Maximum Likelihood Joint Tracking and Association in a Strong Clutter\n without Combinatorial Complexity

2010/10/20 by Leonid Perlovsky, Perlovsky, Leonid I., Ross Deming +1
Computer Science · Engineering · Neuroscience · #Artificial Immune Systems Applications #Cognitive Science and Education Research #FOS: Computer and information sciences #Machine Learning (stat.ML) #Target Tracking and Data Fusion in Sensor Networks

paper · pdf · doi:10.48550/arxiv.1010.4236

openalex publication_date 2010/10/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We have developed an efficient algorithm for the maximum likelihood joint\ntracking and association problem in a strong clutter for GMTI data. By using an\niterative procedure of the dynamic logic process "from vague-to-crisp," the new\ntracker overcomes combinatorial complexity of tracking in highly-cluttered\nscenarios and results in a significant improvement in signal-to-clutter ratio.\n

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