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In this paper, we consider an exponentially windowed sample covariance matrix (EWSCM) and propose an improved estimator for its eigenvalues. We use new advances in random matrix theory, which describe the limiting spectral distribution of the large dimensional doubly correlated Wishart matrices to find the support and distribution of the eigenvalues of the EWSCM. We then employ the complex integration and residue theorem to design an estimator for the eigenvalues, which satisfies the cluster separability condition, assuming that the eigenvalue multiplicities are known. We show that the proposed estimator is consistent in the asymptotic regime and has good performance in finite sample size situations. Simulation results show that the proposed estimator outperforms the traditional estimator, significantly. [ABSTRACT FROM PUBLISHER]

We propose a novel robust design method to jointly optimize the radar transmit code and receive filter, exploiting the Signal-to-Interference plus Noise Ratio (SINR) at the receiver end as design figure of merit. We confer robustness to our method against uncertainties on the target impulse response (TIR) using a worst-case optimization approach based on two different uncertainty sets. The former is composed of a finite collection of TIRs, obtained by sampling the actual TIR at some aspect angles; the latter is a spherical uncertainty set. We further enforce a peak-to-average power ratio (PAR) constraint to the transmit code, which is very important for radar applications where the transmitter operates close to saturation. The design problem is tackled using a sequential optimization procedure alternating between two semi-definite programming (SDP) problems, followed by randomization steps. Our numerical results highlight the robustness and applicability of the proposed method in different scenarios. [ABSTRACT FROM PUBLISHER]

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. Even the performance of the most of the existing robust adaptive beamformers is degraded when the signal‐to‐noise ratio (SNR) is increased. In this study, a high converging rate robust adaptive beamformer is proposed. This method is a promoted eigenspace‐based beamformer. In this paper, a new signal‐plus‐interferences (SPI) covariance matrix estimator is proposed. The subspace of the ideal SPI covariance matrices is exploited and the estimated covariance matrix is projected into this subspace. This projection effectively reduces the covariance matrix estimation error and the proposed estimator yields a more accurate estimation of the SPI covariance matrix. In addition, a computationally efficient steering vector estimator has been proposed. To prevent the absence of the DS steering vector in the estimated SPI subspace, the estimated SPI covariance matrix is compensated. Hence, the proposed method can attain the optimal beamformer in the both high and low SNR cases. The numerical examples indicate that this method has excellent signal‐to‐interference plus noise ratio performance and offers a higher converging rate compared with the existing robust adaptive beamforming algorithms. [ABSTRACT FROM AUTHOR]

The presence of the desired signal (DS) in the training snapshots makes the adaptive beamformer sensitive to any steering vector mismatch and dramatically reduces the convergence rate. The objective of the present study is to propose a new adaptive beamformer which is robust against direction‐of‐arrival (DOA) mismatch and its convergence rate is not sensitive to the presence of the DS. This method is applicable to the arrays with specific structure such as the linear array. Our approach is based on the DS elimination from the training snapshots and the sub‐array beamforming technique. To accomplish this goal, a blocking matrix which converts the primary data to the DS‐free data is synthesized. The Synthesis process is based on the desired degree of freedom and the uncertainty of DOA of the DS. Using the signal‐free data, the beamforming vector is calculated through the presented algorithm. Owing to elimination of the DS from the training snapshots and performing the adaptive operations in the sub‐array level, our algorithm has high convergence rate and excellent performance even in cases with the small sample size. Simulations show that the proposed beamformer can achieve a much better performance in terms of output SINR compared to the existing ones. [ABSTRACT FROM AUTHOR]

Presents a study which proposed an algorithm for the estimation of central angle and angular extension of distributed sources in array signal processing. Data model; Description of the distributed source parameter estimator algorithm for both coherently distributed and incoherently distributed source models; Simulation results and conclusion.

Presents a study which showed a constant false alarm rate (CFAR) processor that exhibits a noticeable detection performance in the presence of interfering targets. Assumptions and statistical models; Simulation results; Conclusion.