Passive wireless sensors provide an attractive technology in niche applications where battery powered sensors are not applicable. Surface acoustic wave technology provides an optimum implementation of passive wireless transducers acting as cooperative targets to short range radar systems: the signal carrying the information is delayed beyond clutter, with a piezoelectric substrate converting the electromagnetic wave to an acoustic wave whose properties are dependent on the environment. We have tackled the issue of short range radar certification by considering a passive radar approach in which existing non-cooperative radiofrequency sources are used to power the sensor, and the reader is made solely of passive receivers aimed at recording the reference signal generated by the non-cooperative source and the backscattered signal returned by the sensor. A passive radar approach only requires a cross-correlation between the reference and surveillance signals to identify the time delay between the incoming and backscattered signals and hence the recovery of the physical quantity sensed by the acoustic transducer through acoustic velocity modulation. Practical sources do exhibit some short term correlation. Hence, strong copies of the reference signal with delays shorter than the one introduced by the sensor must be canceled by the receiver to allow for the weak sensor response to be extracted. This classical problem is called Direct Signal Interference (DSI) suppression. In a post-processing approach with little computation time limitation, this problem is solved using a least square error optimization approach. In the context of real time sensor measurement using a Field Programmable Gate Array (FPGA) implementation, data recording, frequency transposition and decimation are readily implemented in the gate array matrix. DSI removal appears as the limiting factor for a full FPGA implementation of the short range passive radar reader. We address the challenge by the iterative process of DSI suppression using Orthogonal Matching Pursuit (OMP) algorithm, and additionnally consider the FPGAfriendly Stochastic Gradient Descent (SGD) approach to try to recover DSI coefficients from a pipelined algorithm using streams of measurement data.