Subarray selection for adaptive array signal processing in GNSS applications

Radio Frequency Interference (RFI) is a significant threat to the successful operation of Global Navigation Satellite Systems (GNSS) receivers Thus adaptive antenna arrays have been proposed to mitigate broadband interference and multipath in GNSS applications However the high cost, in terms of both hardware and computational load, of a large array makes adaptive array processing a luxury for civilian GNSS receivers In order to reduce the high cost while preserving the performance, we propose in this paper a reconfigurable adaptive antenna array strategy, where we "choose K from N antennas" that are then connected to the following front-ends and beamforming network Then the corresponding beamforming weight vector is developed based on the chosen subarray to obtain the maximum interference suppression adaptively The Spatial Correlation Coefficient (SCC) is introduced in this paper to characterize the effect of the array configuration on the processing performance Subarray selection in terms of minimizing the SCC is an NP-hard combinatorial optimization problem In this paper we adopt a reweighted l₁ -norm regularization method to select the subarray As we seek a binary solution, we modify this method in order to satisfy binary entry requirement of the sparse solution The experimental results have demonstrated the effectiveness and efficiency of the proposed method The focus of this paper is on implementing our proposed subarray selection strategy in a real GPS experiment Firstly the arrival directions of both satellite signals and the interference are estimated and then an adaptive antenna array is utilized to suppress the strong interference and enhance the cross correlation performance Secondly, the proposed l₁ -norm method is employed to choose an optimum subarray The effective carrier to noise density ratio (effective C/N₀) is adopted as a metric to measure the performance and the trade-off curve is utilized to show the compromise between the performance and the cost Experimental results demonstrate that SCC is a convenient parameter to characterize the impact of array configuration on the adaptive array processing performance The utility of adaptive antenna array reconfiguration in order to achieve minimum cost with maximum preserved performance is also proved.