Joint specific emitter identification and tracking using device nonlinearity estimation

In this thesis, we present radio frequency (RF) front-end nonlinearity estimators to perform joint specific emitter identification (SEI) and tracking. Our SEI systems discern radio emitters of interest through the estimation of transmitter nonlinearities caused by design and fabrication variations. These nonlinearity features provide unique signal signatures for each emitter, and we extract those characteristics through the estimation of transmitter nonlinearity coefficients. We first present a nonlinearity estimator which estimates the power series coefficients of nonlinear devices in the radio frequency (RF) front end by observing the spectral regrowth in additive white Gaussian noise (AWGN) channel. Then another robust algorithm is also provided by using alternative degrees of nonlinearities associated with symbol amplitudes for initial estimation, and then iteratively estimating the channel coefficients and distorted transmit symbols to overcome the inter-symbol interference (ISI) effect. The convergence and unbiasedness of the iterative estimator are demonstrated semi-analytically. Based on this analysis, we also trade error performance for complexity reduction using the regularity of the estimation process. The algorithm is applicable to a wide range of multi-amplitude modulation schemes, and we present an SEI system designed for an orthogonal frequency division multiplexing (OFDM) system over an empirical indoor channel model with associated numerical results. This technology is then adapted to provide location tracking in multipath environments, which locates the mobile stations (MS) based on the transmit power variation estimates. The method is simulated over a grid-based city map. In the last part of the thesis, complexity reduction methods are introduced to balance the convergence rate and identification performance.