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This study proposes novel sparsity-aware space-time adaptive processing (SA-STAP) algorithms with L1-norm regularisation for airborne phased-array radar applications. The proposed SA-STAP algorithms suppose that a number of samples of the full-rank STAP datacube are not meaningful for processing and the optimal full-rank STAP filter weight vector is sparse, or nearly sparse. The core idea of the proposed method is imposing a sparse regularisation (L1-norm type) to the minimum variance STAP cost function. Under some reasonable assumptions, the authors firstly propose an L1-based sample matrix inversion to compute the optimal filter weight vector. However, it is impractical because of its matrix inversion, which requires a high computational cost when using a large phased-array antenna. In order to compute the STAP parameters in a cost-effective way, the authors devise low-complexity algorithms based on conjugate gradient techniques. A computational complexity comparison with the existing algorithms and an analysis of the proposed algorithms are conducted. Simulation results with both simulated and the Mountain-Top data demonstrate that fast signal-to-interference-plus-noise-ratio convergence and good performance of the proposed algorithms are achieved. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a novel successive interference cancellation (SIC) strategy for multiple-input multiple-output spatial multiplexing systems based on a structure with multiple interference cancellation branches. The proposed multi-branch SIC (MB-SIC) structure employs multiple SIC schemes in parallel and each branch detects the signal according to its respective ordering pattern. By selecting the branch which yields the estimates with the best performance according to the selection rule, the MB-SIC detector, therefore, achieves higher detection diversity. The authors consider three selection rules for the proposed detector, namely, the maximum likelihood (ML), the minimum mean square error and the constant modulus criteria. An efficient adaptive receiver is developed to update the filter weight vectors and estimate the channel using the recursive least squares algorithm. Furthermore a bit error probability performance analysis is carried out. The simulation results reveal that the authors' scheme successfully mitigates the error propagation and approaches the performance of the optimal ML detector, while requiring a significantly lower complexity than the ML and sphere decoder detectors. [ABSTRACT FROM AUTHOR]

A blind space-time joint channel and direction of arrival (DoA) estimation method is proposed for DS-CDMA systems. The new method employs a linear antenna array and requires only the signature sequence and timing of the desired user to blindly estimate the channel and the DoA parameters. Two blind algorithms that exploit antenna arrays are also proposed for enhancing the channel estimates. Simulations show the improvements for channel estimation of the method and algorithms over existing techniques and the accuracy achieved for DoA computation as compared to recently reported DoA estimators for DS-CDMA systems. [ABSTRACT FROM AUTHOR]

This article proposes constrained adaptive algorithms based on the conjugate gradient (CG) method for adaptive beamforming. The proposed algorithms are derived for the implementation of the beamformer according to the minimum variance and constant modulus criteria subject to a constraint on the array response. A CG-based weight vector strategy is created for enforcing the constraint and computing the weight expressions. The devised algorithms avoid the covariance matrix inversion and exhibit fast convergence with low complexity. A complexity analysis compares the proposed algorithms with the existing ones. The convergence properties of the CCM criterion are studied, conditions for convexity are established and a convergence analysis for the proposed algorithms is derived. Simulation results are conducted for both stationary and non-stationary scenarios, showing the convergence and tracking ability of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Here, the authors propose two adaptive detection schemes based on single-carrier frequency-domain equalisation (SC-FDE) for multiuser direct-sequence ultra-wideband systems, which are termed structured channel estimation (SCE) and direct adaptation (DA). Both schemes use the minimum mean square error (MMSE) linear detection strategy and employ a cyclic prefix. In the SCE scheme, adaptive channel estimation is performed in the frequency domain and the despreading is implemented in the time domain after the FDE. In this scheme, the MMSE detection requires the knowledge of the number of users and the noise variance. For this purpose, simple algorithms are proposed for estimating these parameters. In the DA scheme, the interference suppression task is fulfilled with only one adaptive filter in the frequency domain and a new signal expression is adopted to simplify the design of such a filter. Least mean squares, recursive least squares and conjugate gradient adaptive algorithms are then developed for both schemes. A complexity analysis compares the computational complexity of the proposed algorithms and schemes, and simulation results for the downlink illustrate their performance. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents reduced-rank linearly constrained minimum variance (LCMV) beamforming algorithms based on joint iterative optimization of filters. The proposed reduced-rank scheme is based on a constrained joint iterative optimization of filters according to the minimum variance criterion. The proposed optimization procedure adjusts the parameters of a projection matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. We describe LCMV expressions for the design of the projection matrix and the reduced-rank filter. We then describe stochastic gradient and develop recursive least-squares adaptive algorithms for their efficient implementation along with automatic rank selection techniques. An analysis of the stability and the convergence properties of the proposed algorithms is presented and semi-analytical expressions are derived for predicting their mean squared error (MSE) performance. Simulations for a beamforming application show that the proposed scheme and algorithms outperform in convergence and tracking the existing full-rank and reduced-rank algorithms while requiring comparable complexity. [Copyright &y& Elsevier]