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Joint transform correlator (JTC) can make targets recognized and located accurately, but the bottleneck technique of
JTC is how to recognize spatial distorted targets in cluttered scene. This has restricted the development of the pattern
recognition with JTC to a great extent. In order to solve the problem, improved maximum average correlation height
(MACH) filter algorithm is presented in this paper. The MACH algorithm has powerful capability of recognition for
spatial distorted targets (rotation and scale changed etc.). The controlling parameters of the synthesized filter are
optimized in this paper, which makes the filter have higher distortion tolerance and can suppress cluttered noise
effectively. When improved MACH filter algorithm in frequency domain is projected to space domain, the MACH
reference template image can be obtained which includes various forms of distorted target image. Based on amounts of
computer simulation and optical experiments, MACH reference template is proved to have the capability of sharpening
the correlation peaks and expanding recognizing scope for distorted targets in cluttered scene. MATLAB software is
applied to produce MACH reference image for the detected target images and conduct simulation experiments for its
powerful calculation capability of matrix. In order to prove the feasibility of MACH reference in JTC and determine the
recognition scope, experiments for an aircraft target in the sky are carried out. After the original image is processed by
edge extraction, a MACH filter reference template is obtained in space domain from improved MACH filter in frequency
domain. From simulation experiments, the improved MACH filter is proved to have the feasibility of sharpening
correlation peaks for distorted targets. Optical experiments are given to verify the effectiveness further. The experiments
show the angular distortion tolerance can reach up to ±15 degrees and scale distortion tolerance can reach up to ±23%.
Within this scope, the spatial distorted aircraft can be recognized effectively. The actual effect of the improved MACH
filter algorithm has been confirmed very well.
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