Passive Sensing Using Multiple Types of Communication Signal Waveforms for Internet of Everything

Passive sensing using communication signal waveforms is considered to be a promising technology for target monitoring in Internet of Everything. Conventional passive sensing schemes require accurate estimation of the time difference of arrival (TDOA) and frequency difference of arrival (FDOA), which is leading to high complexity but low accuracy. In this article, a robust passive sensing algorithm using multiple illumination of opportunities is proposed to improve the detection performance while avoiding separate estimation of TDOA and FDOA. The proposed method first combines the linear constrained minimum variance adaptive filter with the wide nulling algorithm to achieve target direction finding while separating the direct wave and suppressing multipath interference. Then, the linear canonical transformation-based cross ambiguity function (LCTCAF) is employed to estimate the distance and radial velocity of the target. Relying on the relationship between distance to time and velocity to Doppler, a distance-velocity transformation-based cross ambiguity function (DVCAF) is introduced to characterize the distance and radial velocity of the target. Finally, a spectral peak search scheme is exploited in DVCAF to estimate the time delay and Doppler shift so as to identify the target parameters directly. Its’ Cramer–Rao low bound is derived. Simulation results validate that the performance of the proposed algorithm outperforms the conventional estimators based on the cross ambiguity function.