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A novel adaptive tracking algorithm for maneuvering targets based on information fusion by neural network

Dehghani Tafti, A ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/EURCON.2007.4400583
  3. Publisher: 2007
  4. Abstract:
  5. The current statistical model and adaptive filtering (CSMAF) algorithm is one of the most effective methods for tracking the maneuvering targets. However, it is still worthy to investigate the characteristics of the CSMAF algorithm, which has a higher precision in tracking the maneuvering targets with larger accelerations while it has a lower precision in tracking the maneuvering targets with smaller acceleration. In this paper a novel adaptive tracking algorithm for maneuvering targets is proposed. To overcome the disadvantage of the CSMAF algorithm, a simple multi-layer feedforward neural network (NN) is used By introducing NN, two sources of information of the filter are fused while its output adjusts the covariance process noise. Simulation results show that the proposed scheme can improve the precision of the CSMAF algorithm significantly. Moreover, it exhibits much better performance in estimating the position, velocity and acceleration of a target in a wide range of maneuvers
  6. Keywords:
  7. Acceleration ; Adaptive filtering ; Adaptive filters ; Algorithms ; Boolean functions ; Data fusion ; Feedforward neural networks ; Filtration ; Image classification ; Neural networks ; Sensor data fusion ; Statistical methods ; Targets ; Adaptive tracking algorithm ; Current statistical (CS) model ; Feed forward (FF) ; Higher precision ; International conferences ; Maneuvering targets ; Multilayer (ML) ; Process noises ; Simulation results ; Two sources ; Wide-range ; Adaptive algorithms
  8. Source: EUROCON 2007 - The International Conference on Computer as a Tool, Warsaw, 9 September 2007 through 12 September 2007 ; December , 2007 , Pages 818-822 ; 142440813X (ISBN); 9781424408139 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4400583