| Three methods are investigated to improve PLL tracking performance in high dynamic applications: a Kalman filter-based tracking algorithm, application of a wavelet de-noising technique in PLL, and an adaptive bandwidth algorithm. The Kalman filter-based tracking algorithm makes use of a carrier phase dynamic model and a measurement from the output of the discriminator to estimate the phase difference between the incoming signal and the Numerical Controlled Oscillator (NCO) output, Doppler frequency and the change rate of Doppler frequency. The wavelet de-noising technique effectively decreases the noise level and allows broadening of the PLL bandwidth to track high dynamics signals. The adaptive bandwidth PLL algorithm adapts the bandwidth of the PLL according to the estimation of the incoming signal dynamics and noise level.; The performance is evaluated in terms of signal-to-noise ratios and dynamic variations using simulating signals. The first two methods are found to produce better improvements when the signal-to-noise ratio is low and the signal dynamic is high. The third method works well under high signal-to-noise ratios and less random dynamic variations.; The results show that these methods outperform the ordinary PLL under high dynamic conditions and the resulting carrier phase measurement is more accurate. (Abstract shortened by UMI.)... |
