Signal extraction using Compressed Sensing for passive radar with OFDM signals

Passive radar is a concept where possibly multiple non-cooperative illuminators are used in a multi-static setup. A freely available signal, like radio or television, is decoded and used to identify moving airborne targets based on their Doppler shift. New digital signals, like Digital Audio/Video Broadcast (DAB/DVB), are excellent candidates for this scheme, as they are widely available, can be easily decoded, and employ orthogonal frequency division multiplex (OFDM), a multicarrier transmission scheme based on channel equalization in the frequency domain using the Fast Fourier Transform (FFT). After successfully decoding the digital broadcast, the channel estimates can be used to estimate targets’ bi-static range and range-rate by separating different multi-path components by their delay and Doppler shift. While previous schemes have simply projected available measurements onto possible Doppler shifts, we employ Compressed Sensing, a type of sparse estimation. This way we can enhance separation between targets, and bypass additional signal processing necessary to determine the actual target within a “blotch” of signal energy smeared across different delays and Doppler frequencies.