Journal ArticleDOI
Application of neural networks in target tracking data fusion
TLDR
In this paper, the authors proposed to incorporate a neural network into the normal Kalman filter configuration such that the neural network provides the adaptive capability the filter needs, thus reducing the estimation error.Abstract:
Kalman filtering is a fundamental building block of most multiple-target tracking (MTT) algorithms. The other building block usually involves some type of data association schemes. Here it is proposed to incorporate a neural network into the normal Kalman filter configuration such that the neural network provides the adaptive capability the filter needs. As such the estimation error of the Kalman filter would be reduced, hence improving the MTT solution. Simulation results have shown that this claim is valid. >read more
Citations
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Journal ArticleDOI
Taxonomy of multiple target tracking methods
TL;DR: A concise summary of techniques for multiple target tracking is provided and their main characterics assessed qualitatively and a comparison chart is provided that lists each algorithm and categorises the processing scheme, data association mechanism, complexity scaling, overall complexity and a subjective performance figure.
Journal ArticleDOI
Tracking a maneuvering target using neural fuzzy network
Fun-Bin Duh,Chin-Teng Lin +1 more
TL;DR: A fast target maneuver detecting and highly accurate tracking technique using a neural fuzzy network based on Kalman filter based on a self-constructing neural fuzzy inference network (KF-SONFIN) algorithm for target tracking is proposed.
Journal ArticleDOI
DeepMTT: A deep learning maneuvering target-tracking algorithm based on bidirectional LSTM network
TL;DR: A deep learning maneuvering target-tracking (DeepMTT) algorithm based on a DeepMTT network, which can quickly track maneuvering targets once it has been well trained by abundant off-line trajectory data from existent maneuversing targets.
Journal ArticleDOI
Online data-driven fuzzy clustering with applications to real-time robotic tracking
Peter X. Liu,Max Q.-H. Meng +1 more
TL;DR: A novel online data-driven fuzzy clustering algorithm that is based on the Maximum Entropy Principle for this particular task, which eliminates the necessity of expert's knowledge and a priori information on moving targets, as required by most traditional techniques.
Journal ArticleDOI
Non-fragile finite-time l 2 - l ∞ state estimation for discrete-time Markov jump neural networks with unreliable communication links
TL;DR: The focus is on the design of non-fragile state estimator such that the augmented estimation error system is mean-square stochastically finite-time stable with a prescribed level of l 2 - l ∞ performance.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Journal ArticleDOI
Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.
Journal ArticleDOI
Tracking in a cluttered environment with probabilistic data association
Yaakov Bar-Shalom,Edison Tse +1 more
TL;DR: Simulation results obtained for tracking an object in a cluttered environment show the PDAF to give significantly better results than the standard filter currently in use for this type of problem.
Journal ArticleDOI
A Kalman Filter Based Tracking Scheme with Input Estimation
Yiu-Tong Chan,A.G.C. Hu,J. Plant +2 more
TL;DR: In this article, a least square estimator is used to estimate the acceleration input vector of a target and a simple Kalman filter is used for tracking the target in constant course and speed mode.
Journal ArticleDOI
Variable Dimension Filter for Maneuvering Target Tracking
Yaakov Bar-Shalom,K. Birmiwal +1 more
TL;DR: In this article, a novel approach to tracking a maneuvering target is developed, which does not rely on a statistical description of the maneuver as a random process, instead, the state model for the target is changed by introducing extra state components when a maneuver is detected.