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Error Constraint Enhanced Particle Filter Using Quantum Particle Swarm Optimization
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AI
DOI：10.1109/JSEN.2021.3113364.png)
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摘要
En 中文
The position is one of the foremost imperative properties of an object. The Inertial Measurement Unit (IMU) development has enabled the system to continuously decrease in size, reduce power consumption, and become more and more universal. Although IMU meets the real-time needs of the localization system, it faces accumulative error and drifting problems. This paper presents an error constraint enhanced particle filter algorithm using quantum particle swarm optimization (EC-QPF), which achieves high-precision position estimation of target tracking with IMU. First, we proposed a quantum particle swarm optimization-based resampling method taking the place of the traditional weight-based resampling method in the particle filter, which avoids the particle impoverishment problem and keeps the particles' diversity. The theoretical foundation of error constraint was raised applied to enhance the performance of the proposed particle filter. The error constraint is established utilizing the known estimated center and confidence scale to achieve particle screening based on the geometrical position of particles. Numerical experimental results show that the proposed EC-QPF has a 67% improvement instead of other modified particle filters and more efficiently eliminates the cumulative error. Furthermore, higher accuracy and stability are obtained under the exact condition, demonstrating better performance than traditional particle filter methods.
Keyword:
Particle swarm optimization
Particle measurements
Atmospheric measurements
Target tracking
Size measurement
Sensors
Hidden Markov models
Target tracking
particle filter
quantum particle swarm optimization
error constraint
accumulative error
期刊
IF:
4.5
论文数:
2.1W
被引数:
7.3W
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