CN103607250B - Based on the lake ecological intelligence method for sensing of Kalman filtering - Google Patents
Based on the lake ecological intelligence method for sensing of Kalman filtering Download PDFInfo
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- CN103607250B CN103607250B CN201310560744.4A CN201310560744A CN103607250B CN 103607250 B CN103607250 B CN 103607250B CN 201310560744 A CN201310560744 A CN 201310560744A CN 103607250 B CN103607250 B CN 103607250B
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Abstract
The invention discloses a kind of lake ecological based on Kalman filtering intelligence method for sensing, described method comprises: provide noise average power fluctuation; ARMA Kalman filtering is utilized to follow the tracks of described noise; For Gaussian white noise channel, dynamic threshold spectrum sensing method is utilized to carry out intelligence sensing to ecology; Frequently fading channel is selected for multipath, utilizes Kalman filtering to carry out relevant information tracking to fading channel tracking, and by cooperation dynamic threshold spectrum sensing method, intelligence sensing is carried out to ecology; For white Gaussian noise and flat fading channel, utilize Higher-Order Cyclic stationary nature method for sensing to sense, utilize the cooperation cyclostationary characteristic method for sensing of Kalman filtering to resist and frequently select fading channel.Method for sensing provided by the invention effectively reduces co-channel interference between lake ecological monitoring sensory perceptual system middle-high density sensing node, enhances the concurrency of transfer of data between sensing node.
Description
Technical field
The present invention relates to wireless communication technology field, particularly relate to a kind of lake ecological based on Kalman filtering intelligence method for sensing.
Background technology
The frequency range be operated in due to wireless sensor network (WSN) is unauthorized frequency range, and increasing wireless communication technology shares this frequency range at present, causes this frequency range more and more crowded.Cognitive radio technology is introduced in wireless sensor network as the good technique solving frequency spectrum resource limited.Cognition wireless sensor network (CRSN) starts to obtain deep research
Cognition wireless sensor network (CRSN) becomes study hotspot problem in recent years, and the aspects such as sensing node energy, route, space-time dynamic monitoring network system and virtual emulation decision support become main direction of studying.Cognition wireless sensor network (CRSN) carries out data acquisition by cognitive sensing node, transmits data by wireless sensor network, but in the communication network environment of high density sensing node, there is co-channel interference between node.How can under the restriction of few Internet resources, in time, synchronous transfer of data being gone out also is wireless sensor network (WSN) problem demanding prompt solution, and be a kind of important means realizing this target based on the dynamic spectrum access of cognition wireless sensor network (CRSN), in cognition wireless sensor network (CRSN) system, collaboration frequency spectrum sensing principle is as shown in Figure 1.At present, there is following shortcoming in the dynamic spectrum access prior art for cognition wireless sensor network (CRSN):
(1) fluctuation of noise causes signal sensing performance sharply to decline, and does not propose noise average power fluctuation thought;
(2) proposition antagonism noise average power fluctuation scheme not yet in effect;
(3) tracking of noise average power is not proposed;
(4) SNR of the unresolved authorized user causing perception user to receive due to multidiameter fading channel and shadow fading channel diminishes and aggravates ecological intelligence sensing difficulty problem;
(5) lake geographic properties causes needing to lay high density sensing node during ecological intelligence sensing, there is obvious co-channel interference in the cognition wireless sensor network of this high density sensing node, affect the concurrency transmission of sense data, reduce channel utilization, this problem is not well solved.
The construction of lake and marshland Ecological Economic Region Internet of Things Applied D emonstration project, be unable to do without the ecological intellectual monitoring sensory perceptual system of cognition wireless sensor network technique.Due to lake and marshland ecotope, to contain area wide, there is certain otherness in wetland Region parabiosis environment, therefore, the density that zones of different lays sensing node is different, the validity of ecological supervisory control system will depend on channel cooperation sensing performance between high density sensing node, the sensing node spectrum sensing algorithm of high robust will reduce the co-channel interference between sensing node, strengthen the concurrency of internodal data transmission, the channel utilization of raising cognition wireless sensor network.
Therefore; those skilled in the art is devoted to develop a kind of lake ecological intelligence method for sensing; the controllability of the co-channel interference between effective reduction lake ecological monitoring sensory perceptual system middle-high density sensing node, the concurrency strengthening transfer of data between sensing node, the channel utilization significantly improving cognition wireless sensor network, promotion lake ecological environment protection and treatment
Summary of the invention
Because the above-mentioned defect of prior art, technical problem to be solved by this invention is to provide a kind of lake ecological based on Kalman filtering intelligence method for sensing, provide noise track algorithm and the collaboration frequency spectrum method for sensing of high robust, reduce the co-channel interference between lake ecological monitoring sensory perceptual system middle-high density sensing node, strengthen the concurrency of transfer of data between sensing node, significantly improve the channel utilization of cognition wireless sensor network.
For achieving the above object, the invention provides a kind of lake ecological based on Kalman filtering intelligence method for sensing, comprise the steps:
Step 1, definition receive noise average power fluctuation;
Step 2, ARMA Kalman filtering is utilized to follow the tracks of described white Gaussian noise;
Step 3, for Gaussian white noise channel, utilize dynamic threshold spectrum sensing method to ecology carry out intelligence sensing;
Step 4, frequently fading channel is selected for multipath, utilize Kalman filtering scheme to follow the tracks of fading channel information, and by cooperation dynamic threshold spectrum sensing method, intelligence is carried out to ecology and sense;
Step 5, for white Gaussian noise and flat fading channel, utilize Higher-Order Cyclic stationary nature method for sensing to sense, utilize the cooperation cyclostationary characteristic method for sensing of Kalman filtering to resist and frequently select fading channel.
In better embodiment of the present invention, in described step 3, dynamic threshold spectrum sensing method comprises the steps:
Step 3-1, searching sense initial decision threshold in the time cycle in short-term;
Step 3-2, ARM Kalman filter is utilized to follow the tracks of noise;
Step 3-3, receive described noise average power fluctuation and set noise average power fluctuation the factor;
Step 3-4, dynamically update decision threshold according to the described noise average power fluctuation factor;
Step 3-5, carry out that searching is next senses decision threshold in the time cycle in short-term, repeat step 3-2 to 3-5.
In better embodiment of the present invention, the dynamic threshold spectrum sensing method that cooperates under multipath frequency selects fading channel in described step 4 draws together following steps:
Step 4-1, searching sense initial court verdict in the time cycle in short-term;
Step 4-2, Kalman filter is utilized to follow the tracks of fading channel information;
Step 4-3, structure radio resource data storehouse;
Step 4-4, multi-user Cooperation dynamic threshold spectrum sensing method based on described radio resource data storehouse, carry out that searching is next senses court verdict in the time cycle in short-term, repeats step 4-1 to 4-4.
In better embodiment of the present invention, the cyclostationary characteristic method for sensing that cooperates in described step 5 comprises the steps:
Step 5-1, structure Higher-Order Cyclic stationary nature sensor;
Step 5-2, searching sense initial court verdict in the time cycle in short-term;
Step 5-3, Kalman filter is utilized to follow the tracks of fading channel information;
Step 5-4, structure radio resource data storehouse;
Step 5-5, multi-user Cooperation cyclostationary characteristic sensing algorithm based on described radio resource data storehouse, carry out that searchings is next senses court verdict in the time cycle in short-term, repetition step 5-1 to 5-5.
The intelligence of the lake ecological based on the Kalman filtering method for sensing that the present invention provides, for Gaussian channel and multidiameter fading channel environment, arma modeling is adopted to follow the tracks of dynamic threshold spectrum-sensing scheme in conjunction with the noise of Kalman filter, scheme is sensed based on the multistage cyclostationary characteristic sensor algorithm of Kalman filtering and Heuristics database and dynamic cooperative cyclostationary characteristic thereof, provide the intelligent ecological sensing scheme of the multifrequency multi-hop cognition wireless sensor network (CRSN) of lake country high density sensing node, realize Indices of Ecological real time data acquisition and concurrency.
Be described further below with reference to the technique effect of accompanying drawing to design of the present invention, concrete structure and generation, to understand object of the present invention, characteristic sum effect fully.
Accompanying drawing explanation
Fig. 1 is collaboration frequency spectrum sensing schematic diagram in cognition wireless sensor network system;
Fig. 2 is the intelligent method for sensing FB(flow block) of a preferred embodiment of the present invention;
Fig. 3 is the dynamic threshold spectrum sensing method flow chart of a preferred embodiment of the present invention;
Fig. 4 is the cooperation dynamic threshold spectrum sensing method flow chart of a preferred embodiment of the present invention;
Fig. 5 is the cooperation cyclostationary characteristic spectrum sensing method flow chart of a preferred embodiment of the present invention.
Embodiment
The spectrum sensing method of available data research is all constant based on noise average power, and practical application scene is noise average power fluctuation, and the sensing performance of cognitive sensing node is more responsive to noise average power fluctuation.Few about Research Literature in this respect.A preferred embodiment of the present invention provides the noise model of employing, definition noise average power fluctuation and the noise average power fluctuation factor, provide sensing node dynamic threshold spectrum sensing method, and analyze sensing sensitivity, sensing performance, Mathematical Modeling between noise average power and noise average power fluctuation.For the feature of ambient noise randomness, adopt arma modeling to follow the tracks of noise in conjunction with Kalman filter, give noise traceable dynamic threshold spectrum-sensing scheme theoretically.According to cognition wireless sensor network high density sensing node feature, the cooperation dynamic threshold spectrum-sensing scheme of multifrequency multi-hop under fading channel is frequently selected in research simultaneously, analyzes the feasibility of cooperation dynamic threshold spectrum sensing algorithm in cognition wireless sensor network spectrum-sensing system.As shown in Figure 2, the ecological intelligent method for sensing of the Poyang Lake based on Kalman filtering, comprises the steps:
Step 1, definition receive noise average power fluctuation;
Step 2, ARMA Kalman filtering is utilized to follow the tracks of described noise;
Step 3, for Gaussian white noise channel, utilize dynamic threshold spectrum sensing method to ecology carry out intelligence sensing;
Step 4, frequently fading channel is selected for multipath, utilize Kalman filtering fading channel information tracking to follow the tracks of, and by cooperation dynamic threshold spectrum sensing method, intelligence is carried out to ecology and sense;
Step 5, for noise and flat fading, utilize Higher-Order Cyclic stationary nature method for sensing to sense, utilize the cooperation cyclostationary characteristic method for sensing of Kalman filtering to resist and frequently select decline.
Cooperation cyclostationary characteristic method for sensing has the advantage of effectively antagonism Noise and Interference, but to multidiameter fading channel and shadow fading channel ratio more responsive, based on this, build single order, second order, Higher-Order Cyclic stationary nature sensor Mathematical Modeling, introduce cooperation cyclostationary characteristic spectrum sensing algorithm, provide the application of dynamic threshold on multifrequency multi-hop cooperation circulation spectrum sensing algorithm.Set up Radio Resource knowledge base, comprise perception sensing node positional information, accessible spectrum information, spectrum allocation strategy information, space-reception signal strength information, power spectrum degrees of data, shadow region distributed intelligence, authorization user signal type etc.Design Kalman filtering algorithm follows the tracks of the effective Mathematical Modeling of shadow fading of optional position in cognition network region, sets up experience database, proposes the cooperation cyclostationary characteristic sensing scheme based on Kalman filtering and Heuristics database.
As shown in Figure 3, concrete steps are as follows for dynamic threshold spectrum sensing method flow chart:
Step 3-1, searching sense initial decision threshold in the time cycle in short-term;
Step 3-2, ARM Kalman filter is utilized to follow the tracks of noise;
Step 3-3, receive noise average power fluctuation and set noise average power fluctuation the factor;
Step 3-4, according to noise average power fluctuation the factor dynamically update decision threshold;
Step 3-5, carry out that searching is next senses decision threshold in the time cycle in short-term, repeat step 3-2 to 3-5.
Multipath cooperates dynamic threshold spectrum sensing method flow chart as shown in Figure 4 under frequently selecting fading channel, and concrete steps are as follows:
Step 4-1, searching sense initial court verdict in the time cycle in short-term;
Step 4-2, Kalman filter is utilized to follow the tracks of fading channel information;
Step 4-3, structure radio resource data storehouse;
Step 4-4, multi-user Cooperation dynamic threshold spectrum sensing method based on radio resource data storehouse, carry out that searching is next senses court verdict in the time cycle in short-term, repeats step 4-1 to 4-4.
As shown in Figure 5, concrete steps are as follows for cooperation cyclostationary characteristic method for sensing flow chart;
Step 5-1, structure Higher-Order Cyclic stationary nature sensor;
Step 5-2, searching sense initial court verdict in the time cycle in short-term;
Step 5-3, Kalman filter is utilized to follow the tracks of fading channel information;
Step 5-4, structure radio resource data storehouse;
Step 5-5, multi-user Cooperation cyclostationary characteristic sensing algorithm based on radio resource data storehouse, carry out that searchings is next senses court verdict in the time cycle in short-term, repetition step 5-1 to 5-5.
Present pre-ferred embodiments based on the ecological intelligent method for sensing of Poyang Lake of Kalman filtering for Gaussian channel and multidiameter fading channel environment, arma modeling is adopted to follow the tracks of dynamic threshold spectrum-sensing scheme in conjunction with the noise of Kalman filter, scheme is sensed based on the multistage cyclostationary characteristic sensor algorithm of Kalman filtering and Heuristics database and dynamic cooperative cyclostationary characteristic thereof, provide the intelligent ecological sensing scheme of the multifrequency multi-hop cognition wireless sensor network (CRSN) of Poyang Lake District high density sensing node, realize Indices of Ecological real time data acquisition and concurrency.
Present pre-ferred embodiments is for the feature of cognitive sensing node to noise, interference, fading channel sensitivity, towards energy sensing, cyclostationary characteristic senses, cooperate theory analysis, algorithm design and practical application three layer viewpoint cognition wireless sensor network sensing node spectrum-sensings mechanism of sensing scheduling algorithm, solves the co-channel interference of high density sensing node, the concurrency of enhancing transfer of data, raising channel utilization.The definition of the noise average power fluctuation provided according to scheme and theoretical, cycle specificity sensor is theoretical, noise follow the tracks of theoretical, fading channel cooperation sensing is theoretical, provide arma modeling in conjunction with the noise of kalman filter follow the tracks of dynamic threshold spectrum sensing algorithm, based on Kalman filtering and Heuristics database multistage cyclostationary characteristic senses algorithm, dynamic cooperative cyclostationary characteristic senses algorithm, provide the embodiment of algorithm in the intelligent sensing system of Poyang Lake ecology below.
More than describe preferred embodiment of the present invention in detail.Should be appreciated that the ordinary skill of this area just design according to the present invention can make many modifications and variations without the need to creative work.Therefore, all technical staff in the art, all should by the determined protection range of claims under this invention's idea on the basis of existing technology by the available technical scheme of logical analysis, reasoning, or a limited experiment.
Claims (3)
1., based on the lake ecological intelligence method for sensing of Kalman filtering, comprise the steps:
Step 1, definition receive noise average power fluctuation;
Step 2, ARMA Kalman filtering is utilized to follow the tracks of white Gaussian noise;
Step 3, for Gaussian white noise channel, utilize dynamic threshold spectrum sensing method to ecology carry out intelligence sensing;
Step 4, frequently fading channel is selected for multipath, utilize Kalman filtering scheme to follow the tracks of fading channel information, and by cooperation dynamic threshold spectrum sensing method, intelligence is carried out to ecology and sense;
Step 5, for white Gaussian noise and flat fading channel, utilize Higher-Order Cyclic stationary nature method for sensing to sense, utilize the cooperation cyclostationary characteristic method for sensing of Kalman filtering to resist and frequently select fading channel;
In described step 3, dynamic threshold spectrum sensing method comprises the steps:
Step 3-1, searching sense initial decision threshold in the time cycle in short-term;
Step 3-2, ARM Kalman filter is utilized to follow the tracks of noise;
Step 3-3, receive described noise average power fluctuation and set noise average power fluctuation the factor;
Step 3-4, dynamically update decision threshold according to the described noise average power fluctuation factor;
Step 3-5, carry out that searching is next senses decision threshold in the time cycle in short-term, repeat step 3-2 to 3-5.
2., as claimed in claim 1 based on the lake ecological intelligence method for sensing of Kalman filtering, wherein, cooperate under in described step 4, multipath frequency selects fading channel dynamic threshold spectrum sensing method, comprises the steps:
Step 4-1, searching sense initial court verdict in the time cycle in short-term;
Step 4-2, Kalman filter is utilized to follow the tracks of fading channel information;
Step 4-3, structure radio resource data storehouse;
Step 4-4, multi-user Cooperation dynamic threshold spectrum sensing algorithm based on described radio resource data storehouse, carry out that searching is next senses court verdict in the time cycle in short-term, repeats step 4-1 to 4-4.
3., as claimed in claim 1 based on the lake ecological intelligence method for sensing of Kalman filtering, wherein, the cyclostationary characteristic method for sensing that cooperates in described step 5 comprises the steps:
Step 5-1, structure Higher-Order Cyclic stationary nature sensor;
Step 5-2, searching sense initial court verdict in the time cycle in short-term;
Step 5-3, Kalman filter is utilized to follow the tracks of fading channel information;
Step 5-4, structure radio resource data storehouse;
Step 5-5, multi-user Cooperation cyclostationary characteristic sensing algorithm based on described radio resource data storehouse, carry out that searchings is next senses court verdict in the time cycle in short-term, repetition step 5-1 to 5-5.
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| 《A Novel Energy Detection Scheme to Improve Detection Sensitivity in Cognitive Radio Systems》;Guicai yu, etc.;《Wireless Communications, Networking and Mobile Computing (WiCOM),2011 7th International Conference on》;20110925;全文 * |
| 《Research on Energy Detection Algorithm in Cognitive Radio Systems》;Guicai Yu, etc.;《Computer Science and Service System (CSSS), 2011 International Conference on》;20110629;全文 * |
| 《感知无线电系统中改进的循环频谱检测算法》;虞贵财等;《北京邮电大学学报》;20090430;全文 * |
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