Abstract
We present a radio frequency (RF) front-end nonlinearity estimator that is based on the knowledge of a training sequence to perform specific emitter identification (SEI), which discerns radio emitters of interest. Design and fabrication variations provide unique signal signatures for each emitter, and we extract those characteristics through the estimation of transmitter nonlinearity coefficients. The algorithm provides robust identification by first 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.
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