Abstract
Widely linear (WL) adaptive beamforming algorithms are proposed based on constrained minimum variance (CMV) and constrained constant modulus (CCM) criteria, respectively, to fully exploit both the desired signal and interferences' noncircularity. Modified conjugate gradient (MCG) algorithm is employed to ensure convergence with one iteration per sample. To further facilitate the adaptive processing, CCM criterion based WL beamforming algorithm is modified by second-order local approximation. Global convergence of the proposed algorithms is analyzed and constraints of parameters are given. Results of numerical simulations demonstrate that the proposed WL CCM beamformer has superior performance over WL CMV beamformer and conventional beamformers in convergence rate, robustness against direction of arrival (DOA) mismatch, and the noncircularity coefficient estimation error, while the former two have comparable low complexities without numerical instability.
Highlights
Distributed sensor networks (DSNs) with independent sensing, processing, and distributed communication capabilities are widely used in field of climate, environment, and disaster monitoring, logistics and assets tracking, machine to machine (M2M) communication, and enclosures antiintrusion [1]
Under the framework of widely linear (WL) beamforming, novel WL beamformers with modified conjugate gradient (CG) algorithm based on constrained minimum variance (CMV) and constrained constant modulus (CCM) criteria are proposed in this paper, respectively
The results demonstrate that the CCMbased beamforming algorithms especially the WL-CCMMCG algorithm have good robustness against the change of interference environments
Summary
Distributed sensor networks (DSNs) with independent sensing, processing, and distributed communication capabilities are widely used in field of climate, environment, and disaster monitoring, logistics and assets tracking, machine to machine (M2M) communication, and enclosures antiintrusion [1]. The steering vector mismatch of the desired signal introduced by platform perturbation, which will rigorously degrade the performance of the beamformer, should be considered To overcome this problem, several excellent methods have been proposed [27, 28]. In [28] the spatial spectrum of noncircularity coefficient is first introduced, and the robustness against steering vector mismatch is obtained by exploiting the reconstruction of the augmented interference-plus-noise covariance matrix. The drawback of this method still lies in its high complexity introduced by the augmented dimension.
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