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 l_1 -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.
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