This paper addresses the source association (SA), direction of arrival (DOA), and fading coefficients (FCs) estimation problem in multipath environment. First, we establish a rank reduction property for a multipath signal model with the existence of multiple groups of coherent signals. Subsequently, based on this property, effective algorithms for SA, DOA, and FCs estimation have been developed. The proposed DOA and FCs estimation methods exploit the multipath structure information to achieve improved accuracy. The new DOA estimation methods work well even in the case that the DOAs of the multipath signals associated with different sources are (nearly) overlapped. Meanwhile, the new methods are applicable to arbitrary array geometry while without decreasing the effective array aperture. Then, the stochastic Cramér-Rao bound on DOA and FCs estimation of multipath model (MCRB) exploiting the multipath structure information is derived in closed form. Numerical simulations have been provided to demonstrate the effectiveness of the proposed methods.

This paper develops a unified solution for time-difference-of-arrival (TDOA) localization in the presence of sensor position errors. This technique starts with maximum likelihood estimation (MLE), which is known to be nonconvex. A semidefinite programming technique to effectively transform the MLE problem into a convex optimization is proposed, together with a unified solution for four scenarios: 1) without a calibration emitter; 2) with a single calibration emitter, whose position is subject to measurement errors; 3) with a single calibration emitter, whose position is perfectly known; and 4) with a single calibration emitter, whose position is completely unknown. The results are finally extended to the case of multiple calibration emitters, whose positions are also subject to errors. Similar to the existing schemes that are known to have good performances, the proposed solution also reaches the Cramer-Rao lower bound when sensor position errors and TDOA measurement noise are sufficiently small. However, as TDOA measurement noise or sensor position errors increase, comparison with the existing state-of-the-art methods for each scenario shows that the proposed solution performs significantly better.

The problem of direction-of-arrival (DOA) estimation for noncircular sources impinging on a central symmetric array (CSA) in the presence of sensor gain-phase uncertainties is addressed in this paper. A noniterative method is proposed and the corresponding stochastic Cramér-Rao bound is derived. The proposed method is realized through two steps. First, an eigenstructure-based technique is presented to estimate the spatial signatures. Second, the DOAs are obtained by adopting an element-wise division approach to the estimated spatial signatures, based on which, the sensor gain-phase errors are given in closed-form. The ambiguity of DOA estimation is analyzed as well. The proposed method offers a number of advantages in comparison with the existing methods that apply to CSA. First, the DOA estimator is independent of the sensor phases. Second, the proposed method applies to incoherent sources. Third, the proposed method is capable of providing 360° azimuthal coverage under certain conditions. Fourth, an additional performance gain is achieved by taking the property of noncircular sources into consideration. Numerical simulations are provided to verify the effectiveness of the proposed method.

This paper addresses the problem of direction-of-arrival (DOA) estimation for cyclostationary signals using less reception channel in comparison with the number of sensors. The system is considered to be realized by using the reception channel(s) to receive the signal reached each sensor in turn. Such simplification results in a cost reduction of the system and the effect of inconsistency among different channels are removed. However, non-synchronized sampling also makes the traditional DOA estimation algorithms ineffective. To cope with the problem, the signal models for DOA estimation using a single channel and dual channels are formulated based on signal cyclostationary, and the single channel cyclic MUSIC (SC-Cyclic-MUSIC) algorithm and the dual channels cyclic MUSIC (DC-Cyclic-MUSIC) algorithm are proposed correspondingly. This paper shows that both SC-Cyclic-MUSIC algorithm and DC-Cyclic-MUSIC algorithm work well when the switching interval is known, and a satisfied performance can also be obtained by DC-Cyclic-MUSIC algorithm when the uncertainty of the switching interval exists. Moreover, the proposed estimators are suitable for both narrow-band and wide-band signals. The computer simulations are provided to verify the effectiveness of the proposed algorithms.

For non-circular signals, the existing robust widely linear (WL) beamforming algorithms either suffer from performance degradation at high signal-to-noise ratio or have high computational complexities. In order to cope with this problem, three low-complexity robust WL beamformers, i.e., the WL-IQCQP, the WL-RBLW, and the WL-OAS, are proposed in this paper. First, based on the spatial spectrum of non-circularity coefficient, the augmented interference-plus-noise covariance matrix is reconstructed to remove the signal-of-interest’s component from the augmented sample covariance matrix. Then, the method of iterative quadratically constrained quadratic programming, the modified Rao-Blackwell Ledoit-Wolf estimator, and oracle approximating shrinkage estimator are developed, respectively, to estimate the desired signal’s extended steering vector. Only the prior knowledge of the antenna array geometry and the angular sector in which the desired signal is located are utilized in the proposed algorithms. Compared with several representative robust WL beamformers, numerical simulations demonstrate that the proposed beamformers can achieve better performances.

Three novel adaptive beamforming algorithms are developed to mitigate the steering vector (SV) random errors of both the desired signal and interferences. With the use of iterative robust Capon beamforming technique, each interference's SV is estimated first, and then the interference‐plus‐noise covariance matrix is reconstructed according to its definition. To accurately estimate the signal of interest's (SOI's) SV, the authors introduce three new methods, which are based on the techniques of the second‐order cone programming, the eigendecomposition and the oracle approximating shrinkage. For the proposed beamformers, only the prior information about the array geometry and the SOI's angular sector are needed. The main advantage is that the proposed methods are robust against a variety of SV mismatches. Simulation results are presented to demonstrate the robustness and effectiveness of the proposed adaptive beamformers.