High-Speed Cross-Correlation for Spectrum Sensing and Direction Finding of Time-Varying Signals

Cross-correlation is a promising method for highly sensitive spectrum sensing. By computing the cross-power spectrum from a pair of sensors and performing time averaging, significant reduction of noise can be achieved, even between incoherent sensors. However, this improvement requires a large number of time-averages and is thus not useful for sensing of time-varying electromagnetic environments. To alleviate this long sample-time requirement, we propose using more than two sensors to capture data and cross-correlate between all possible sensor-pair combinations. This exchanges a long sample-time requirement for more sensors to achieve a given spur-free dynamic range, thus taking advantage of a large number of easily obtained low-cost software-defined radios. We quantify the rate of change of spur-free dynamic range (SFDR) due to attenuating a sensor as well as the rate of change of SFDR for collections of sensors. Then, we show that the proposed approach is effective in detecting time varying signals. Finally, we detail a modification to the method which allows for time-varying, angle-dependent sensing, and results in a new approach for direction finding based upon cooperative cross-correlation.