Abstract
In this study, we proposed the two new interpolation algoirthms: Subsample Waveform Shifting (SWS) and Subsample Cross-Correlation (SCC) which are designed to enhance the accuracy of chaos lidar system. Chaos lidar system calculated cross-correlation function by the time delay of the chaos signal and reference signal to obtain the target distance. The accuracy of chaos lidar is influenced by the sampling frequency of the analog-to-digital converter (ADC). The concept of SWS use the correlation trace of known location to be the model, and transfer the model into frequency domain by Fourier transform, it can do continuous phase adjustment which is not limited by frequency sampling in frequency domain and find the most similar location with correlation trace of unknown location. The core concept of SCC are the same as SWS, but SCC directly do the fine time delay in the reference chaotic signal and reflected from the target chaos signal, and get a high resolution cross-correlation function. The study also compares SWS, SCC and other interpolation algorithms, discussed the performance of each algorithm under different operating conditions, such as changing optical intensity, correlation length, bandwidth and sampling frequency. In the study, the accuracy of the chaotic lidar was improved. The original accuracy was 3.47 cm . Under the appropriate conditions, the accuracy of SWS and SCC are improved by nearly two orders of magnitude up to 0.05 cm, successfully breakthrough the accuracy limited by sampling frequency.