Multi-user RSS Fusion Method for WLAN Indoor Positioning Based on Linear Regression Algorithm
A linear regression and indoor positioning technology, applied in positioning, radio wave measurement system, measuring device, etc., can solve the problem of large positioning error, achieve the effect of improving accuracy and positioning accuracy
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specific Embodiment approach 1
[0052] The specific embodiment one, WLAN indoor positioning multi-user RSS fusion method based on linear regression algorithm, it is realized by the following steps:
[0053] Step 1. In the offline Radio Map establishment stage, the mobile terminal perceives the surrounding environment in the background without affecting the normal use of the user, and uploads the perceived RSS value, mobile terminal brand and other information to the server;
[0054] Step 2: The server selects a mobile terminal of a certain brand as the basic mobile terminal, performs linear regression processing on the RSS data collected by other mobile terminals and the RSS value collected by the basic mobile terminal at several reference points, and obtains the RSS value and Linear regression coefficients between different mobile terminals;
[0055] Step 3, use the obtained linear regression coefficients to perform linear regression processing on the RSS values collected by different mobile terminals at ...
specific Embodiment approach 2
[0059] Embodiment 2. This embodiment is a further limitation of the linear regression algorithm-based WLAN indoor positioning multi-user RSS fusion method described in Embodiment 1. The offline stage Radio Map establishment and analysis process includes the following steps:
[0060] 1. At each reference point in the area to be positioned, use different brands of mobile terminals to collect and record the received signal strength RSS value from each AP n times and record the two-dimensional coordinates of the reference point to form 4 N i ×n×(M+2), i=1,2,3,4 matrix, where N i The number of reference points for collecting RSS values for a mobile terminal of a certain brand, i=1, 2, 3, and 4 represent Huawei, Xiaomi, Samsung, and Nexus mobile terminals respectively, is the total number of reference points in the area to be located, n is the number of signal acquisitions at each reference point, M in M+2 represents the number of APs in the environment, and 2 represents the two-...
specific Embodiment approach 3
[0070] Embodiment 3: In the online positioning stage, the server performs linear regression processing on the RSS value collected by the user's mobile terminal to calculate the user's position coordinates, and calculates the error between the two-dimensional coordinates and the actual position. It consists of the following steps:
[0071] 1), such as Figure 4 As shown, the user selects the brand of the user's mobile terminal in the positioning software or the server independently identifies the brand of the user's mobile terminal, so as to obtain the linear regression coefficient in the Radio Map, and use the linear regression coefficient to linearize the RSS value measured by the user's mobile terminal. Regression processing obtains the RSS value after processing, and the calculation formula is as shown in formula (1) in the specific embodiment two;
[0072] 2) Average the n times of RSS value vectors measured at each reference point in the Radio Map to obtain a 1×(M+2) vec...
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