Quantization-based set value Kalman filtering algorithm

Abstract

The invention discloses a quantization-based set value Kalman filtering algorithm. Compared with the prior art, the algorithm has the advantages that the problem in remote state estimation of a dynamic random system in a wireless sensor limited in bandwidth is solved; original measurement values are quantized into messages, and the messages are transmitted to a remote estimator from a local sensor; and a set region of the original measurement values is represented by utilizing information contained in a quantization policy, and a closest ellipsoid approximation method of the region is given. Therefore, the quantization-based set value Kalman filtering algorithm is proposed. Three algorithms are compared and analyzed in computer simulation to explain the validity of the algorithm proposed by the invention.

Description

technical field [0001] The invention relates to the technical field of microelectronics, in particular to a set-valued Kalman filter algorithm based on quantization. Background technique [0002] With the development of microelectronics technology, wireless communication technology and embedded technology, wireless sensor network has developed rapidly. Wireless sensor network is a kind of distributed sensor network. Because of the low price of the sensors, flexible location movement, variable network settings and strong fault tolerance, it is widely used in national defense and military, smart home, biomedical, environmental monitoring, space Exploration, industrial commerce and many other fields. [0003] Since the energy of the sensor in the wireless sensor network is limited, and the energy of the sensor is provided by the battery, replacing the battery consumes a lot of money, sometimes it is very difficult, and sometimes it is even impossible to replace the battery due...

AI Technical Summary

Problems solved by technology

[0004] At present, all estimates based on quantization finally give point values. However, due to the existence of quantization errors, the actual observation values ​​are uncertain, and it is obviously unreasonable to use quantized single-point values ​​for estimation.

Another literature proposes a set-valued Kalman filter algorithm for the first time when the initial state estimation distribution is a convex set, but this algorithm still only considers the measurement of a single point value

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