Robust Adaptive Beamforming against Signal Steering Vector Mismatch and Jammer Motion

Abstract

Since adaptive beamformer suffers from output performance degradation in the presence of interference nonstationarity and signal steering vector mismatch, a novel robust null broadening adaptive beamforming is proposed. The proposed method is realized by the combination of projection transform and diagonal loading techniques. First, a new projection matrix with null broadening ability is constructed and then projects the array received data onto the projection matrix. With the diagonal loading technique, a new sample covariance matrix is obtained. The theoretical analysis shows that the projection transform operation can expand the incident direction of the interference and improve orthogonality between the signal-plus-interference and the noise subspaces; thus the proposed beamformer can effectively broaden the jammer null and enhance the null depth. The analytical expressions of the proposed algorithm are also provided, which are efficient and easily solved. Simulation results are presented and demonstrated that the proposed beamformer can provide strong robustness against signal steering vector mismatch and jammer motion.

Adaptive Antenna Null Broadening Beamforming against Array Calibration Error Based on Adaptive Variable Diagonal Loading

“…The broad nulls can be formed by the PDL approach [13] through projection. The correlation matrix of the steering vector is constructed in the predefined null broadening sectors as…”

“…There exist many adaptive antenna null broadening beamforming approaches in literature [6][7][8][9][10][11][12][13][14]. Covariance matrix taper (CMT) [6][7][8][9] is a classical and widespread approach of null broadening.…”

“…The nonlinear quadratic constraint is transformed into a set of linear constraints; hence the solving process is greatly simplified. A null broadening beamforming approach based on projection and diagonal loading (PDL) is proposed in [13], where the CMT and projection are combined. The null depth of the PDL approach is deeper than that of the CMT approach, and the computational complexity is low.…”

“…The approaches of LCSS [12], PDL [13], and CMRSC [14] are all proposed in recent years. Without array calibration error, deeper broad nulls and higher output signal-to-interference-plus-noise ratio (SINR) can be obtained by virtue of these approaches in comparison with the CMT approach.…”

“…The robustness of adaptive antenna beamforming can be effectively improved by DL with a proper loading factor that is difficult to achieve [17,18]. The CMT approach always uses a small fixed loading factor [9,13,14], so its robustness against the array calibration error is very limited. In order to improve the robustness of adaptive antenna null broadening beamforming against array calibration error, an adaptive variable diagonal loading based covariance matrix taper (VDL-CMT) null broadening beamforming approach is put forward.…”