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A probabilistic strongest neighbor filter algorithm for m validated measurements
- Source :
- IEEE Transactions on Aerospace and Electronic Systems. April, 2009, Vol. 45 Issue 2, p431, 12 p.
- Publication Year :
- 2009
-
Abstract
- A new form of the probabilistically strongest neighbor filter (PSNF) algorithm taking into account the number of validated measurements is proposed. The probabilistic nature of the strongest neighbor (SN) measurement in a cluttered environment is shown to be varied with respect to the number of validated measurements. Incorporating the number of validated measurements into design of the PSNF produces a consistent and cost effective data association method. Simulation studies show that the new filter is less sensitive to the unknown spatial clutter density and is more reliable for practical target tracking in nonhomogeneous clutter than the existing PSNF. It has similar performances to the probabilistic data association filter amplitude information (PDAF-AI) with much less computational complexities.
- Subjects :
- Avionics -- Research
Algorithms -- Usage
Combinatorial probabilities -- Research
Geometric probabilities -- Research
Probabilities -- Research
Electronic data processing -- Methods
Algorithm
Aerospace and defense industries
Business
Computers
Electronics
Electronics and electrical industries
Subjects
Details
- Language :
- English
- ISSN :
- 00189251
- Volume :
- 45
- Issue :
- 2
- Database :
- Gale General OneFile
- Journal :
- IEEE Transactions on Aerospace and Electronic Systems
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.204035585