Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion

A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This paper presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy.

[1]  Hyun Myung,et al.  Evolutionary programming techniques for constrained optimization problems , 1997, IEEE Trans. Evol. Comput..

[2]  Franco Zambonelli,et al.  Distributed Speaking Objects: A Case for Massive Multiagent Systems , 2018, MMAS.

[3]  Seongju Chang,et al.  High-Resolution Touch Floor System Using Particle Swarm Optimization Neural Network , 2013, IEEE Sensors Journal.

[4]  Hassan Ghasemzadeh,et al.  Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges , 2017, Inf. Fusion.

[5]  Daniel A. Levinthal,et al.  Exploration and Exploitation in Organizational Learning , 2007 .

[6]  Jun Zhang,et al.  A Level-Based Learning Swarm Optimizer for Large-Scale Optimization , 2018, IEEE Transactions on Evolutionary Computation.

[7]  Ning Xiong,et al.  Multi-sensor management for information fusion: issues and approaches , 2002, Inf. Fusion.

[8]  Vera Kurková,et al.  Kolmogorov's theorem and multilayer neural networks , 1992, Neural Networks.

[9]  Eric T. Matson,et al.  Special issue on smart interactions in cyber-physical systems: Humans, agents, robots, machines, and sensors , 2018 .

[10]  C. Knospe,et al.  PID control , 2006, IEEE Control Systems.

[11]  David Cruz,et al.  Indoor Robot Positioning Using an Enhanced Trilateration Algorithm , 2016 .

[12]  M J Willis.  Proportional-Integral-Derivative Control , 1999 .

[13]  Wei Yang,et al.  Multi-Sensor Detection with Particle Swarm Optimization for Time-Frequency Coded Cooperative WSNs Based on MC-CDMA for Underground Coal Mines , 2015, Sensors.

[14]  Abdelhafid Elouardi,et al.  A Practical Approach for High Precision Reconstruction of a Motorcycle Trajectory Using a Low-Cost Multi-Sensor System , 2018, Sensors.

[15]  Robert Riener,et al.  A survey of sensor fusion methods in wearable robotics , 2015, Robotics Auton. Syst..

[16]  Fakhri Karray,et al.  Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.

[17]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[18]  Dafang Zhuang,et al.  Advances in Multi-Sensor Data Fusion: Algorithms and Applications , 2009, Sensors.

[19]  R. Brereton,et al.  Support vector machines for classification and regression. , 2010, The Analyst.

[20]  Mihir Mody,et al.  Multi-sensor fusion for Automated Driving: Selecting model and optimizing on Embedded platform , 2018, Autonomous Vehicles and Machines.

[21]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.

[22]  Dacheng Tao,et al.  On Better Exploring and Exploiting Task Relationships in Multitask Learning: Joint Model and Feature Learning , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[23]  Dongpu Cao,et al.  Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System , 2018, IEEE Transactions on Industrial Informatics.

[24]  Guochu Chen,et al.  Particle Swarm Optimization Neural Network and Its Application in Soft-Sensing Modeling , 2005, ICNC.

[25]  Graham Kendall,et al.  Is Evolutionary Computation Evolving Fast Enough? , 2018, IEEE Computational Intelligence Magazine.

[26]  Seongju Chang,et al.  Enhancement of Particle Swarm Optimization by Stabilizing Particle Movement , 2013 .

[27]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..