RSSI (received signal strength indicator) distance measuring method based on cost-reference particle filter

Abstract

The invention provides an RSSI (received signal strength indicator) distance measuring method based on a cost-reference particle filter. The method comprises the following steps of: arranging anchor nodes in an indoor environment, and planning a moving path of a mobile node; enabling the mobile node to move according to the planned path, and acquiring data packs sent by different anchor nodes at each predetermined position; performing Gaussian filter preprocessing on the data packs acquired at each position; and performing cost-reference particle filter processing on the data after preprocessing so as to estimate the distance between the anchor nodes and the mobile node. The method provided by the invention improves the present situation that distance measurement requires a lot of priori experiments to fit models, is suitable for a situation with unknown noise in a wireless signal transmission attenuation process, and has the characteristics that a latest observed value is used for sequential estimation, and real-time adaptive adjustment is performed according to environmental data; adaptive fading factors are introduced to dynamically adjust the influence degree of history observed values on an estimation process; a loop-locked adjustment strategy is adopted to calculate feedback coefficients, enhance system robustness, and improve the precision of distance measurement.

Description

technical field [0001] The invention belongs to the field of wireless sensor networks, in particular to an RSSI ranging method based on a cost reference particle filter. Background technique [0002] In recent years, with the continuous development of wireless network, mobile computing, ubiquitous computing and other technologies, the application of location-based services is becoming more and more popular, especially the indoor positioning technology has received extensive attention. However, due to non-line-of-sight propagation due to wall blocking and complex indoor environments, it is easy to cause multipath effects of wireless signals, making GPS signals severely attenuated or even invalid indoors, making positioning impossible. [0003] With the rapid development of wireless technology, infrared positioning technology, ultrasonic positioning technology, Bluetooth technology, radio frequency identification technology, ultra-wideband technology, WiFi technology and ZigBe...

AI Technical Summary

Problems solved by technology

However, this method needs to measure a large amount of prior data for fitting, and the workload is heavy, and once the environment changes, it needs to re-collect the prior data for fitting; in the patent No. ZL201110382344. In the invention patent of "Indoor Positioning Method with Improved Centroid Algorithm", a large number of (RSSI, d) is measured in the prior stage to train the BP neural network, and the network is saved. In the ranging stage, RSSI is used as input and d is output to achieve ranging. Using a neural network to fit a nonlinear curve is simple and reduces the amount of prior data, but this method can only process data in batches and cannot use the latest measured values, and data collection needs to be adjusted when the environment changes

[0006] It can be seen that the existing RSSI-based ranging method generally selects a simplified logarithmic distance path loss model, and obtains the parameters in the model through nonlinear function parameter fitting or performs fitting through a neural network. Both fitting methods need to collect A large amount of prior data, a lot of work in the prior stage

And the latest measured value cannot be used. Once the environment changes, the parameters fitted in the prior stage need to be corrected, so the fitted parameters cannot be adaptively adjusted to the environment

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