CN115290130A - Distributed information estimation method based on multivariate probability quantification - Google Patents
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Abstract
The invention discloses a distributed information estimation method based on multivariate probability quantization, which comprises the following steps: s1, constructing a distributed information estimation scene: comprises a fusion center FC located at the center of wireless communication network and a plurality of wireless communication devices distributed in the wireless communication networkA sensor at the edge of the network; s2, constructing a multivariate probability quantizer for quantizing local observation data by a sensor; s3, optimizing design parameters of multi-element quantization probability function(ii) a S4, designing a quantitative fusion estimator by a fusion center FC and optimizing to obtain an optimal estimation function(ii) a S5. Based onAndthe distributed information estimation is quantized with multivariate probability. The method can adapt to the condition that the quantization result has a plurality of elements and keep higher estimation performance.
Description
Technical Field
The invention relates to distributed information estimation, in particular to a distributed information estimation method based on multivariate probability quantization.
Background
Distributed information estimation based on quantized data has been an active area of research. In a typical distributed estimation framework, local sensors send local observation data of raw information to a fusion center. The fusion center receives data sent from different local sensors and estimates unknown original information by using an estimation algorithm. However, due to bandwidth/energy limitations, the local observed data on the sensors typically needs to be quantified before transmission to the fusion center. The use of the same quantizer for all sensors is a widely adopted solution because it simplifies the design problem.
However, many conventional solutions mainly consider the problem of quantizer optimization in an environment where ideally no observation noise exists. And further research into quantizer designs that take into account observed noise conditions is lacking. Furthermore, many performance analyses and theories regarding the optimal quantizer only consider the case of binary quantization, i.e., the length of the quantized data on the sensor is limited to 1 bit. For the case of quantizing the observed data into multi-bit data on the sensor, that is, when there is a plurality of possibilities for the quantized result, the corresponding quantizer design scheme is also lack of research.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a distributed information estimation method based on multivariate probability quantization, which can adapt to the situation that a quantization result has multivariate and keep higher estimation performance.
The purpose of the invention is realized by the following technical scheme: a distributed information estimation method based on multivariate probability quantization comprises the following steps:
s1, constructing a distributed information estimation scene: the system comprises a fusion center FC positioned in the center of a wireless communication network and a plurality of sensors distributed at the edge of the wireless communication network;
each sensor is responsible for the raw information required by the fusion centerObserving to obtain own local observation data, performing multivariate probability quantization operation on the local observation data, converting continuous observation data into binary discrete data which can be used for digital communication, and sending the binary discrete data to a fusion center FC; the FC fusion center estimates original information according to the quantitative data sent by all the sensors;
s2, constructing a multivariate probability quantizer for quantizing local observation data by a sensor;
S4, designing a quantitative fusion estimator by a fusion center FC and optimizing to obtain an optimal estimation function;
The beneficial effects of the invention are: the multivariate quantization probability method still keeps the capability of approximately linearly decreasing with the total bit number under the condition that the total bit number of the network is changed, and the performance of the multivariate quantization probability method is efficiently estimated in the distributed wireless sensor network.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram of a distributed information estimation scenario;
FIG. 3 is a block diagram of a multivariate probability quantizer;
FIG. 4 is a diagram of a quantization function structure;
FIG. 5 is a block diagram of a quantized fusion estimator;
fig. 6 is a diagram illustrating MSE estimated by the network for the original information under the condition that the total quantization bit number of the entire network changes.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
Aiming at the problem of information estimation based on a bandwidth/energy limited distributed wireless sensor in a future wireless communication network, the invention designs a distributed information estimation scheme based on multivariate probability quantization: the method comprises the steps of designing a multivariate probability quantizer positioned on a sensor and a corresponding multivariate quantization probability function optimization algorithm; the quantitative fusion estimator located on the fusion center is designed and a corresponding estimation function optimization algorithm is designed. Consider a generalized scenario in which a distributed wireless sensor network estimates an unknown raw information, the network containing a Fusion Center (FC) located at a central node of the network and multiple sensors distributed at different locations at the edge of the network. The original information may be any kind of data required by the network, and is determined by the specific requirements of the network, such as common positioning information or weather information. Each sensor observes the original information and obtains its own local observation data, and usually, in an actual environment, due to the influence of environmental noise on the observation, an error exists between the local observation data and the original information. For a sensor with limited bandwidth/energy, the local observation data needs to be quantized first, and the continuous observation data is converted into binary discrete data which can be used for modern digital communication, so that the own observation data can be smoothly sent to the FC. FC can only estimate raw information using quantized data sent from all sensors. The measurement index of the estimation performance of the original information generally uses Mean Squared Error (MSE) of the original information and the estimation value thereof, and smaller MSE means more accurate estimation and better estimation performance;
as shown in fig. 1, a distributed information estimation method based on multivariate probability quantization includes the following steps:
s1, constructing a distributed information estimation scene: as shown in fig. 2, the system comprises a fusion center FC located at the center of the wireless communication network and a plurality of sensors distributed at the edge of the wireless communication network;
each sensor is responsible for the raw information required by the fusion centerObserving to obtain local observation data of the user, performing multivariate probability quantization operation on the local observation data, converting continuous observation data into binary discrete data which can be used for digital communication, and sending the binary discrete data to a fusion center FC; the FC fusion center estimates original information according to the quantized data sent by all the sensors;
s2, constructing a multivariate probability quantizer for quantizing local observation data by a sensor:
multiple sensors distributed at the edge of the network, together with the original information required by the fusion centerAnd (5) observing to respectively obtain own local observation. When all sensor observations are considered here, the observation noise affected by the environment is independently and equally distributed. Therefore, to reduce the difficulty in sensor design and deployment, we also consider the use of the exact same multivariate probability quantizer structure on all sensors, including any adjustable design parameters on the quantizer.
Because independent and equally distributed observation noise on all sensors is considered and the same multivariate quantizer is used. We take any sensor (ignoring sensor numbers) as an example here to describe the process of observation and data quantization on the sensor, and the structure, function and design scheme of the multivariate probability quantizer. As shown in FIG. 3, the sensor observes the raw informationObtaining local observationsBy usingTo express the observed valueRelative to what is observedTo describe the randomness between them. The sensor obtains the observed valueThen inputting the quantized data into a multi-element probability quantizer and outputting the final quantization resultQuantifying the resultsIs a one containsBinary data of bits. Inside the quantizer, the observed value of the inputIs first fed into a multivariate quantization probability controlFunction(s)Is mapped to oneProbability vector of dimensionProbability vectorAll of the elements in (1) areInterval values are taken while satisfying the condition that the sum of the additions is 1, i.e.
Wherein, the first and the second end of the pipe are connected with each other,is a function of design parametersA variable function of control having
WhereinComprises aAll of the parameters that can be adjusted in (c),is the number of design parameters. From the formula(2) It can be seen that by changing the design parametersBy changing the parameter function accordinglyThe functional expressions and structures of (c). FromOutput probability vectorThen fed into the quantization functionIn (A), (B)Detailed in the following and in fig. 4), a decimal one-dimensional discrete value is outputThen we go through decimalQuantization result converted into binary. For quantization functionIts outputA is common toDifferent results, it is desirable to makeTaking the probability of each result entirely fromProbability vector of dimensionControl, i.e. effecting
WhereinRepresenting observed values at a given input multivariate probability quantizerUnder the premise of (1), output is quantizedTake a value ofThe probability of (c).
To implement the above-described function of probability quantization of local observation data by the multivariate probability quantizer, we apply quantization function in the multivariate probability quantizerThe structure shown in fig. 4 is designed.
Wherein the function is quantizedIs inputted byProbability vector of dimensionThe output being a one-dimensional discrete valueIt is composed ofThe serial sublayers with the same structure are composed, and the specific structural functions are as follows:
M sub-layer, M =1,2, \8230;, M: input of M sub-layer (if M =1, its input isAnd) Is output from the previous sublayer (m-1 th sublayer)Vector of dimensionsAnd quantized value(ii) a First, in the m-th sublayer, to be inputtedDivided into two sub-vectors of equal length, each containingAll elements of the first half and all elements of the second half, i.e. twoSubvectors of dimensionsAnd(ii) a Then, the m sub-layer utilizesAndoutputting the quantized valueIn which
Is [0,1 ]]Random noise, function, evenly distributed over intervalsAn input non-negative number will output a1, whereas an input negative number will output a 0. Definition ofThe mth sublayer outputVector of dimensionsIn which
And (3) outputting: quantization functionIs output quantized valueIs that it isQuantized values output by the sub-layersI.e. by。
With the structure as in FIG. 4, the function is quantizedImplements the quantized value of making its outputTaking the probability of each possible outcome entirely from the probability vectorTo control, the function in equation (3) is implemented.
Probability control function is quantized by adjusting the multivariate in the multivariate probability quantizer on the sensor as shown in equations (2) and (3)Design parameters ofCan be varied accordinglyAnd further changing the probability distribution of the quantized data on the sensor relative to the local observed data and the original information. This means that we can optimize the multivariate quantization probability function for the original information subject to different random distributions and the observation noise with different random characteristics under different observation environmentsDesign parameters ofTo obtain an optimal quantized data probability distribution adapted to the current environment. By using Bayesian estimation theory, we consider minimizing the channel by an algorithmAnd determining an estimated MSE lower bound which can be achieved by using quantized data in the fusion center after the sensor quantizes the local observation so as to find out the optimal design parameter suitable for the current observation environmentAnd corresponding use-optimized design parameters on the sensorIs optimized to a multivariate quantization probability function。
We assume a commonThe individual sensors are distributed throughout the network, a multivariate probability quantizer structure as shown in fig. 3 is used on all sensors, and we use the exact same multivariate quantization probability function in all multivariate probability quantizersAnd corresponding design parametersSo as to reduce the design cost and difficulty of the whole network.Each sensor is used for respectively comparing the original informationObserving to obtain own local observation data with observation noise. We consider here the presence of independent identically distributed observation noise on each sensor and define a probability density functionTo describe the distribution of observed noise: for the firstA sensor, its local observation dataIs quantized into a plurality of probability quantizers through a local multivariate probability quantizerBinary data of bitsAnd sent to the FC of the hub. Therefore, it is possible toA sensor jointly generatesAnQuantized data of bitsAnd sent to the FC, which needs to estimate the original information using all the received quantized data.
Through Bayesian probability theory, quantitative data on all sensors is givenAre independently and simultaneously distributed. Therefore, based on the probability distribution of the quantized data, first, the quantized data is calculated when the FC receives the quantized dataFor the original informationThe lower bound of the estimated MSE that can be achieved by the estimation, i.e.
Wherein,Indicating that FC receives all the quantized data sent from the sensor,representing FC utilization quantized dataPair that can be realizedRespectively, to the left of the inequality in the formulaRepresenting original informationAnd its estimated valueThe MSE between the first and second MSE,represents the operation of calculating mathematical expectation, the right side of the inequality in the formula (6) represents the lower bound of the calculated MSE,is the number of combinations in the mathematical definition,
is based on a multivariate quantization probability functionDesign parameters ofAnd originalInformationA series of intermediate calculation terms for the decision. As can be seen from equation (6), in the original informationProbability distribution and observed noise distribution ofFC with both determinants utilizes quantized data pairsMSE estimated, i.e.The lower bound of which is entirely defined by the multivariate quantization probability functionDesign parameters ofAnd (6) determining. Thus, the right side of the inequality in equation (6) is minimized algorithmically byThe determined FC uses the lower bound of the MSE estimated by the quantized data on the original information to find the optimal design parameter adapted to the current observation environmentAnd corresponding optimal multivariate quantization probability function。
Based on the above analysis, we consider an iterative algorithm that is computationally based on a series of samples of raw information collected from the actual observation environment and sensor local observation dataThe method approximately solves the optimization problem about the design parameters in each iteration and gradually approaches the optimal design parameters in the iteration process。
Initialization: total number of sensors isSample set, Is the total number of samples contained in the sample set,is a sample of the original information that was,indicates the serial number of the sample, andrepresenting sensor versus raw information samplesTotal observationsObtained secondarilyAn observation sample; setting initial design parametersSetting the tolerance threshold of iteration toSetting upThe initial iteration count is。
The method comprises the following steps: in the first placeIn the second iteration, the pairs are obtained according to the formula (7)Definition of (2) toIs calculated byMultivariate quantization probability function design parameters obtained in sub-iterationA determined series of intermediate calculation termsWherein
Step two: using as in formula (8)And sample setThe lower MSE bound on the right side of the inequality in equation (6) is approximately calculated as
Then, by using the interior point method and the gradient descent method, the following minimization problem is solved
If not, the explanation needs to continue iteration, and needs to beStep A2 is carried out to carry out the next iteration, and the iteration count is updated(ii) a If the convergence condition is established, outputtingAs an optimal design parameter.
The optimal design parameters can be obtained through the algorithmAnd corresponding to the optimal multivariate quantization probability function。
S4. Fusion center FCSetting a metric fusion estimator and optimizing to obtain an optimal estimation function;
As mentioned in the foregoing description of the preferred embodiment,a sensor jointly generatesAnOf bitsAnd sent to the FC, which needs to estimate the original information using all the received quantized data. Therefore, we first present a quantitative fusion estimator design on FC.
As shown in fig. 5, FC receives a slaveTransmitted from a sensorQuantized dataAnd inputting it into the quantization fusion estimator, outputting the original informationIs estimated value of. In the quantitative fusion estimator, of the inputAnBinary quantized data of bitsIs first converted intoIs arranged atMedian decimal discrete data;
Decimal dataThen the data is sent to an Onehot function for carrying out one-hot coding operation to obtain the corresponding dataOne-hot coded vector. By the firstDecimal dataBy way of example only, it is possible to use,is its corresponding one-hot coded vector,is composed ofbinary data of bit length, toIn total ofThe bits bit are numbered in sequence asBit, decimal dataThe value range of (A) is just the serial number of all bits, and the one-hot coding means that only one bit is codedToThe bit will be set to 1 and all the rest of the bit bits will be set to 0, i.e. the bit is set to 1,
Then, the process of the present invention is carried out,one-hot coded vectorIs fed into an averager to obtain their mean vectors;
After thatIs then fed into the estimation functionIn the method, an estimated value of the original information is output,Is also a design parameterA function of control, i.e.
When the same multivariate probability quantizer as shown in FIG. 3 is used on all sensors, and with the same optimal design parametersOf the multivariate quantization probability functionThen the quantized data sent by all sensors to the FC is relative to the original informationAre conditionally independent and identically distributed;
at the moment, based on Bayesian estimation theory and probability model, the estimation value of FC to the original information is calculatedWith the original informationMSE between is
following the pair in equation (7)By quantifying the optimal design parameters of the probability functionAnd original informationA determined series of intermediate calculation terms;
the optimal design parameters of the probability function in the multivariate quantization are obtained from the formula (13)In the determined caseThe MSE estimated for the original information at FC is entirely determined by the estimation functionVariable design parameters ofControlling;
s404, a series of samples based on original information collected from actual observation environment and local observation data of the sensor and an optimal multivariate quantization probability function on the sensorSolving the optimum estimation function design parameters that minimize the estimated MSE on FC。
Initialization: total number of input sensorsOptimal multivariate quantization probability function on sensorAnd their corresponding optimum design parametersSample setFollowing the pair in equation (14)Definition of (2) to arbitraryWill be composed ofAnd original information samplesIntermediate item of decisionIs approximately calculated as
Setting initial design parametersSetting the tolerance threshold of iteration toSetting an initial iteration count to。
The method comprises the following steps: in the first placeIn the second iteration, defineFor the estimated MSE at FC, using equation (13),and the design parameters obtained in the first iterationWill beIs approximately calculated as
Using the interior point method and the gradient descent method, the following minimization problem is solved
Step two: calculating and viewing convergence criteriaWhether the result is true; if not, continue iteration, willStep B2 is carried out to carry out the next iteration and the iteration count is updated(ii) a If the convergence condition is established, outputtingAs an optimal design parameter.
The optimal design parameters can be obtained through the algorithmAnd corresponding optimal estimation function。
Based on the above two algorithms, we obtain the optimal multivariate quantization probability functions on all sensors respectivelyAnd optimal estimation function on FC. Next, we briefly describe the whole process of the whole network to estimate the original information. The functional structures of the multivariate probability quantizer on the sensor and the quantized fusion estimator on the FC are described in detail above, and are not described herein again.
First of all, the first step is to,individual sensor pair raw informationThe observation was performed separately. By the firstAn example of a sensor, itObtaining own local observation data after observationAnd will beIs sent into as shown in the figure3 (note that at this time, the multivariate quantization probability function in fig. 3Has been optimizedReplacement), and finally outputbinary quantization data of bitIs sent to the FC. All ofA sensor jointly generatesQuantized data. FC receiving signals from all sensorsQuantizes the data and feeds them into a quantized fusion estimator as shown in fig. 5 (note also that in this case, the estimation function in fig. 5Has been optimizedSubstitution), and finally outputs an estimated value of the original information。
In the embodiment of the present application, considering the estimation performance of the proposed distributed information estimation method based on multivariate probability quantization on original information in a practical environment, as described above, we use the Mean Square Error (MSE) of the original information and its estimation value as an evaluation criterion, and a smaller MSE indicates better estimation performance. Specifically, we have tested the MSE performance of the network on the original information estimate when the total quantized bit number of the entire network changes and compared it with the current optimal binary quantization SQMLF method and the lower bound of the theoretical minimum MSE that can be reached on the original information estimate under binary quantization (one-bit quantization). As can be seen from fig. 6, although the SQMLF method has almost at all times completely approached the lower MSE bound for the original information estimation using the constraint of binary quantization, the estimated MSE for the original information using the distributed wireless sensor network of the multivariate probability quantization method is much smaller than both, which means that the multivariate probability quantization method we propose is better than any binary quantization method without considering the constraint on the number of bits to quantize data on the sensor. Furthermore, it can be observed that the multivariate quantization probability method still maintains the capability of approximately linearly decreasing with the total number of quantization bits under the condition that the total number of bits of the network is changed. The method verifies the efficient estimation performance of the multivariate probability quantification method in the distributed wireless sensor network and the adaptability and the expansibility of the total quantification bit number dynamic change of the network in the actual environment.
The foregoing description shows and describes a preferred embodiment of the invention, but as aforementioned, it is to be understood that the invention is not limited to the form disclosed herein, but is not to be construed as excluding other embodiments and from various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A distributed information estimation method based on multivariate probability quantization is characterized in that: the method comprises the following steps:
s1, constructing a distributed information estimation scene: the system comprises a fusion center FC positioned in the center of a wireless communication network and a plurality of sensors distributed at the edge of the wireless communication network;
each sensor is responsible for the raw information required by the fusion centerObserving to obtain own local observation data, performing multivariate probability quantization operation on the local observation data, converting continuous observation data into binary discrete data which can be used for digital communication, and sending the binary discrete data to a fusion center FC; the FC fusion center estimates original information according to the quantized data sent by all the sensors;
s2, constructing a multivariate probability quantizer for quantizing local observation data by a sensor;
S4, designing a quantitative fusion estimator by a fusion center FC and optimizing to obtain an optimal estimation function;
2. The distributed information estimation method based on multivariate probability quantization as claimed in claim 1, characterized in that: when each sensor observes the original information, the observation noise influenced by the environment is independently and identically distributed, and all the sensors use the same multivariate probability quantizer structure.
3. The distributed information estimation method based on multivariate probability quantization as claimed in claim 1, characterized in that: the step S2 includes:
s201, observing original information by a sensorObtaining local observationsBy usingTo express the observed valueRelative to what is observedTo describe randomness therebetween;
s202, obtaining an observed value by a sensorThen inputting the data into a multi-element probability quantizer and outputting a final quantization resultQuantification of the resultsIs a one containsBinary data of bits:
inside the quantizer, the observed value of the inputIs first fed into a multivariate quantization probability control functionIs mapped to oneProbability vector of dimensionProbability vectorAll of the elements in (A) are inInterval values are taken while satisfying the condition that the sum of the additions is 1, i.e.
Wherein, the first and the second end of the pipe are connected with each other,is a function of design parametersA variable function of control having
WhereinComprises aAll of which are adjustableThe parameters of the number of the first and second antennas,is the number of design parameters; by varying design parametersBy changing the parameter function accordinglyThe functional expressions and structures of (a);
fromOutput probability vectorThen fed into the quantization functionIn, one-dimensional discrete value of one decimal is outputThen we go through decimalQuantization result converted into binary(ii) a For quantization functionIts outputA share ofDifferent results, it is desirable to makeTaking the probability of each result entirely fromProbability vector of dimensionControl, i.e. effecting
4. The distributed information estimation method based on multivariate probability quantization as claimed in claim 3, characterized in that: the quantization functionIs inputted byProbability vector of dimensionThe output being a one-dimensional discrete valueIt is composed ofThe serial sublayers with the same structure are composed, and the specific structural functions are as follows:
M sub-layer, M =1,2, \ 8230, M: the input of M sub-layer is output of the last sub-layerVector of dimensionsAnd quantized value(ii) a First, in the m-th sublayer, to be inputtedDivided into two sub-vectors of equal length, each containingAll elements of the first half and all elements of the second half, i.e. twoSubvectors of dimensionsAnd(ii) a Then, the m sub-layer utilizesAndoutputting the quantized valueWherein
Is [0,1 ]]Random noise, function, evenly distributed over intervalsInputting a non-negative number and outputting 1, otherwise, inputting a negative number and outputting 0; definition ofThe mth sublayer outputVector of dimensionsWherein
5. The distributed information estimation method based on multivariate probability quantization as claimed in claim 1, characterized in that: in the step S3, bayesian estimation theory is used, and the method is consideredAfter the determined sensor quantifies the local observation, the fusion center finds the optimal design parameter suitable for the current observation environment by using the estimated MSE lower bound which can be reached by the quantified dataAnd corresponding use-optimized design parameters on the sensorIs optimized to a multivariate quantization probability function。
6. The distributed information estimation method based on multivariate probability quantization as claimed in claim 1, characterized in that: the step S3 includes:
are all provided withThe independent sensors are distributed in the whole network, each sensor adopts the step S2 to construct a multi-element probability quantizer structure, and all the multi-element probability quantizers use the same multi-element quantization probability functionAnd corresponding design parameters;
Each sensor is used for respectively comparing the original informationObserving to obtain own local observation data with observation noise;
Considering the presence of independent and identically distributed observation noise on each sensor, and defining a probability density functionTo describe the distribution of observed noise: for the firstA sensor, its local observation dataIs quantized into a plurality of probability quantizers through a local multivariate probability quantizerBinary data of bitsAnd is sent to the FC of the network center, soA sensor jointly generatesAnQuantized data of bitsAnd sending the information to the FC, wherein the FC needs to estimate the original information by using all received quantized data;
quantitative data on all sensors at a given time by Bayesian probability theoryAlso in the case of (2), since they are independently and identically distributed, based on the probability distribution of the quantized data, it is first calculated when the FC receives the quantized dataFor the original informationThe lower bound of the estimated MSE that the estimation can achieve, i.e. the lower bound
Wherein,Indicating that FC receives all the quantized data sent from the sensor,representing FC utilization quantized dataPair that can be realizedRespectively, to the left of the inequality in the formulaRepresenting original informationAnd its estimated valueThe MSE between the two is the MSE,shows the operation of calculating mathematical expectation, the right side of the inequality in the formula (6) shows the lower bound of the MSE,is the number of combinations in the mathematical definition,
is based on a multivariate quantization probability functionDesign parameters ofAnd original informationThe determined series of intermediate calculation terms, as can be seen from equation (6), are in the original informationProbability distribution and observed noise distribution ofFC utilizes quantized data pairsMSE estimated, i.e.Whose lower bound is fully defined by the multivariate quantization probability functionDesign parameters ofDetermining;
minimizing the right side of the inequality in equation (6) by an algorithmThe determined FC uses the lower bound of the MSE estimated for the original information by the quantized data to find the adaptation to the currentOptimal design parameters under observation environmentAnd corresponding optimal multivariate quantization probability function。
7. The distributed information estimation method based on multivariate probability quantization as claimed in claim 1, characterized in that: in the step S3, optimal design parameters suitable for the current observation environment are obtainedAnd corresponding optimal multivariate quantization probability functionThe process comprises the following steps:
a1, setting the total number of sensors asSample set, Is the total number of samples contained in the sample set,is a sample of the original information that was,indicates the serial number of the sample, andrepresenting sensor versus raw information samplesTotal observationsObtained by(ii) an observation sample; setting initial design parametersSetting the tolerance threshold of iteration toSetting an initial iteration count to;
A2 in the first placeIn the second iteration, the pairs are obtained according to the formula (7)Definition of (2) toIs calculated byMultivariate quantization probability function design parameters obtained in sub-iterationA series of intermediate calculation items of the decisionWherein
A3, using the formula (8)And sample setThe lower MSE bound on the right side of the inequality in equation (6) is approximately calculated as
Then using the interior point method and the gradient descent method, the following minimization problem is solved
if not, the explanation needs to continue iteration, and needs to beStep A2 is carried out to carry out the next iteration, and the iteration count is updated(ii) a If the convergence condition is established, outputtingAs an optimal design parameter, and determining a corresponding optimal multivariate quantization probability function。
8. The distributed information estimation method based on multivariate probability quantization as claimed in claim 1, characterized in that: the step S4 includes:
s401.FC receives a slaveTransmitted from a sensorQuantized dataAnd inputting it into the quantization fusion estimator, outputting the original informationIs estimated value of;
In the quantitative fusion estimator, of the inputAnBinary quantized data of bitsIs first converted intoIs arranged atMean decimal discrete data;
Decimal dataThen the data is sent to an Onehot function for carrying out one-hot coding operation to obtain the corresponding dataOne-hot coded vector;
S402, with the firstDecimal dataBy way of example only, it is possible to use,is its corresponding one-hot coded vector,is composed ofbinary data of bit length toIn totalThe bits bit are numbered in sequence asBit, decimal dataThe value range of (1) is just the serial number of all bits, and the one-hot coding means that only one bit is codedTo (1)The bit will be set to 1 and all the rest of the bit bits will be set to 0, i.e. the bit is set to 1,
Then, the process of the present invention is carried out,one-hot coded vectorIs fed into an averager to obtain their mean vectors;
After thatIs then fed into the estimation functionIn the method, an estimated value of the original information is output,Is also a design parameterA function of control, i.e.
s403. When the same multivariate probability quantizer is used on all sensors, and the method is used with the same optimal design parametersOf the multivariate quantization probability functionThen the quantized data sent by all sensors to the FC is relative to the original informationAre conditionally independent and identically distributed;
at the moment, based on Bayesian estimation theory and probability model, the estimation value of FC to the original information is calculatedWith the original informationMSE between
following the pair in equation (7)By quantifying the optimal design parameters of the probability functionAnd original informationA determined series of intermediate calculation terms;
the optimal design parameters of the probability function in the multivariate quantization are obtained from the formula (13)The MSE estimated on FC for the original information is determined entirely by the estimation functionVariable design parameters ofControlling;
s404, a series of samples based on original information collected from actual observation environment and local observation data of the sensor and an optimal multivariate quantization probability function on the sensorSolving the optimum estimation function design parameters that minimize the estimated MSE on FC。
9. The distributed information estimation method based on multivariate probability quantization as claimed in claim 8, wherein: the step S404 includes:
b1, total number of input sensorsOptimal multivariate quantization probability function on sensorAnd their corresponding optimum design parametersSample setFollowing the pair in equation (14)Definition of (2) to arbitraryWill be composed ofAnd original information samplesIntermediate item of decisionIs approximately calculated as
Setting initial design parametersSetting the tolerance threshold of iteration toSetting an initial iteration count to;
B2 in the firstIn the second iteration, defineFor the estimated MSE at FC, using equation (13),and the design parameters obtained in the first iterationWill beIs approximately calculated as
Using the interior point method and the gradient descent method, the following minimization problem is solved
B3, calculating and checking convergence conditionsWhether the result is true or not; if not, continue iteration, willStep B2 is carried out to carry out the next iteration and the iteration count is updated(ii) a If the convergence condition is established, outputtingAs optimal design parameters and determining corresponding optimal estimation functions。
10. The distributed information estimation method based on multivariate probability quantization as claimed in claim 1, characterized in that: the step S5 includes:
for the firstA sensor, it is toObtaining own local observation data after observationAnd will beFed into a multivariate probability quantizer, multivariate quantization probability functionsGet the bestAnd finally outputbinary quantization data of bitIs sent to FC(ii) a All ofA sensor jointly generatesQuantized data;
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Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130297298A1 (en) * | 2012-05-04 | 2013-11-07 | Sony Computer Entertainment Inc. | Source separation using independent component analysis with mixed multi-variate probability density function |
CN107451101A (en) * | 2017-07-21 | 2017-12-08 | 江南大学 | It is a kind of to be layered integrated Gaussian process recurrence soft-measuring modeling method |
CN110035405A (en) * | 2019-04-18 | 2019-07-19 | 电子科技大学 | A kind of efficient fusion method of Multisensor Distributed based on random set theory |
CN114235257A (en) * | 2021-12-24 | 2022-03-25 | 长春工业大学 | Bolt axial force online accurate monitoring method and device based on data fusion |
CN114900817A (en) * | 2022-07-12 | 2022-08-12 | 香港中文大学(深圳) | Distributed wireless sensor network information estimation method based on binary quantization |
-
2022
- 2022-10-08 CN CN202211229494.1A patent/CN115290130B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130297298A1 (en) * | 2012-05-04 | 2013-11-07 | Sony Computer Entertainment Inc. | Source separation using independent component analysis with mixed multi-variate probability density function |
CN107451101A (en) * | 2017-07-21 | 2017-12-08 | 江南大学 | It is a kind of to be layered integrated Gaussian process recurrence soft-measuring modeling method |
CN110035405A (en) * | 2019-04-18 | 2019-07-19 | 电子科技大学 | A kind of efficient fusion method of Multisensor Distributed based on random set theory |
CN114235257A (en) * | 2021-12-24 | 2022-03-25 | 长春工业大学 | Bolt axial force online accurate monitoring method and device based on data fusion |
CN114900817A (en) * | 2022-07-12 | 2022-08-12 | 香港中文大学(深圳) | Distributed wireless sensor network information estimation method based on binary quantization |
Non-Patent Citations (1)
Title |
---|
郭黎利等: "无线传感器网络中基于多比特量化的极大似然分布式估计方法", 《电子学报》 * |
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