An Efficient Angular Separation Maximization Antenna Selection Scheme for High Precision Fingerprint-Based Indoor Positioning

High-precision indoor positioning based on fingerprint usually requires more observation samples, either time or frequency. Our previous work explores the possibility of enlarging the observation window in the spatial domain, which requires significant baseband and radio frequency (RF) processing capabilities to deal with multiple antennas. In this paper, we propose a low-complexity antenna selection-based positioning scheme in order to trade off the positioning performance and hardware cost or computational complexity. The optimal antenna subset is selected with the help of the theoretical error-bound analysis, which ensures robust and high-precision positioning performance. Based on the numerical results, we show that our proposed antenna selection mechanism is able to reserve the positioning precision improvement provided by the spatial domain observations and reduce more than 100-200 times implementation complexity in terms of running times, if compared with other baseline selection algorithms.