate Gaussian-distributed representations for nonhomogeneous backgrounds or their low discrimination between subpixel anomalies and the background. To alleviate these issues, a novel hyperspectral AD weighting strategy based on tensor decomposition and cluster weighting is proposed in this letter. Equipped with this simple but effective strategy as a postprocess, the detection performances of generic AD methods can be significantly boosted. In this strategy, Tucker decomposition is adopted to remove the major background information. A parameter-adaptive k-means clustering method is then applied on the decomposed anomaly/noise data cube to assemble homogeneous regions. After segmenting the clustering result into a number of nonoverlapping eight-connected domains, corresponding weights are assigned to large domains according to an improved Gaussian weight function. Finally, the resulting weight matrix is multiplied by the results of the detectors to achieve a performance boost. Experiments on two hyperspectral data sets validate the effectiveness of the proposed strategy.