Abstract
Laser radar (Lidar) has been used extensively for remote sensing of wind patterns, turbulence in the atmospheric boundary layer and other important atmospheric transport phenomenon. As in most narrowband radar application, radial velocity of remote objects is encoded in the Doppler shift of the backscattered signal relative to the transmitted signal. In contrast to many applications, however, the backscattered signal in atmospheric Lidar sensing arises from a multitude of moving particles in a spatial cell under examination rather than from a few prominent “target” scattering features. This complicates the process of extracting a single Doppler value and corresponding radial velocity figure to associate with the cell. This paper summarizes the prevalent methods for Doppler estimation in atmospheric Lidar applications and proposes a computationally efficient scheme for improving Doppler estimation by exploiting the local structure of spectral density estimates near spectral peaks.
Highlights
Estimation of range rate by measuring Doppler shift of scattered signals is a foundational capability in radar remote sensing of dynamic scenes
Most current-generation commercial lidars estimate Doppler using straightforward digital signal processing methods such as the Periodogram Discrete spectral Peak Estimator [1] or the Maximum Likelihood Estimator described in [2]. Both of these methods begin by forming a discrete spectral density estimate from digitally sampled temporal data representing the range cell of interest and identifying a single dominant peak in this spectrum estimate
In this paper we examine methods for Doppler estimation, inspired by [4], that exploit a small neighborhood of values around the maximal peak in the spectral density estimate to improve estimation accuracy relative to accepting the frequency of the single peak as the estimate
Summary
Estimation of range rate by measuring Doppler shift of scattered signals is a foundational capability in radar remote sensing of dynamic scenes. Most current-generation commercial lidars estimate Doppler using straightforward digital signal processing methods such as the Periodogram Discrete spectral Peak Estimator [1] or the Maximum Likelihood Estimator described in [2]. Both of these methods begin by forming a discrete spectral density estimate from digitally sampled temporal data representing the range cell of interest and identifying a single dominant peak in this spectrum estimate. Tradeoffs between additional computational complexity entailed in these methods and the improvement in estimation accuracy they provide are quantified, and their performance is compared to performance descriptors for other algorithms that appear in [2]
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