Multi-level high-robustness fingerprint positioning method

Abstract

The invention provides a multi-level high-robustness fingerprint positioning method. The multi-level high-robustness fingerprint positioning method comprises the following steps: 1, setting a reference point; 2, deploying anchor point equipment; 3, collecting fingerprint data to establish a fingerprint library; 4, collecting positioning data; 5, positioning a first-stage region; and 6, carrying out second-stage accurate positioning. The method has the characteristics of high positioning precision, high reliability, excellent real-time performance and the like, is suitable for various wirelesspositioning technologies such as Bluetooth, WIFI and ZigBee, and realizes indoor high-precision positioning under the condition of not increasing hardware investment.

Description

technical field [0001] The invention relates to a multi-level high robust fingerprint positioning method. Background technique [0002] With the popularity of smartphones, various innovative location-based applications are emerging, which are gradually changing people's lifestyles, such as Meituan, Didi and WeChat. Positioning has become an indispensable requirement in this era, especially indoor positioning. Since people spend much more time indoors than outdoors, indoor location-based information services are booming, with a broad market space, and have become the focus of attention from all walks of life . The accuracy, stability and cost of positioning have always been the severe challenges faced by this industry. At present, the mainstream indoor positioning technology is wireless positioning technology, such as Bluetooth, WIFI and ZigBee, etc. The mainstream algorithms supporting wireless positioning technology include proximity method, centroid method, multilateral ...

AI Technical Summary

Problems solved by technology

[0003] In current engineering practice, due to the multipath effect of wireless signals, co-frequency resonance interference, and obstacle occlusion, the above-mentioned mainstream positioning algorithms cannot well support the application of wireless positioning technology in indoor scenarios, and the positioning accuracy is low. , weak reliability, poor real-time performance, etc., cannot well meet the needs of the industry

Among them, the proximity method and the centroid method can only roughly calculate the target position based on the location of the wireless anchor device due to their simple positioning principles. The positioning accuracy of the positioning method is greatly affected by the quality of the wireless signal. In practical applications, due to the serious interference of the wireless signal by the external environment and large fluctuations, the accuracy and stability of the multilateral positioning algorithm are not high; relatively speaking, the accuracy and stability of the fingerprint positioning algorithm The stability performance is good, but when the positioning area is large, a large positioning fingerprint library needs to be established, resulting in a complex fingerprint matching calculation process, a long time-consuming, and poor real-time performance. When the overall change or a small number of wireless anchor devices are damaged or faulty, the fingerprint data will change, causing the positioning accuracy to drop sharply, and the robustness is not high

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