Lidar compressive sensing using chaotic waveform

Abstract

Full waveform Lidar systems have the ability of recording the complete signal reflected from the illuminated target. Therefore, more detail information can be obtained compared to conventional Lidar systems. The problem that is faced in using full waveform Lidar is the acquisition of high volume data, a solution proposed to solve this problem is compressive sensing. By using a compressive sensing approach we can reduce the sampling rate and still be able to recover the signal. The reduction is incorporated in the acquisition hardware, where we perform sensing of the signal with compression. In this paper we propose to use a deterministic compressive sensing approach by using a chaotic signal as the sensing matrix. The proposed approach gives the range profile information without the requirement of further processing techniques. For comparison we used two different types of transmitted signals: chaotic and Linear Frequency Modulated (LFM) signals. Simulations demonstrate that chaotic signals give better results than the LFM signals. By using a chaotic signal we can obtain the impulse response of the target by using less than 20 percent of the samples.