Null/Optimum Point Optimization for Indoor Passive Radar Motion Sensing

Abstract

Indoor passive radar has gained traction as a method for measuring small-amplitude motions without requiring a cooperative signal to be transmitted by the sensor. Ubiquitous signals such as Wi-Fi and Bluetooth may be used as illuminators of opportunity in order to measure the motion of various targets. Both the direct, unmodulated signal as well as the Doppler-shifted signal are received at the radar and are used for down-conversion to baseband. Since there is no cooperative local oscillator used in passive radar, it is not currently possible to effectively extract both the $I$ and $Q$ channel data making null-point detection a returning problem. In this work, the null-point detection problem is analyzed theoretically to develop a simulation model for passive radar sensing. Using this model, an in-depth analysis is undertaken in order to determine the effectiveness of methods such as channel selection, frequency tuning, or multi-band/multi-static sensing in removing or mitigating the null-point detection problem. The results demonstrate that despite the presence of the null-point issue, it is possible to reduce its impact on motion detection and optimize the detection sensitivity.