Reference-free amplitude-based WiFi passive sensing

The parasitic exploitation of WiFi signals for passive sensing purposes is a topic that is attracting considerable interest in the scientific community. In an attempt at meeting the requirements for sensor compactness, easy deployment, and low cost, we resort to a non-coherent signal processing scheme that does not rely on the availability of a reference signal and relaxes the constraints on the sensor hardware implementation. Specifically, with the proposed strategy, the presence of a moving target echo is determined by detecting the amplitude modulation that it produces on the direct signal transmitted from the WiFi access point. We investigate the target discrimination capability of the resulting sensor against the competing interference background and we theoretically characterize the impact of undesired amplitude fluctuations in the received signal that are determined by causes other than the superposition of the target echo, thereby including the waveform properties. Hence, we propose different solutions to address the limitations identified, characterized by different complexities, and we investigate their advantages and drawbacks. The conceived signal processing schemes are thoroughly validated on both simulated and experimental data, collected in different operational scenarios

Human and drone surveillance via RpF-based WiFi passive radar. Experimental validation

This work deals with the short-range monitoring of small radar cross section targets using commercial WiFi transmitters as source of opportunity. Proper modifications to the conventional WiFi-based passive radar signal processing scheme are presented, based on the use of the Reciprocal Filter (RpF) approach for range compression, that (i) simplify the overall scheme (ii) make the processing robust to current and future IEEE 802.11 standards that might be characterized by mixed modulation transmissions (iii) result in a better sidelobe control (iv) enable an easier and cost-effective clutter cancellation strategy. Then, we introduce an appropriate solution to tackle the signal-to-noise ratio loss introduced by the presented strategy. The effectiveness of the resulting processing scheme is tested on experimental data, collected in both the 2.4 and 5 GHz WiFi bands, against both humans and a small drone