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22 August 2001 Extended Kalman filter for multiwavelength differential absorption lidar
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Our earlier study described an approach for estimating the path-integrated concentration, CL, of a set of vapor materials using time series data from topographic backscatter lidar with frequency-agile lasers. That methodology assumed the availability of background data samples collected before the release of the vapors of interest to estimate statistical parameters such as the mean topographic backscatter return and the transmitter energy mean and variance as a function of wavelength. The background data were then used in an extended Kalman filter approach for estimating the CL components as a function of time. That approach worked well for data that showed negligible drift in the mean parameters over the data collection time. In practice, however, the transmitter energy and background return can drift, producing substantial bias in the estimates. In this paper we generalize the approach to a more complete state model that includes the mean transmitter energy and background return in addition to the CL vapor set. This generalization allows the algorithm to track slow drift in those parameters and provides generally improved estimates. Results of the new algorithm are compared with those of a two-wavelength classical DIAL estimator on synthetic and field test data.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Russell E. Warren and Richard G. Vanderbeek "Extended Kalman filter for multiwavelength differential absorption lidar", Proc. SPIE 4378, Chemical and Biological Sensing II, (22 August 2001);

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