22 October 1993 Closely spaced object resolution enhancement and measurement technique for AST
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Abstract
Closely spaced object (CSO) processing methods often utilize a low pass filter plus deconvolution process for resolution enhancement. Other methods utilize an iterative least squares fitting process employing either a prior knowledge for initial estimates, or parameterizing initial estimates until an optimal fit is achieved. Each method has advantages and disadvantages. Low pass filtering plus deconvolution, performed in the time domain with a finite impulse response filter (FIR), is less computationally intensive than nonlinear least squares fitting. Nonlinear least squares fitting is generally superior as a maximum likelihood estimator of amplitude and position for lower signal to noise ratio (SNR) CSO clusters. Here we present a method of combining the two processes by using cues derived from deconvolved data as initial estimates for least squares fitting. The process is demonstrated on raw sensor data from the Airborne Surveillance Testbed (AST) sensor. Effects of apparent focal plane motion on CSO resolution enhancement and least squares refinement are discussed. Several least squares refinement methods are presented.
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Garth E. Klein, Mike A. Loudiana, "Closely spaced object resolution enhancement and measurement technique for AST", Proc. SPIE 1954, Signal and Data Processing of Small Targets 1993, (22 October 1993); doi: 10.1117/12.157813; https://doi.org/10.1117/12.157813
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