In this paper, we propose an online antenna-pulse selection method in space time adaptive processing, while maintaining considerable performance and low computational complexity. The proposed method considers the antenna-pulse selection and covariance matrix estimation at the same time by exploiting the structured clutter covariance matrix. Such prior knowledge can enhance the covariance matrix estimation accuracy and thus can provide a better objective function for antenna-pulse selection. Simulations also validate the effectiveness of the proposed method.

This letter addresses the problem of space-time adaptive processing (STAP) for airborne nonuniform linear array (NLA) radar using a generalized sidelobe canceller (GSC). Due to the difficulty of determining the spatial nulls for the NLAs, it is a problem to obtain a valid blocking matrix (BM) of the GSC directly. In order to solve this problem and improve the STAP performance, a BM modification method based on the modified Gram-Schmidt orthogonalization algorithm is proposed. The modified GSC processor can achieve the optimal STAP performance and as well a faster convergence rate than the orthogonal subspace projection method. Numerical simulations validate the effectiveness of the proposed methods.

The issue of robust multi-input multi-output (MIMO) radar waveform design is investigated in the presence of imperfect clutter prior knowledge to improve the worst-case detection performance of space-time adaptive processing (STAP). Robust design is needed because waveform design is often sensitive to uncertainties in the initial parameter estimates. Following the min-max approach, a robust waveform covariance matrix (WCM) design is formulated in this work with the criterion of maximization of the worst-case output signal-interference-noise-ratio (SINR) under the constraint of the initial parameter estimation errors to ease this sensitivity systematically and thus improve the robustness of the detection performance to the uncertainties in the initial parameter estimates. To tackle the resultant complicated and nonlinear robust waveform optimization issue, a new diagonal loading (DL) based iterative approach is developed, in which the inner and outer optimization problems can be relaxed to convex problems by using DL method, and hence both of them can be solved very effectively. As compared to the non-robust method and uncorrelated waveforms, numerical simulations show that the proposed method can improve the robustness of the detection performance of STAP.

Most images obtained with imaging sensors contain Bayer patterns and suffer from blurring caused by the lens. In order to convert a blurred Bayer-patterned image into a viewable image, demosaicing and deblurring are needed. These concepts have been major research areas in digital image processing for several decades. Despite their importance, their performance and efficiency are not satisfactory when considered independently. In this paper, we propose a joint deblurring and demosaicing method in which edge direction and edge strength are estimated in the Bayer domain and then edge adaptive deblurring and edge-oriented interpolation are performed simultaneously from the estimated edge information. Experimental results show that the proposed method produces better image quality than conventional algorithms in both objective and subjective terms.

The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.

A hot clutter mitigation algorithm based on Subbanding and Space Fast-time Adaptive Processing (Fast-time STAP) for Multi-channel Synthetic Aperture Radar (MSAR) is analyzed, and is compared with the method based on just fast-time STAP. Simulation results demonstrate that the method based on subbanding and fast-time STAP performs better than the method based on just fast-time STAP in hot clutter mitigation for MSAR.

In this letter, we first investigate the bias of Doppler shift estimator based on autocorrelation function (ACF). Then we derive a signal-to-noise ratio (SNR) independent condition for Doppler shift estimation and achieve this condition by a adaptive process. Moreover, we present theoretical analysis about the convergency of our adaptive Doppler shift estimator, and derive a close-form expression for its mean square error (MSE). We verify the proposed estimator by computer simulation, the results of which are in agreement with the analysis, i.e., the proposed method achieves a good SNR-independent performance in a wide range of velocities and SNRs.

In this paper we discuss an adaptive process, which is based on the so-called self-tuning mechanism. We simplify this mechanism and apply it to a threshold system. From view points of information quantity and estimation accuracy we show this mechanism enhances information transmission through the threshold system. In addition we extend our theory so that it could be applied to a truncation coding.

A new algorithm and architecture to eliminate redundant operations in block-matching (BM) motion estimation is proposed. The key step of this work is to use binary-matching to define image regions with the static background content and then exclude these regions from the actual motion estimation. According to experiments, the approach maintains the highest PSNR, while making as half as less computations in comparison to the adaptive BM or 1/8 of the computations required by the full-search BM. An implementation scheme is outlined.

Several subband array methods have been proposed as useful means to perform joint spatio-temporal equalization in digital mobile communications. These methods can be applied to mitigate problems caused by the inter-symbol interference (ISI) and co-channel interference (CCI). The subband array methods proposed so far can be classified into two major schemes: (1) a centralized feedback scheme and (2) a localized feedback scheme. In this paper, we propose subband arrays with partial feedback scheme, which generalize the above two feedback schemes. The main contribution of this paper is to derive the steady-state mean square error (MSE) performance of subband arrays implementing these three different feedback schemes. Unlike the centralized feedback scheme which can be designed to provide the optimum equalization performance, the subband arrays with localized and partial feedback schemes are in general suboptimal. The performance of these two suboptimal feedback schemes depends on the channel characteristics, the filter banks employed, and the number of subbands.