CN101282243A - Method for recognizing distributed amalgamation of wireless sensor network - Google Patents

Abstract

The present invention provides a distributed fusion recognizing method of wireless sensor network. Firstly each node of the wireless sensor network is established with a respective elementary probability distribution model, a sensor reliability analysis model, an uncertainty analysis model and a consistency analysis model. Then these node enabled by the target is executed with estimation to the elementary probability distribution function and sensor reliability analysis. Each node is executed with a time fusion in order to realize result synchronization. A modality fusion should be executed to the node which is equipped with a plurality of types of sensors. Then through analyzing the uncertainty to the recognizing result, the on-line self-adopting selection is executed. When the fusion center receives the element probability distribution and sensor reliability parameter transmitted by the selected node, the consistency analysis is executed. The information fusion is executed combining the reliability of sensor, and the uncertainty and consistency of result, and in this way the distributed fusion recognizing is realized.

Description

Method for recognizing distributed amalgamation of wireless sensor network

Technical field:

The present invention relates to a kind of information processing method of wireless sensor network, particularly a kind of method for recognizing distributed amalgamation of wireless sensor network.

Background technology:

The upsurge that wireless sensor network has caused the great attention of world academia and industrial quarters and caused wireless sensor network research because of its broad prospect of application that represents at aspects such as military and national defense, industrial or agricultural, city management, biologic medical, environmental monitoring, rescue and relief work, anti-probably anti-terrorisms.What wireless sensor network was made up of extensive wireless sensor node of laying at random or bunch point etc. is the network of purpose with the real physical space of perception.Yet, because how the uncertainty and the dynamic changeable characteristics of physical space signal and state thereof work in coordination with a plurality of sensor nodes and obtain correct relevant physical world information from uncertain, incomplete, the local incomplete even wrong signals that they collect the important topic that is Intelligent Information Processing.And simultaneously, wireless sensor network itself also faces significant challenge, because energy, the communication distance of node, the computing capability of node and the memory capacity of node of the network node in the wireless sensor network all are subjected to limitation in height, this makes becomes the important content of current wireless sensor network research based on the information fusion technology of distributed treatment.By Distributed Calculation, calculation task is distributed to each node, merge by decision-making again, reduce the traffic between the node.

Target identification in the wireless sensor network has following characteristics:

1. the applied environment of wireless sensor network is complicated and changeable often, make sensor senses to information have uncertainty, thereby need the environmental analysis module, the environmental sensor information of perceiving is carried out rational analysis, for the sensor reliability analysis provides information.

2. the monitoring objective in the wireless sensor network is dynamic often, the information of its generation also is dynamic change in the time-space domain, in order to obtain enough information about target, the a plurality of node cooperations that need a plurality of different spaces to distribute are finished, because wireless sensor network, be solution preferably based on the fusion method of distributed treatment.Simultaneously, can effectively reduce resource consumption, strengthen and merge performance because the effective information of target in the time-space domain dynamic change, selects those nodes that perception information is the abundantest, resource consumption is minimum to participate in fusion treatment.

3. node in wireless sensor network isomery often, they are all different at the aspects such as mobility of the accuracy of the perception mode of disposal ability, communication bandwidth, communication distance, loss of communications, transducer, sensing range, perception information, reliability, real-time, node, this makes the confidence level of the estimated result that different nodes obtain there are differences, therefore need to estimate the confidence level of each nodal information, for follow-up fusion treatment provides weight coefficient.

4. because the online adaptive characteristic of complicated and changeable, the target property dynamic change of network application environment, network topology dynamic change and node selection strategy, make and participate in the interstitial content dynamic change of merging, this needs blending algorithm that the information source number of participating in fusion is had compatibility feature.

The abundant blending algorithm that is mostly to inherit based on integrated calculating of target identification in the current wireless sensor network, propose several different methods such as Bayes method, cluster analysis, arest neighbors method, DS evidential reasoning, fuzzy reasoning, Rough Set, neural net, but considered the above characteristics of target identification in the wireless sensor network seldom comprehensively.How at these characteristics, inventing suitable target identification method is an important topic of wireless sensor network research.

Summary of the invention:

The object of the present invention is to provide a kind of method for recognizing distributed amalgamation of wireless sensor network, when reliablely and stablely realizing target identification, reduce the resource consumption of network as far as possible.

In order to achieve the above object, method for recognizing distributed amalgamation of wireless sensor network provided by the invention, may further comprise the steps: 1) each is carried out the sensor reliability analysis by target activated wireless sensor network nodes according to the correct identification probability of history, signal-to-noise ratio (SNR) estimation result, Target Distance Estimation result, environmental analysis result etc., and the computed reliability coefficient is also closed the node that there is sensor fault in those; 2) node of operate as normal is by Signal Pretreatment, feature extraction and analysis, based on hierarchical structure, adopt the method for fuzzy reasoning to carry out the estimation of the elementary probability partition function of multi-class targets, in training process, determine model parameter by cluster and steepest descending method; 3) the operate as normal node realizes that by the result of current time and historical results are merged the result upgrades, and it is asynchronous to eliminate the result that sample rate because of transducer, difference that the result produces speed cause simultaneously; 4) node for equipment polytype transducer also will carry out the mode fusion, and the information of comprehensive utilization different attribute strengthens estimation effect, and the result that different modalities is produced is unified simultaneously arrives in the identical framework; 5) estimated result is carried out analysis of uncertainty, utilize fuzzy entropy to come the uncertainty of estimated result, assessment result is to the degree of support of follow-up fusion treatment, take all factors into consideration maximum communication distance, dump energy, node simultaneously to the connection status of fusion center, the jumping figure that transmission needs etc., carry out node to select; 6) elementary probability partition function and coefficient of reliability that the node of choosing sends separately arrive fusion center, fusion center carries out analysis of uncertainty as a result and consistency analysis, the combined reliability parameter, result's confidence level is respectively uploaded in estimation, utilizes improved evidence theory to carry out amalgamation judging.

Preferable, the sensor reliability estimation model of being set up is:

$k_i={\mathrm{\α}}_i*{\mathrm{CR}}_i*e^{-(\frac{{({\mathrm{SNR}}_i-{\mathrm{SNR}}_{i\max })}^2}{2*{{\mathrm{SNR}}^2}_{i\max }}+\frac{{(D_i-D_{i\min })}^2}{2*{D^2}_{i\min }+1})},$ Wherein, k _iBe the coefficient of reliability of transducer i, α _iBe environmental impact coefficient, CR _iBe the historical correct identification probability of transducer i, SNR _iBe the current signal-to-noise ratio (SNR) estimation of transducer i, SNR _ImaxBe the maximum signal to noise ratio of transducer i, D _iBe the current goal distance estimations of transducer i, D _IminBe the minimum target distance of transducer i, to add one be to prevent that denominator from being zero to one denominator behind the index.

Preferable, the elementary probability apportion model is:

$m_i\left(A_l\right)=\frac{\left|g^l\right|*u_{i,A_l}^o\left(X\right)}{\underset{l=1}{\overset{N}{\mathrm{\Σ}}}\left|g^l\right|*u_{i,A_l}^o\left(X\right)},$ Wherein, m _i(A _l) be that transducer i is about classification A _lElementary probability distribute g ^lBe the de-fuzzy coefficient,

Be that transducer i is about classification A _lFuzzy output, X is a characteristic vector, N is the classification number.

Preferable, the time Fusion Model is:

$m_i\left(A_l\right)=\frac{\mathrm{\β}*m_i^h\left(A_l\right)+m_i^c\left(A_l\right)}{1+\mathrm{\β}},$ Wherein, m _i(Al) be that transducer i is about classification A _lNew elementary probability distribute m _i ^h(A _l) be the historical elementary probability distribution of transducer i, m _i ^c(A _l) be the current elementary probability distribution of transducer i, β is a fusion coefficients, adjusts current results and the historical results influence degree to fusion results.

Preferable, the mode Fusion Model is:

$\stackrel{\‾}{k}=\underset{A_i\∩B_j\≠\mathrm{\φ}}{\mathrm{\Σ}}{\left(\frac{u(A_i\∩B_j)}{u\left(A_i\right)u\left(B_j\right)}\right)}^{\mathrm{\λ}}{m}_1\left(A_i\right)m_2\left(B_j\right),$

$\left\{\begin{array}{c}m\left(C\right)={\stackrel{\‾}{k}}^{-1}\underset{A_i\∩B_j\≠C}{\mathrm{\Σ}}{\left(\frac{u(A_i\∩B_j)}{u\left(A_i\right)u\left(B_j\right)}\right)}^{\mathrm{\λ}}{m}_1\left(A_i\right)m_2\left(B_j\right),\stackrel{\‾}{k}\≠0\\ m(A\∪B)=m\left(A\right)\×m\left(B\right),\stackrel{\‾}{k}=0\end{array}\right.,$ Wherein, m _i( ^*) described in claim 4,

Be non-conflict item, m (C) and m (A ∪ B) are fusion results, and λ is a corrected parameter, adjust to intersect the influence of degree to merging in the framework between classification.

Preferable, the node preference pattern is:

${\mathrm{<figure-callout\; id="1"\; label="e\; C"\; filenames="A20081006005800161.png"\; state="\{\{state\}\}">e</figure-callout>}}^{C_{}}*e^{-{\mathrm{<figure-callout\; id="2"\; label="C"\; filenames="A20081006005800161.png"\; state="\{\{state\}\}">C</figure-callout>}}_2*\frac{{\tilde{H}}_i}{{\tilde{H}}_{i\max }}}*e^{-{\mathrm{<figure-callout\; id="3"\; label="C"\; filenames="A20081006005800131.png,A20081006005800161.png"\; state="\{\{state\}\}">C</figure-callout>}}_3*\frac{D_{\mathrm{inc}}}{D_{\mathrm{icomm}\max }}}\&GreaterEqual;T_{\mathrm{node}\_\mathrm{choose}},$ Wherein, E _iBe the dump energy of node i, E _ImaxBe the primary power of node i,

Estimate for the uncertainty of current results, represent with fuzzy entropy,

${\tilde{H}}_i=-\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{m}_i\left(A_j\right)*{\log }_2\left(m_i\left(A_j\right)\right)-\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}(1-m_i\left(A_j\right))*{\log }_2(1-m_i\left(A_j\right)),$

Be maximum fuzzy entropy, D _IncBe the distance of node i to fusion center, C _{Icomm max}Be the maximum communication distance of node i, C ₁, C ₂, C ₃For weight is adjusted parameter, m _i( ^*) described in claim 5.

Preferable, the Intelligent Fusion model is:

$m\left(A\right)=\frac{\underset{\&cap;A_i=A}{\mathrm{\&Sigma;}}\underset{1\&le;i\&le;}{\mathrm{\&Pi;}}{m}_i^{*}\left(A_i\right)}{1-\underset{\&cap;A_i=\mathrm{\&phi;}}{\mathrm{\&Sigma;}}\underset{1\&le;i\&le;}{\mathrm{\&Pi;}}{m}_i^{*}\left(A_i\right)},$ Wherein, $\left\{\begin{array}{c}{m}_i^{*}\left(A\right)=w_i{m}_i\left(A\right),A\&Subset;\mathrm{\&Theta;}\\ m_i^{*}\left(\mathrm{\&Theta;}\right)=w_i{m}_i\left(\mathrm{\&Theta;}\right)+1-w_i\end{array}\right.,$ Θ is the set of all categories, $w_i=\frac{{\tilde{w}}_i}{\max \left\{{\tilde{w}}_i\right\}}$ Be weight coefficient, ${\tilde{w}}_i=\frac{k_i*\mathrm{co}{h}_i}{{\tilde{H}}_i},$ k _iDescribed in claim 2,

Described in claim 6, coh _iBe parameter of consistency, model is:

${\mathrm{coh}}_i=\mathrm{support}\left(S_i\right)/\underset{i=1}{\overset{K}{\mathrm{\&Sigma;}}}\mathrm{support}\left(S_i\right),$ $\mathrm{support}\left(S_i\right)=\underset{j=1,j\&NotEqual;i}{\overset{K}{\mathrm{\&Sigma;}}}\mathrm{sim}(S_i,S_j),$

D (S _i, S _j) be the Jousselme distance between the result of the result of node i and node j, γ is for adjusting parameter, m _i( ^*) described in claim 5.

On the basis of the present invention's target identification characteristics in the research wireless sensor network,, come the analyte sensors Reliability of Information by the reliability of assessment transducer, result's uncertainty and consistency each other; By the node selection strategy of online adaptive, take all factors into consideration information increment and energy consumption, on the basis of guaranteed performance, reduce resource consumption as far as possible; Improved evidence synthetic method has extensibility to the node number of participating in fusion.

Description of drawings:

Fig. 1 is the overall framework figure of method for recognizing distributed amalgamation of wireless sensor network.

Fig. 2 is a sensor node reliability analysis model block diagram.

Fig. 3 is a transducer mode Fusion Model block diagram.

Fig. 4 is a node preference pattern block diagram.

Fig. 5 is the Intelligent Fusion model framework chart.

Fig. 6 is the distributed fuse recognition system block diagram of battlefield, ground reconnaissance net.

Fig. 7 is a sound channel elementary probability apportion model block diagram.

Fig. 8 is vibrations passage elementary probability apportion model block diagram.

Fig. 9 is simulation result figure.

Embodiment:

Below be applied to an example of target identification in the reconnaissance net of battlefield, ground for the present invention.

Main target comprises people, wheeled vehicle, creeper truck in the reconnaissance net of battlefield, ground, sensor node equipment sound, two types of transducers of vibrations, and after target entered scope of reconnaissance, network carried out target detection, identification, tracking.Because the complexity of battlefield surroundings, the dynamic variation characteristic of target morphology, dynamic variation characteristic of network topology or the like, the information that perceives often has uncertainty.By the assessment sensor reliability, uncertainty and consistency are carried out the confidence level analysis as a result, through Intelligent Fusion judgement carry out distributed fusion recognition a kind of effective ways of can yet be regarded as.

Concrete steps are as follows:

1. sensor reliability analysis

After sensor node is activated by target, sound and vibrations passage be image data respectively, carry out signal-to-noise ratio (SNR) estimation and Target Distance Estimation then, by the perception information of environmental sensor, carry out environmental analysis again, what influence voice signal mainly is wind direction, what influence vibration signal mainly is geological conditions, comprehensive then historical correct identification probability, signal-to-noise ratio (SNR) estimation, Target Distance Estimation and environmental impact coefficient, according to carrying out the sensor reliability analysis, model is:

$k_i={\mathrm{\&alpha;}}_i*C{R}_i*e^{-(\frac{{({\mathrm{SNR}}_i-{\mathrm{SNR}}_{i\max })}^2}{2*{{\mathrm{SNR}}^2}_{i\max }}+\frac{{(D_i-D_{i\min })}^2}{2*{D^2}_{i\min }+1})},$ Wherein, k _iBe the coefficient of reliability of transducer i, α _iBe environmental impact coefficient, CR _iBe the historical correct identification probability of transducer i, SNR _iBe the current signal-to-noise ratio (SNR) estimation of transducer i, SNR _ImaxBe the maximum signal to noise ratio of transducer i, D _iBe the current goal distance estimations of transducer i, D _IminBe the minimum target distance of transducer i, to add one be to prevent that denominator from being zero to one denominator behind the index.

2. the elementary probability partition function is estimated

Sound channel and vibrations passage carry out elementary probability according to figure seven and figure eight respectively and distribute, and wherein Fuzzy Inference Model is:

$m_i\left(A_l\right)=\frac{\left|g^l\right|*u_{i,A_l}^o\left(X\right)}{\underset{l=1}{\overset{N}{\mathrm{\&Sigma;}}}\left|g^l\right|*u_{i,A_l}^o\left(X\right)},$ Wherein, m _i(A _l) be that transducer i is about classification A _lElementary probability distribute,

Be that transducer i is about classification A _lFuzzy output, its model is:

$u_{i,A_l}^o\left(X\right)=\underset{k=1}{\overset{p}{\mathrm{\&Pi;}}}\exp \{-0.5*\frac{{({x_k}^{\&prime;}-m_k^l)}^2}{{{\mathrm{\&sigma;}}_{\mathrm{<figure-callout\; id="2"\; label="k"\; filenames="A20081006005800161.png"\; state="\{\{state\}\}">k</figure-callout>}}}^2+{\mathrm{\&sigma;}}_k^{l2}}\},$ X is a characteristic vector, and N is the classification number, g ^lBe the de-fuzzy coefficient, m _k ^l, σ _k, σ _k ^lBe model parameter, p is the characteristic vector dimension.

3. the time merges

Press the time Fusion Model at sound and vibrations passage respectively $m_i\left(A_l\right)=\frac{\mathrm{\&beta;}*m_i^h\left(A_l\right)+m_i^c\left(A_l\right)}{1+\mathrm{\&beta;}}$ Handle m _i(A _l) be that transducer i is about classification A _lNew elementary probability distribute m _i ^h(A _l) be the historical elementary probability distribution of transducer i, m _i ^c(A _l) be the current elementary probability distribution of transducer i, β is a fusion coefficients, adjusts current results and the historical results influence degree to fusion results.

4. mode merges

According to the mode Fusion Model

$\left\{\begin{array}{c}m\left(C\right)={\stackrel{\&OverBar;}{k}}^{-1}\underset{A_i\&cap;B_j\&NotEqual;C}{\mathrm{\&Sigma;}}{\left(\frac{u(A_i\&cap;B_j)}{u\left(A_i\right)u\left(B_j\right)}\right)}^{\mathrm{\&lambda;}}{m}_1\left(A_i\right)m_2\left(B_j\right),\stackrel{\&OverBar;}{k}\&NotEqual;0\\ m(A\&cup;B)=m\left(A\right)\&times;m\left(B\right),\stackrel{\&OverBar;}{k}=0\end{array}\right.$ Carry out the fusion of sound and vibrations passage, wherein, $\stackrel{\&OverBar;}{k}=\underset{A_i\&cap;B_j\&NotEqual;\mathrm{\&phi;}}{\mathrm{\&Sigma;}}{\left(\frac{u(A_i\&cap;B_j)}{u\left(A_i\right)u\left(B_j\right)}\right)}^{\mathrm{\&lambda;}}{m}_1\left(A_i\right)m_2\left(B_j\right),$

m _i( ^*) be the result of step 3,

Be non-conflict item, m (C) and m (A ∪ B) are fusion results, and λ is a corrected parameter, adjust to intersect the influence of degree to merging in the framework between classification.

5. node is selected

According to the node preference pattern $e^{C_{}}$ ${{}_1*\frac{E_i}{E_{i\max }}}^{}*e^{-{\mathrm{<figure-callout\; id="2"\; label="C"\; filenames="A20081006005800161.png"\; state="\{\{state\}\}">C</figure-callout>}}_2*\frac{{\tilde{H}}_i}{{\tilde{H}}_{i\max }}}*e^{-{\mathrm{<figure-callout\; id="3"\; label="C"\; filenames="A20081006005800131.png,A20081006005800161.png"\; state="\{\{state\}\}">C</figure-callout>}}_3*\frac{D_{\mathrm{inc}}}{D_{\mathrm{icomm}\max }}}\&GreaterEqual;T_{\mathrm{node}\_\mathrm{choose}}$ Carry out the node online adaptive and select, wherein, E _iBe the dump energy of node i, E _ImaxBe the primary power of node i,

Estimate for the uncertainty of current results, represent with fuzzy entropy:

${\tilde{H}}_i=-\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{m}_i\left(A_j\right)*{\log }_2\left(m_i\left(A_j\right)\right)-\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}(1-m_i\left(A_j\right))*{\log }_2(1-m_i\left(A_j\right)),$

Be maximum fuzzy entropy, D _IncBe the distance of node i to fusion center, D _{Icomm max}Be the maximum communication distance of node i, C ₁, C ₂, C ₃For weight is adjusted parameter, m _i( ^*) be the result of step 4

6. Intelligent Fusion judgement

According to the Intelligent Fusion model

$m\left(A\right)=\frac{\underset{\&cap;A_i=A}{\mathrm{\&Sigma;}}\underset{1\&le;i\&le;n}{\mathrm{\&Pi;}}{m}_i^{*}\left(A_i\right)}{1-\underset{\&cap;A_i=\mathrm{\&phi;}}{\mathrm{\&Sigma;}}\underset{1\&le;i\&le;n}{\mathrm{\&Pi;}}{m}_i^{*}\left(A_i\right)}$ The classification that fusion treatment, selection have maximum elementary probability distribution is a court verdict, wherein, $\left\{\begin{array}{c}{m}_i^{*}\left(A\right)=w_i{m}_i\left(A\right),A\&Subset;\mathrm{\&Theta;}\\ m_i^{*}\left(\mathrm{\&Theta;}\right)=w_i{m}_i\left(\mathrm{\&Theta;}\right)+1-w_i\end{array}\right.,$ Θ is the set of all categories, $w_i=\frac{{\tilde{w}}_i}{\max \left\{{\tilde{w}}_i\right\}}$ Be weight coefficient, ${\tilde{w}}_i=\frac{k_i*{\mathrm{coh}}_i}{{\tilde{H}}_i},$ k _iBe the result of step 1, For being the result of step 5, coh _iBe parameter of consistency, model is:

${\mathrm{coh}}_i=\mathrm{support}\left(S_i\right)/\underset{i=1}{\overset{K}{\mathrm{\&Sigma;}}}\mathrm{support}\left(S_i\right),$ $\mathrm{support}\left(S_i\right)=\underset{j=1,j\&NotEqual;i}{\overset{K}{\mathrm{\&Sigma;}}}\mathrm{sim}(S_i,S_j),$

D (S _i, S _j) be the Jousselme distance between the result of the result of node i and node j, γ is for adjusting parameter, m _i( ^*) be the result of step 4.

Simulation result:

Suppose that simulated environment is better, α _i=1; The historical correct identification probability of sound channel is 95%, and the historical correct identification probability of vibrations passage is 90%; Maximum signal to noise ratio is 30dB, and the minimum target distance is 1; Time fusion coefficients β=0.9; Mode merges correction factor λ=0.6; Residue energy of node is sufficient and make that based on the procotol of intelligent mobile agent the distance of fusion center is very short, so C ₁=C ₃=0, C ₂=1.5, T _{Node_choose}=0.31; In the emulation, lay node road both sides, the speed that creeper truck does not wait with 20～40km/h is selected through the online adaptive node in travels down, and synchronization is participated in the node number that merges and is 3～5 and do not wait.Figure nine is a simulation result, above red line be distributed fusion recognition result, below blue line be single node identification average result.From figure we as can be seen, distributed fusion identification method has improved the reliability and stability of target identification.

In sum, the present invention is directed to the characteristics of target identification in the wireless sensor network, sensor reliability, information uncertainty, factor such as consistency are as a result taken all factors into consideration in proposition, improve evidence theory based on discount thought, thereby carry out the distributed intelligence fusion recognition, improved the reliability and stability of target identification.Owing to considered the selection of node simultaneously, the present invention has certain effect to reducing network resource consumption.

Claims (7)

1. method for recognizing distributed amalgamation of wireless sensor network is characterized in that may further comprise the steps:

1) each is carried out the sensor reliability analysis by target activated wireless sensor network nodes according to the correct identification probability of history, signal-to-noise ratio (SNR) estimation result, Target Distance Estimation result, environmental analysis result etc., and the computed reliability coefficient is also closed the node that there is sensor fault in those;

2) node of operate as normal is by Signal Pretreatment, feature extraction and analysis, based on hierarchical structure, adopt the method for fuzzy reasoning to carry out the estimation of the elementary probability partition function of multi-class targets, in training process, determine model parameter by cluster and steepest descending method;

3) the operate as normal node realizes that by the result of current time and historical results are merged the result upgrades, and it is asynchronous to eliminate the result that sample rate because of transducer, difference that the result produces speed cause simultaneously;

4) node for equipment polytype transducer also will carry out the mode fusion, and the information of comprehensive utilization different attribute strengthens estimation effect, and the result that different modalities is produced is unified simultaneously arrives in the identical framework;

5) estimated result is carried out analysis of uncertainty, utilize fuzzy entropy to come the uncertainty of estimated result, assessment result is to the degree of support of follow-up fusion treatment, take all factors into consideration maximum communication distance, dump energy, node simultaneously to the connection status of fusion center, the jumping figure that transmission needs etc., carry out node to select;

6) elementary probability partition function and coefficient of reliability that the node of choosing sends separately arrive fusion center, fusion center carries out analysis of uncertainty as a result and consistency analysis, the combined reliability parameter, result's confidence level is respectively uploaded in estimation, utilizes improved evidence theory to carry out amalgamation judging.

2. method for recognizing distributed amalgamation of wireless sensor network as claimed in claim 1 is characterized in that: the sensor reliability estimation model of being set up is

$k_i={\mathrm{\&alpha;}}_i*C{R}_i*e^{-(\frac{{({\mathrm{SNR}}_i-{\mathrm{SNR}}_{i\max })}^2}{2*{{\mathrm{SNR}}^2}_{i\max }}+\frac{{(D_i-D_{i\min })}^2}{2*{D^2}_{i\min }+1})},$ Wherein, k _iBe the coefficient of reliability of transducer i, α _iBe environmental impact coefficient, CR _iBe the historical correct identification probability of transducer i, SNR _iBe the current signal-to-noise ratio (SNR) estimation of transducer i, SNR _ImaxBe the maximum signal to noise ratio of transducer i, D _iBe the current goal distance estimations of transducer i, D _IminBe the minimum target distance of transducer i, to add one be to prevent that denominator from being zero to one denominator behind the index.

3. method for recognizing distributed amalgamation of wireless sensor network as claimed in claim 1 is characterized in that: the elementary probability apportion model is

$m_i\left(A_l\right)=\frac{\left|g^l\right|*u_{i,A_l}^o\left(X\right)}{\underset{l=1}{\overset{N}{\mathrm{\&Sigma;}}}\left|g^l\right|*u_{i,A_l}^o\left(X\right)},$ Wherein, m _i(A _l) be that transducer i is about classification A _lElementary probability distribute g ^lBe the de-fuzzy coefficient,

Be that transducer i is about classification A _lFuzzy output, X is a characteristic vector, N is the classification number.

4. method for recognizing distributed amalgamation of wireless sensor network as claimed in claim 1 is characterized in that: the time Fusion Model is

$m_i\left(A_l\right)=\frac{\mathrm{\&beta;}*m_i^h\left(A_l\right)+m_i^c\left(A_l\right)}{1+\mathrm{\&beta;}},$ Wherein, m _i(A _l) be that transducer i is about classification A _lNew elementary probability distribute m _i ^h(A _l) be the historical elementary probability distribution of transducer i, m _i ^c(A _l) be the current elementary probability distribution of transducer i, β is a fusion coefficients, adjusts current results and the historical results influence degree to fusion results.

5. method for recognizing distributed amalgamation of wireless sensor network as claimed in claim 1 is characterized in that: the mode Fusion Model is

$\stackrel{\&OverBar;}{k}=\underset{A_i\&cap;B_j\&NotEqual;\mathrm{\&phi;}}{\mathrm{\&Sigma;}}{\left(\frac{u(A_i\&cap;B_j)}{u\left(A_i\right)u\left(B_j\right)}\right)}^{\mathrm{\&lambda;}}{m}_1\left(A_i\right)m_2\left(B_j\right),$

$\left\{\begin{array}{c}m\left(C\right)={\stackrel{\&OverBar;}{k}}^{-1}\underset{A_i\&cap;B_j\&NotEqual;C}{\mathrm{\&Sigma;}}{\left(\frac{u(A_i\&cap;B_j)}{u\left(A_i\right)u\left(B_j\right)}\right)}^{\mathrm{\&lambda;}}{m}_1\left(A_i\right)m_2\left(B_j\right),\stackrel{\&OverBar;}{k}\&NotEqual;0\\ m(A\&cup;B)=m\left(A\right)\&times;m\left(B\right),\stackrel{\&OverBar;}{k}=0\end{array}\right.,$ Wherein, m _i( ^*) described in claim 4, Be non-conflict item, m (C) and m (A ∪ B) are fusion results, and λ is a corrected parameter, adjust to intersect the influence of degree to merging in the framework between classification.

6. method for recognizing distributed amalgamation of wireless sensor network as claimed in claim 1 is characterized in that: the node preference pattern is

$e^{C_1*\frac{E_i}{E_{i\max }}}*e^{-C_2*\frac{{\tilde{H}}_i}{{\tilde{H}}_{i\max }}}*e^{-C_3*\frac{D_{\mathrm{inc}}}{D_{\mathrm{i\; comm}\max }}}\&GreaterEqual;T_{\mathrm{node}\_\mathrm{choose}},$ Wherein, E _iBe the dump energy of node i, E _ImaxBe the primary power of node i,

Estimate for the uncertainty of current results, represent with fuzzy entropy, ${\tilde{H}}_i=-\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{m}_i\left(A_j\right)*{\log }_2\left(m_i\left(A_j\right)\right)-\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}(1-m_i\left(A_j\right))*{\log }_2(1-m_i\left(A_j\right)),$

Be maximum fuzzy entropy, D _IncBe the distance of node i to fusion center, D _{Icomm max}Be the maximum communication distance of node i, C ₁, C ₂, C ₃For weight is adjusted parameter, m _i( ^*) described in claim 5.

7. method for recognizing distributed amalgamation of wireless sensor network as claimed in claim 1 is characterized in that: the Intelligent Fusion model is

$m\left(A\right)=\frac{\underset{\&cap;A_i=A}{\mathrm{\&Sigma;}}\underset{1\&le;i\&le;n}{\mathrm{\&Pi;}}{m}_i^{*}\left(A_i\right)}{1-\underset{\&cap;A_i=\mathrm{\&phi;}}{\mathrm{\&Sigma;}}\underset{1\&le;i\&le;n}{\mathrm{\&Pi;}}{m}_i^{*}\left(A_i\right)},$ Wherein, $\left\{\begin{array}{c}{m}_i^{*}\left(A\right)=w_i{m}_i\left(A\right),A\&Subset;\mathrm{\&Theta;}\\ m_i^{*}\left(\mathrm{\&Theta;}\right)=w_i{m}_i\left(\mathrm{\&Theta;}\right)+1-w_i\end{array}\right.,$ Θ is the set of all categories, $w_i=\frac{{\tilde{w}}_i}{\max \left\{{\tilde{w}}_i\right\}}$ Be weight coefficient, ${\stackrel{\&not;}{w}}_i=\frac{k_i*{\mathrm{coh}}_i}{{\stackrel{\&not;}{H}}_i},$ k _iDescribed in claim 2,

Described in claim 6, coh _iBe parameter of consistency, model is

${\mathrm{coh}}_i=\mathrm{support}\left(S_i\right)/\underset{i=1}{\overset{K}{\mathrm{\&Sigma;}}}\mathrm{support}\left(S_i\right),$ $\mathrm{support}\left(S_i\right)=\underset{j=1,j\&NotEqual;i}{\overset{K}{\mathrm{\&Sigma;}}}\mathrm{sim}(S_i,S_j),$

D (S _i, S _j) be the Jousselme distance between the result of the result of node i and node j, γ is for adjusting parameter, m _i( ^*) described in claim 5.

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