OPTIMIZATION OF ADAPTIVE DIRECT SIGNAL SUPPRESSION FOR SINGLE-CHANNEL PASSIVE RADAR SENSING

This research aims to optimize a previously developed direct signal suppression (DSS) algorithm for single-channel passive radars that use radio-astronomical sources (e.g., the Sun and Jupiter's radio emissions) as ambient noise. Such passive radars can be used in extreme environments such as polar regions to measure ice sheet thickness and space-based experiments as a low-resource solution. One possible path to optimize DSS algorithms is by accurately estimating the direct signal and performing the Wiener deconvolution. This requires constructing the impulse response function, where the two most critical parameters are the echo peak power (alpha) and delay time (iota). Although only a minor increase in signal to noise ratio (SNR) was achieved compared to the previous approach, our results highlight the importance of correctly estimating the phase of the impulse response function. We observe significant losses in SNR if the phase information is not accurately estimated; while, on the other hand, the amplitude of the impulse response has a negligible impact on SNR compared to the correct phase estimation.