Method for dynamic estimation of distance between nodes under multi-mobile mode

Abstract

The invention discloses a method for dynamic estimation of a distance between nodes under a multi-mobile mode. In offline mapping, an anchor node measures signal strength RSSI from wireless nodes at different distance points D for many times and establishes a mapping relation between RSSI values and the D through statistical analysis. In linear matching estimation, the nodes move away from the anchor node at the initial speed V0 and the acceleration a, so that the mapping relation between the RSSI values and the D can be equivalent to a mapping relation between the RSSI values and the time T; the anchor node records RSSI information once at every deltat to form RSSI value data stream. In the data stream, a data window is extracted to carry out linear processing to form a slide window which is easily matched with the mapping of the RSSI values and the time T, so that the distance estimation is carried out. The method has the advantages that during an estimation process of RSSI-D, a point-to-point matching mode extends to a multipoint-to-multipoint window matching mode, the linear processing is carried out on the data window to form an elastic slide window with a variable time difference ratio, a problem of uncertainty of the speed of the mobile node is overcome, and the estimation accuracy for the dynamic distance is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of wireless sensor networks, and in particular relates to a dynamic distance estimation method for multi-mobile mode wireless nodes. Background technique [0002] In static distance estimation, the factors that affect the estimation result are mostly the sensitivity of the acquisition equipment signal and the accuracy of the calculation process. However, in dynamic distance estimation, in addition to the above-mentioned effects, the position of the mobile node is constantly changing, and the mobile measurement and surrounding environment changes will also cause dynamic changes in the RSSI measurement data, resulting in more uncertainties in the RSSI data, making different modes of speed It is more difficult to estimate the distance below. [0003] In order to overcome the uncertainty of RSSI data, the method of clustering is usually used to process the data. Generally, the cluster center is used to represe...

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Problems solved by technology

However, this method puts too much emphasis on the adaptability to a single mode of uniform speed movement. In practical applications, it is not common for nodes to maintain absolute constant speed movement. For nodes that move with uniform speed and variable acceleration, this method cannot guarantee the dynamic RSSI-D estimation the accuracy of

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