Abstract
Aiming at the problem that the indoor target location algorithm based on received signal strength (RSSI) in the IoT environment is susceptible to interference and large fluctuations, an indoor localization algorithm combining RSSI and nonmetric multidimensional scaling (NMDS) is proposed (RSSI- NMDS). First, Gaussian filtering is performed on the received plurality of sets of RSSI signals to eliminate abnormal fluctuations of the RSSI. Then, based on the RSSI data, the dissimilarity matrix is constructed, and the relative coordinates of the nodes in the low-dimensional space are obtained by NMDS solution. Finally, according to the actual coordinates of the reference node, the coordinate transformation is performed by the planar four-parameter model, and the position of the node in the actual coordinate system is obtained. The simulation results show that the proposed method has strong anti-RSSI perturbation and high positioning accuracy.
Highlights
With the development of Internet of things and mobile Internet technology, smart home is gradually entering people’s daily life
The positioning algorithm can be divided into two categories: based on ranging positioning algorithm and no ranging positioning algorithm [4, 5]. e ranging algorithm is commonly used based on received signal strength indicator (RSSI), based on time of arrival (TOA), time difference of arrival (TDA), and AOA based on the signal arrival angle positioning method [6,7,8]
Aiming at the problem of the information jump in WIFI indoor location based on the RSSI, which influences the positioning accuracy, the authors in [15] proposed an improved adaptive weighted K-nearest neighbor (AWKNN) localization method based on the Kalman filter
Summary
With the development of Internet of things and mobile Internet technology, smart home is gradually entering people’s daily life. Mathematical Problems in Engineering can adaptively control the interval derivative particle number to enhance the filtering real time ability; the building map information, RSSI positioning information, and certainty factor are integrated into the particle weight calculation to improve the positioning accuracy. Aiming at the problem of the information jump in WIFI indoor location based on the RSSI, which influences the positioning accuracy, the authors in [15] proposed an improved adaptive weighted K-nearest neighbor (AWKNN) localization method based on the Kalman filter. Experiments show that these methods have achieved good results. In order to improve the positioning performance, this paper applies Gaussian filtering to RSSI signal and adopts nonmetric multidimensional scaling (NMDS) to enhance the resistance to RSSI perturbation and improve the positioning accuracy
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