Abstract
For indoor mobile robots, many localization systems based on wireless sensor network have been reported. Received signal strength indicator is often used for distance measurement. However, the value of received signal strength indicator always has large fluctuation because radio signal is easily influenced by environmental factors. This will bring adverse effect on the distance measurement and deteriorate the performance of robot localization. In this article, the measured data are dealt with weighted recursive filter, which can depress the measurement noise effectively. In the linearization procedure, the least square method often causes additional error because it seriously relies on anchor nodes. Therefore, a minimum residual localization algorithm based on particle swarm optimization is proposed for a mobile robot running in indoor environment. With continuous optimization and update of particle swarm, the position that gets the best solution of objective function can be adopted as the final estimated position. Experiment results show that the proposed algorithm, compared with traditional algorithms, can attain better localization accuracy and is closer to Cramer–Rao lower bound.
Highlights
With wireless sensor networks (WSNs), the localization for indoor mobile robot has become one of the most important research issue
The localization methods based on received signal strength indicator (RSSI), which is widely used in WSN system, do not rely on extra hardware device
Considering the shortcomings of least square (LS) algorithm, we propose a minimum residual algorithm based on particle swarm optimization (PSO) to improve the localization accuracy effectively
Summary
With wireless sensor networks (WSNs), the localization for indoor mobile robot has become one of the most important research issue. Wang et al. proposed an improved maximum likelihood estimation, called “two-step least square,” to reduce NLOS error and improve the performance effectively. His assumption is that the covariance matrix of measurement noise is known beforehand. Based on the relative RSSI and the split-step particle swarm optimization algorithm, Xiufang and Shufang proposed a WSN localization algorithm, called improved particle swarm optimization–improved received signal strength indicator (IPSO-IRSSI). Non-iterative localization algorithms use only current observation data of target position for location estimation, without prior knowledge of environment noise and measurement noise. By simulating appropriate environmental noise parameters, the iterative algorithm can obtain better positioning accuracy. Experiment results show that the precision of our algorithm is closer to the lower bound of Cramer–Rao than existing algorithms
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