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New Data Association Technique for Target Tracking in Dense Clutter Environment Using Filtered Gate Structure
El Said Mostafa Saad, El. Bardawiny, H. I. Ali, N. M. Shawky
Pages - 338 - 351     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 4   Issue - 6    |    Publication Date - January / February  Table of Contents
Target Tracking, Data Association, Probabilistic Data Association Algorithm, Kalman Filter
Improving data association process by increasing the probability of detecting valid data points (measurements obtained from radar/sonar system) in the presence of noise for target tracking are discussed in manuscript. We develop a novel algorithm by filtering gate for target tracking in dense clutter environment. This algorithm is less sensitive to false alarm (clutter) in gate size than conventional approaches as probabilistic data association filter (PDAF) which has data association algorithm that begin to fail due to the increase in the false alarm rate or low probability of target detection. This new selection filtered gate method combines a conventional threshold based algorithm with geometric metric measure based on one type of the filtering methods that depends on the idea of adaptive clutter suppression methods. An adaptive search based on the distance threshold measure is then used to detect valid filtered data point for target tracking. Simulation results demonstrate the effectiveness and better performance when compared to conventional algorithm.
CITED BY (1)  
1 Saad, E. M., Bardawiny, E. L., Ali, H. I., & Shawky, N. M. (2011). Improving data association based on finding optimum innovation applied to nearest neighbor for multi-target tracking in dense clutter environment. International Journal of Computer Science Issues.
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Professor El Said Mostafa Saad
- Egypt
Mr. El. Bardawiny
- Egypt
Mr. H. I. Ali
- Egypt
Mr. N. M. Shawky

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