Lake ecology intelligent sensing method based on Kalman filtering

Abstract

The invention discloses a lake ecology intelligent sensing method based on Kalman filtering. The method comprises: giving noise mean power fluctuation; tracking noise by use of ARMA Kalman filtering; for a Gauss white noise channel, using a dynamic threshold frequency spectrum sensing method to perform intelligent sensing on the ecology; for a multipath frequency selection fading channel, using the Kalman filtering to perform correlation information tracking on a fading channel tracking method, and performing the intelligent sensing on the ecology through a cooperation dynamic threshold frequency spectrum sensing method; and for a Gauss white noise and flat fading channel, using a higher-order cycle stationary characteristic sensing method to perform sensing, and using the cooperation cycle stationary characteristic sensing method of the Kalman filtering to confront the frequency selection fading channel. The sensing method provided by the invention can effectively reduce the co-channel interference among high-density sensing nodes of a lake ecology monitoring perception system and enhance the concurrency of data transmission among sensing nodes.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a method for intelligently sensing lake ecology based on Kalman filtering. Background technique [0002] Since the wireless sensor network (WSN) operates in an unlicensed frequency band, more and more wireless communication technologies share this frequency band, causing the frequency band to become more and more crowded. Cognitive radio technology has been introduced into wireless sensor networks as a good technology to solve the limited spectrum resources. Cognitive Wireless Sensor Networks (CRSN) have begun to be studied in depth [0003] Cognitive wireless sensor network (CRSN) has become a research hotspot in recent years, and sensor node energy, routing, spatiotemporal dynamic monitoring network system and virtual simulation decision support have become the main research directions. Cognitive wireless sensor network (CRSN) collects data through cognitive se...

AI Technical Summary

Problems solved by technology

[0004] (1) The fluctuation of noise leads to a sharp decline in signal sensing performance, and the idea of ​​noise average power fluctuation is not proposed;

[0005] (2) No effective anti-noise average power fluctuation scheme was proposed;

[0006] (3) The tracking method of noise average power is not proposed;

[0007] (4) It does not solve the problem of aggravating the difficulty of ecological intelligent sensing due to the reduction of the SNR of the authorized user received by the perceived user due to the multipath fading channel and the shadow fading channel;

[0008] (5) The geographical characteristics of the lake lead to the deployment of high-density sensor nodes for ecological intelligent sensing. The cognitive wireless sensor network of this high-density sensor node has obvious co-channel interference, which affects the concurrent transmission of sensing data and reduces channel utilization. rate, this problem has not been well resolved

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