Research on Compression Sensing Positioning Algorithm of Indoor Complex Environment Visible Light Indoor Based on Hybrid APIT

Abstract

In today’s highly urbanized world, indoor space is becoming more extensive and more complex, and under the increasingly urgent needs, indoor positioning has attracted people’s attention. With the rapid development of LED lighting technology, indoor positioning technology based on visible light communication has many advantages over traditional indoor positioning technology. Aiming at the influence of environmental factors such as noise and reflected light on the positioning accuracy, the compression perception theory is applied to the localization of visible light. The position of the receiving end in the positioning space is defined as a sparse variable in the discrete space. The power measurement matrix is expressed as the product of the observation matrix, and the sparse matrix and sparse vector in the compression perception theory are expressed. The traditional APIT algorithm is easy to misjudge unknown nodes in the triangle, resulting in low positioning accuracy of the algorithm. In this study, an indoor visible positioning algorithm based on hybrid APIT is proposed, which uses the area relationship of the triangle to determine the initial position of the unknown node, and then uses the tangent circle to further narrow the area where the unknown node may be located, and uses the hybrid centroid localization algorithm to obtain the estimated position of the unknown node.

Research and Implementation of Indoor 3D Positioning Algorithm Based on LED Visible Light Communication and Corresponding Parameter Estimation

“…Five actual position points were taken from the top, bottom, left, right, and middle of these 5 AP points. This is the result of the nearest point search method under the Matlab simulation [ 11 ], where the point represents the sample point taken, the five-pointed star represents the wireless access point, that is, the AP point, the difference indicates the actual location, and the point surrounded by the black triangle is the result of the nearest point search method.…”

“…In the two-dimensional plane, at least three known position points are required as reference points, and these three known reference points are used as the center point, and the distance between the LED and PD obtained under the above received signal strength RSS attenuation is used as a half diameter to draw an arc; then the intersection of the three arcs is the estimated position of the target unknown point [ 31 ]. As shown in Figure 5 , points A, B, and C are the positions of the three LED lights, X are the coordinate positions of the moving target points., According to the received signal strength, RSS measures the distance between the three LEDs and the PD, respectively is the sum of squares of d 1 , d 2 and d 3 so that the intersection of the three circles is ideally the moving target position [ 11 ].…”

RSSI centroid node location algorithm based on chimpanzee optimization

“…However, non-ranging depends on the connectivity of nodes, such as the number of hops in the location, without any additional hardware support. Typical algorithms include DV-Hop [7] algorithm, APIT [8]algorithm, etc. Among them, RSSI based on ranging algorithm has low requirements for hardware resources, is easy to implement and widely used [9], but location based on RSSI algorithm is easily affected by the environment, so many scholars have improved RSSI.…”