Research On State Estimation Method Based On Spectrum Sensing Technology In Industrial Wireless Sensor Networks

With the rapid development of cognitive radio technology in industrial wireless sensor network,spectrum sensing technology is widely used in network protocols,resource allocation,and network security.Based on the current spectrum usage,the thesis uses the spectrum sensing key technology to explore opportunistic accessibility of spectrum in the idle state,so the communication quality of unauthorized users and spectrum utilization is improved.Firstly,the state estimation problem in industrial wireless sensor networks is strongly dependent on the quality of the wireless network channel.Traditional Kalman filtering cannot satisfy the change of dynamic channel.Secondly,the existing description of the channel state estimation model is not satisfactory in terms of dynamic changes of the channel and optimal estimation under multiple channels.In dynamic channel scenario,in order to adapt to the impact of packets loss on state estimation performance during the state of channel state transformation,an estimator based on the Kalman particle filter and a controller based on the channel sensing and switching model are designed.Extending to multi-channel scenarios based on single channel;breaking through the traditional Kalman filtering research framework;improving spectrum sensing efficiency;accelerating system evolution speed;quantifying estimation errors;and improving system estimation performance.The main work of the thesis is as follows:1.A channel state tracking model based on Kalman particle filter is established.In the dynamic channel scenario,traditional Kalman filtering is limited,it causes the packets can't be detected in time.According to the packet loss process that satisfies stochastic process of Bernoulli distribution,the Kalman particle filter algorithm,which is more suitable for optimizing the channel state space model,is used based on the traditional Kalman filter.It comprehensively considers the estimation stability under the influence factors such as lossy channel and bandwidth limitation.2.The Markov chain model describing the dynamic process of channel based on the status of authorized users and unauthorized users is discussed.Based on the Kalman particle filter model,the dynamic channel is tracked to improve the estimation accuracy.The channel model adopts the “listen before listening” strategy,that is,sensing before transmitting.The authorized user is modeled as a Markov process and its transition probability matrix is deduced.Define a new variable that indicates the working state of the authorized user and find the probability corresponding to the different states.Based on the spectrum sensing energy detection method,a dynamic channel space observation model is established,which comprehensively considers the computational complexity of the spectrum sensing algorithm and the feasibility of applying the channel estimation viewpoint.The multi-channel state is simultaneously estimated by establishing a stochastic finite set model,which achieves a perfect match between the estimator and the user state.3.A channel sensing and switching model based on the estimation performance is established.Apply spectrum sensing technology to the sensor antenna so that the sensor can respond the channel change of the remote unit in time by wireless medium,improve the estimation performance,and explore the possibility of using multiple channels opportunistically.In this thesis,the channels are divided into two types,an unlicensed channel that can be freely accessed and a plurality of authorized channels pre-assigned to the primary user.For the single channel case,the necessary conditions for estimating the stability are derived.At the same time,the conditions for the channel sensing and switching model used improving the estimation performance are derived,and the above results are extended to the multi-authorization channel scenario.Finally,the effectiveness of the spectrum sensing strategy is verified by comparing the worst case performance ratio.