Low Cost Adaptive Array Signal Processing by Subarray Selection

Adaptive antenna arrays have been proposed to mitigate strong interference and multipath in Global Navigation Satellite Systems (GNSS) receivers. However, the high cost of an entire front-end and limited signal processing resources make large (or oversampled) antenna array a luxury for GNSS receivers. Therefore, a low cost adaptive array processing strategy by subarray selection is proposed in this work, while with maximum preserved performance. There are only K front-ends installed in the receiver, thus the hardware cost is reduced dramatically. An optimum K-antenna subarray is adaptively selected from N candidates using switches and then connected to the following signal processing network. A parameter defined as Spatial Correlation Coefficient (SCC) is introduced to characterise the spatial separation between the satellite signal and the interference and used as a cost function to calculate the optimum subarray in terms of minimization problem. The relationship between the effective carrier to noise density ratio (effective 0 / C N ) and SCC is formulated as a closed formula. This formula shows the effective 0 / C N depends on both the number of selected antennas K and the SCC value, and thus there is no linear relationship between the effective 0 / C N and K. The optimum K-antenna subarray can guarantee negligible performance loss when the satellite signal and the interference are close in space, while no more than 10 10log / N K dB/Hz when the two are well separated. An 8-antenna circular array is used to collect the data in our experiment and only the data received from selected antennas is processed. Experimental results show that (1) SCC is an effective parameter for characterising the effect of array configuration on the performance; (2) The optimum subarray can reduce the hardware cost dramatically with maximum preserved performance; (3) The optimum subarray can achieve the best performance given the fixed hardware cost.