• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.12
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• pp.1062-1070
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• 2015

This paper presents an application of an optimal smoothing filter for moving target tracking problem based on measured noise source. In order to measure distance and velocity for the moving target, a beamforming method is applied to use the noise source by using microphone array. Also a Kalman filter and an optimal smoothing algorithm are adopted to improve accuracy of trajectory estimation by using a Singer target model. The simulation is conducted with a missile dynamics to verify performance of the optimal smoothing filter, and a model rocket is used for experiment environment to compare the trajectory estimation results between the beamforming, the Kalman filter, and the smoother. The Kalman filter results show better tracking performance than the beamforming technique, and the estimation results of the optimal smoother outperform the Kalman filter in terms of trajectory accuracy in the experiment results.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1309-1314
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• 2012

The SAMED filter is introduced as a wide class of multi-stage filters which encompass linear FIR and nonlinear order statistic filters. The output of SAMED filter is linear combination of sub-median outputs. In this paper, optimal SAMED filter is designed for images corrupted by various noise, and performance is analogized. The experimental result shows that the efficiency of each order of SAMED filters is depends on type of noise. It is shown that low order filters are effective in Gaussian environments but high order filters are effective in impulsive case. This result may be used to follow-up research on successive SAMED filters.