A WLAN indoor position fingerprint positioning method for real-time monitoring
An indoor positioning and indoor location technology, applied in advanced technology, electrical components, network topology, etc., can solve the problems of high total energy consumption of positioning server energy consumption system, large consumption of mobile terminal data transmission, and large physical space search range, etc. Achieve the effect of reducing overall energy consumption, extending the working time, and prolonging the service cycle
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specific Embodiment approach 1
[0026] Specific implementation mode one: refer to figure 1 Describe this embodiment in detail, a WLAN indoor position fingerprint positioning method for real-time monitoring described in this embodiment, the indoor positioning space is provided with N reference nodes equally spaced in the indoor positioning space and M Wireless access node AP, so that any position in the indoor positioning space can receive the wireless signal sent by at least one wireless access node AP, and the received signal strength should be greater than -95dBm, M and N are both positive integers;
[0027] According to the physical locations of all reference nodes, the K-means clustering algorithm is used to divide the indoor positioning space where the reference nodes are located into α-block positioning sub-regions, denoted as: S 1 ,S 2 ,...,S α , where α is a positive integer, and the positioning method includes the following steps:
[0028] Step 1: The mobile terminal to be positioned uses the wei...
specific Embodiment approach 2
[0032] Specific embodiment two: this embodiment is to further explain a kind of WLAN indoor position fingerprint location method for real-time monitoring described in specific embodiment one, in this embodiment, the described K-means clustering algorithm will refer to A method for dividing the indoor positioning space where the nodes are located into α-block positioning sub-regions, which includes the following steps:
[0033]Step 1: Randomly select a point in the indoor positioning space as the origin to establish a Cartesian coordinate system, use the physical positions of N reference nodes as parameters to describe the reference nodes, randomly select K initial cluster centers among the N reference nodes, and record For: Z 1 (1), Z 2 (1),...,Z K (1), wherein K=α and 0≤K<N, the numbers in the clustering center brackets represent the number of iterations, and then perform step 1 and 2;
[0034] Step 12: Assign the remaining reference nodes to an initial cluster center acco...
specific Embodiment approach 3
[0044] Specific implementation mode three: refer to figure 2 Describe this embodiment in detail. This embodiment is a further description of a WLAN indoor location fingerprint positioning method for real-time monitoring described in Embodiment 1. In this embodiment, the weight matrix and step 1 described in Step 1 B. The transformation matrix is obtained by the following steps:
[0045] Step 2 1: Establish the fingerprint data space RadioMap data according to the distribution of the indoor positioning space and its internal reference nodes and wireless access nodes AP, and then perform step 2 2;
[0046] Step 2 2: Using the fingerprint data space RadioMap data obtained in step 2 1, respectively in each positioning sub-area and each pair of adjacent positioning sub-areas, take the received signal strength value RSS value of all wireless access nodes AP as Parameters, M wireless access nodes AP are clustered and divided, and then step 2 and 3 are performed;
[0047] Step 23...
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