Nonlinear Kalman filtering techniques for incoherent backscatter lidar: return power and log power estimation

Area of Science:

• Optical Engineering
• Signal Processing
• Remote Sensing

Background:

• Lidar systems require accurate estimation of return power, even with multiplicative noise like speckle.
• Traditional methods may struggle with nonlinearities and noise in lidar data processing.

Purpose of the Study:

• To evaluate the extended Kalman filter (EKF) for recursive estimation of nonlinear functions in lidar systems.
• To assess the EKF's capability in handling multiplicative speckle noise.
• To estimate lidar return power, log power, and speckle noise parameters.

Main Methods:

• Utilized coherent lidar returns and simulated data for testing.
• Employed a nonlinear filter, specifically the extended Kalman filter.
• Developed system models incorporating a random walk signal and an uncorrelated speckle term.

Main Results:

• The extended Kalman filter demonstrated effective estimation of return power, log power, and speckle noise.
• The filter successfully processed lidar returns in the presence of multiplicative speckle noise.
• Reiterative processing led to self-consistent parameter estimation.

Conclusions:

• The extended Kalman filter is a viable tool for recursive nonlinear estimation in lidar systems.
• EKF provides robust estimation of key lidar parameters despite speckle noise.
• This approach enables more accurate analysis of lidar system performance and data.