Abstract
In a complex indoor environment, wireless signals are affected by multiple factors such as reflection, scattering or diffuse reflection of electromagnetic waves from indoor walls and other objects, and the signal strength will fluctuate significantly. For the signal strength and the distance between the unknown nodes and the known nodes are a typical nonlinear estimation problem, and the unknown nodes cannot receive all Access Points (APs) signal strength data, this paper proposes a Particle Filter (PF) indoor position algorithm based on the Kernel Extreme Learning Machine (KELM) reconstruction observation model. Firstly, on the basis of establishing a fingerprint database of wireless signal strength and unknown node position, we use KELM to convert the fingerprint location problem into a machine learning problem and establish the mapping relationship between the location of the unknown node and the wireless signal strength, thereby refocusing construct an observation model of the indoor positioning system. Secondly, according to the measured values obtained by KELM, PF algorithm is adopted to obtain the predicted value of the unknown nodes. Thirdly, the predicted value is fused with the measured value obtained by KELM to locate the position of the unknown nodes. Moreover, a novel control strategy is proposed by introducing a reception factor to deal with the situation that unknown nodes in the system cannot receive all of the AP data, i.e., data loss occurs. This indoor positioning experimental results show that the accuracy of the method is significantly improved contrasted with commonly used PF, GP-PF and other positioning algorithms.
Highlights
Publisher’s Note: MDPI stays neutralIndoor positioning has been extensively used and developed with the increasing demand for indoor position information in recent years
For the problem of blocked wireless signals in complex indoor environments, which leads to the inability to receive all Access Points (APs) data, this paper proposes a control strategy by introducing a reception factor E, which can solve the problem of unknown nodes not being able to receive all APs data due to blocked wireless signals in real environments
Steps of Iterative Indoor Location Based on Kernel Extreme Learning Machine (KELM)-Particle Filter (PF). This KELM-PF-based indoor positioning algorithm firstly trains the weights of the hidden and the output layer through the KELM network, calculates the number nr of AP signal strengths received by unknown nodes, and adopts the control strategy of the reception factor E: if E = 1, using the KELM-PF algorithm reconstruct the observation model and obtain the position coordinates of the unknown node; if E = 0, it returns to the last execution of PF
Summary
Indoor positioning has been extensively used and developed with the increasing demand for indoor position information in recent years. The above references [11,12,13,14] focus on positioning methods by ranging based on RSS propagation models. Due to the complex indoor conditions, wireless signals are affected by diffuse reflection, scattering or reflection, bypassing, refraction, or transmission of electromagnetic waves from objects such as building walls in a complex indoor environment, which produces a certain impact on the propagation model These methods are affected by the environment. Compared to propagation model methods, which are highly influenced by the environment, location fingerprint-based indoor localization methods [16,17] can reduce the impact of signal shadow fading and multipath effects. The proposed reconstructed observation model indoor positioning method is described in detail in the third section. Conclusions and prospects are illustrated in the fifth section
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