A Learning Method for Optimal Chirp Predistortion

Abstract

This article introduces the &#34;chart & chirp&#34; method of one-shot, in situ VCO tuning curve estimation, learning, and predistortion, which provides an alternative to prevailing LMS-based background calibration loops. The proposed approach utilizes a cycle-counting FDC to estimate the VCO tuning curve and a linearly interpolating QDAC to actuate the predistortion. An LLSE-based learning process generates predistorted DAC control codes for any physically achievable chirp parameters. In simulation, 50 and 51 kHz of RMS FM error is achieved for 5 and 20 us chirps, respectively. The locations of nonlinearity-induced spurs in the IF spectrum are predicted via analysis of the DFT of FM error in generated chirps, with higher frequency DAC updates weakening these spurs and moving them out of the IF band of interest. Simulation in a coherent, monostatic radar model reveals that deterministic phase error from the chirp nonlinearity arises at a level above -70 dBc/Hz at 1 MHz offset, dominating random phase noise and setting the IF SNDR for R < 23 m and R = 92 m around 40 dB and 24 dB, respectively.