摘要 : Receiver nonlinearities pose a serious risk to the functionality of modern radars as they can compromise the sensor’s immunity to interfering signals. With the radio frequency (RF) spectrum becoming increasingly crowded, it is now... 展开 Receiver nonlinearities pose a serious risk to the functionality of modern radars as they can compromise the sensor’s immunity to interfering signals. With the radio frequency (RF) spectrum becoming increasingly crowded, it is now more important than ever that the sensor can maintain system performance when exposed to mutual interference. In this article, we present a nonlinear compressive sensing (NCS) solution, which, unlike the standard nonlinear equalization (NLEQ) techniques, is designed around the forward nonlinearity rather than the inverse. Importantly, in this article, the NCS theory is extended to include nonlinear memory. Furthermore, a radar-specific formalization is derived, which allows the nonlinear optimization to exploit the unique properties of pulsed-Doppler radar processing. As a result, the NCS solution can successfully restore system sensitivity back to the linear case when in-band interference drives the radar receiver into its nonlinear regime. Additionally, it is shown that the technique can consistently mitigate complex nonlinear memory effects generated in the RF receiver. This is a significant result as it proves that forward modeling techniques are a viable alternative to NLEQ. This is of particular importance to radar systems as they provide a far more explicit formalization to mitigate nonlinear memory effects. 收起