CN107426748A - Multisensor estimates performance methodology in a kind of wireless network control system - Google Patents

Multisensor estimates performance methodology in a kind of wireless network control system Download PDF

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CN107426748A
CN107426748A CN201710256853.5A CN201710256853A CN107426748A CN 107426748 A CN107426748 A CN 107426748A CN 201710256853 A CN201710256853 A CN 201710256853A CN 107426748 A CN107426748 A CN 107426748A
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CN107426748B (en
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胡亮
任祝
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Zhejiang Sci Tech University ZSTU
Zhejiang University of Science and Technology ZUST
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3913—Predictive models, e.g. based on neural network models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/021—Estimation of channel covariance
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic

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Abstract

The present invention relates to network control technology field, discloses multisensor in a kind of wireless network control system and estimates performance methodology, comprises the following steps：(1) wireless network control system sensor parameters are obtained, establish the dynamic characteristic and sensor measurement equation of process；(2) multiple sensors are connected, two channels are set between each two sensor；Channel using wherein one connection carries out data transmission, if this channel starts packet loss, channel is switched over；The predicted value of sensor and predicting covariance matrix at single channel and multichannel center are calculated respectively；(3) to, by two transmission performance comparisions, completing the predicted value and predicting covariance matrix data situation data cases in circulation to channel according to step (2), the comparison for generating multiple channels is drawn between two sensors.The present invention ensures in multichannel transmission systems efficient scheduling sensor to reduce evaluated error using Kalman filtering algorithm.

Description

Multisensor estimates performance methodology in a kind of wireless network control system
Technical field
The present invention relates to network control technology field, estimates more particularly to multisensor in a kind of wireless network control system Count performance methodology.
Background technology
With the development of modern communicationses science and technology, wireless network networked control systems (WirelessNCSs, WNCSs) are Traditional cable network networked control systems are gradually substituted, for cable network, wireless network passes through wireless medium transmissions Data, so as to eliminate the installation wiring process of complexity, also make it that the maintenance and upgrade of system from now on is more convenient, effective section Use cost is saved.Because the node that can move inside network in WNCSs be present, and the communication context of system by The influence of the quantity of node and the power of transmitting and reception, can't be by the control connected up, so which increase system Mobility and expansion.More importantly the place that some people can not reach either environment very severe place, WNCSs can be still operated well.
Although wireless network networked control systems have many good qualities, the current system still suffers from actual use Lot of challenges.On the one hand, exist in WNCSs as data packet transmission delay, data-bag lost, clock synchronization and package time sequence are wrong The problem of to be resolved in the network control system (NetworkedControlSystems, NCSs) such as random.On the other hand, The fading of wireless channel in WNCSs be present and be easily disturbed, the energy saving requirement of radio node (such as wireless senser), communication letter The problems such as road, bandwidth and frequency spectrum resource are limited, these all turn into the obstacle that WNCSs is studied and applied.In addition for wireless channel Packet loss problem, existing research focuses primarily upon single-sensor estimation problem, and this estimation model is difficult to obtain comprehensively, stably Information and also transmission range it is limited, can not meet improve constantly control system performance requirement.
The content of the invention
The present invention is directed to deficiency of the prior art, there is provided multisensor estimates performance in a kind of wireless network control system Method, the inventive method consider multisensor channel switching problem in wireless network control system, it is ensured that in multichannel Efficient scheduling sensor completes multisensor in wireless network control system to reduce the purpose of evaluated error in Transmission system Estimate the optimization of performance.
In order to solve the above-mentioned technical problem, the present invention is addressed by following technical proposals.
Multisensor estimates performance methodology in a kind of wireless network control system, comprises the following steps：
(1) wireless network control system sensor parameters are obtained, establish the dynamic characteristic and sensor measurement equation of process：
Wherein, A ∈ Rn*nRepresent sytem matrix, C ∈ Rm*nRepresent the observing matrix of row full rank, xk∈RnAnd yk∈RmRespectively Represent state and the measurement of sensor, wk∈RnAnd vk∈RmIt is the Gaussian process of zero-mean, Q is wkCovariance matrix, R is vk Covariance matrix, meet Q ＞ 0 and R ＞ 0；
(2) multiple sensors are connected, two channels are set between each two sensor, while set the ginseng of every channel Number；Channel using wherein one connection carries out data transmission, if this channel starts packet loss, channel is switched over； Cycle-index is set, initializes x0, P0, using Kalman filtering algorithm, sensor at single channel and multichannel center is calculated respectively Predicted value and predicting covariance matrix, accounting equation is：
WhereinCentered on locate sensor predicted value, Pk|kFor predicting covariance matrix, P is calculatedk|kPole Limit value, until circulation is completed；
(3) to, by two transmission performance comparisions, completing to circulate to channel according to step (2) between two sensors In predicted value and predicting covariance matrix data situation data cases, generate multiple channels comparison draw.
Preferably, in step (1), the original state x of system0It is that average is 0 and covariance matrix is P0＞ 0 Gauss Random vector, wk, vkAnd x0It is separate.
The present invention has significant technique effect as a result of above technical scheme：
The inventive method initially sets up the dynamic characteristic and sensor measurement equation of process, obtains systematic parameter；Then count Critical packet receiving probability is calculated, i.e., error can dissipate with elapsing constantly in the case that packet receiving rate is smaller than critical value；Then distinguish Iteratively solve the predicted value of sensor and predicting covariance matrix at single channel and multichannel center；Finally according to calculating Obtained estimate and evaluated error, multichannel switching transmission is determined better than single channel transmission and provides switching method, is completed The optimization of multisensor estimation performance in wireless network control system.The present invention utilizes Kalman filtering algorithm design wireless network Multisensor estimates performance methodology in control system, can ensure in multichannel transmission systems efficient scheduling sensor to drop Low evaluated error.
Brief description of the drawings
Fig. 1 is that model structure is illustrated in multisensor estimation performance methodology in a kind of wireless network control system of the present invention Figure；
Fig. 2 is that workflow is illustrated in multisensor estimation performance methodology in a kind of wireless network control system of the present invention Figure；
Fig. 3 is that multisensor estimates single-channel actual value in performance methodology in a kind of wireless network control system of the present invention The figure compared with estimate；
Fig. 4 is that multisensor estimates single channel sensor 1 in performance methodology in a kind of wireless network control system of the present invention The figure compared with the evaluated error of sensor 2；
Fig. 5 is to estimate in a kind of wireless network control system of the present invention in multisensor estimation performance methodology in performance methodology The averaged power spectrum Error Graph of single channel sensor 2；
Fig. 6 is that multisensor estimates two-channel actual value in performance methodology in a kind of wireless network control system of the present invention The figure compared with estimate；
Fig. 7 is that multisensor estimates double-channel sensor 1 in performance methodology in a kind of wireless network control system of the present invention The figure compared with the evaluated error of sensor 2；
Fig. 8 is that multisensor estimates double-channel sensor 2 in performance methodology in a kind of wireless network control system of the present invention Averaged power spectrum Error Graph.
Embodiment
The present invention is described in further detail with embodiment below in conjunction with the accompanying drawings.
Embodiment 1
As shown in Figures 1 to 8, multisensor estimates performance methodology, including following step in a kind of wireless network control system Suddenly：
(1) wireless network control system sensor parameters are obtained, establish the dynamic characteristic and sensor measurement equation of process：
Wherein, A ∈ Rn*nRepresent sytem matrix, C ∈ Rm*nRepresent the observing matrix of row full rank, xk∈RnAnd yk∈RmRespectively Represent state and the measurement of sensor, wk∈RnAnd υk∈RmIt is the Gaussian process of zero-mean, Q is wkCovariance matrix, R is vk Covariance matrix, meet Q ＞ 0 and R ＞ 0；The original state x of system0It is that average is 0 and covariance matrix is P0＞ 0 Gauss Random vector, wk, vkAnd x0It is separate；
(2) multiple sensors are connected, two channels are set between each two sensor, while set the ginseng of every channel Number；Channel using wherein one connection carries out data transmission, if this channel starts packet loss, channel is switched over； Cycle-index is set, initializes x0, P0, using Kalman filtering algorithm, sensor at single channel and multichannel center is calculated respectively Predicted value and predicting covariance matrix, accounting equation is：
WhereinCentered on locate sensor predicted value, Pk|kFor predicting covariance matrix, P is calculatedk|kPole Limit value, until circulation is completed；
(3) to, by two transmission performance comparisions, completing to circulate to channel according to step (2) between two sensors In predicted value and predicting covariance matrix data situation data cases, generate multiple channels comparison draw.
For the multisensor in single channel wireless networks control system, due to fading channel or congestion, packet is being believed Loss is there may be in the communication process in road.Similar, γkRepresent whether estimator receives and measure yk, estimator it is also known that γkInformation, do not consider delay etc. other uncertain factors, according to the optimality of Kalman filter, obtain：
Wherein, kalman gain Kk=Pk|k-1C′(CPk|k-1C′+R)-1, also, the renewal of Kalman filtering in time And it is optimal, i.e.,Pk+1|k=APk|kA '+Q andP0|-1=P0。
Due to the random data packet loss of network, the covariance matrix for predicting error is also random, and this is with standard Kalman Wave filter is different, its being to determine property of predicting covariance matrix.Make Pk=Pk|k-1, then PkRenewal equation be：
Pk+1=APkA′+Q-γkAPkC′(CPkC′+R)-1CPkA′
A value is now exported from system, sensor 2 is reached after reaching sensor 1, wherein having packet loss and time delay In the presence of, it is assumed that from the 9th moment, the 9th moment sensor 1 receives signal and is successfully transferred to the 2, the 10th moment unsuccessful transmission, 11 moment are unsuccessful, the success of 12 moment, can obtain following relation, i.e.
It can be obtained from figure, between sensor 1,2 during success, the value that sensor 2 obtains multiplies coefficient of combination A for 1 output valve, into Work(probability isIf it fails, then calculated by 2 acquired value of last moment sensor.It can obtain, in list The predicting covariance of multisensor will be increased very in maximum probability with exponential type in channel radio network control system.
Fig. 1 represents the illustraton of model of double-channel wireless network control system.
Fig. 2 gives the flow chart of multisensor estimation performance methodology in wireless network control system.Specifically, can retouch State as follows：
Multisensor estimates performance methodology in a kind of wireless network control system, and this method comprises the following steps：
Step 1：Establish the dynamic characteristic and sensor measurement equation of process：
Wherein, A ∈ Rn*nRepresent sytem matrix, C ∈ Rm*nRepresent observing matrix, xk∈RnAnd yk∈RmSystem is represented respectively State and measurement, wk∈RnAnd vk∈RmIt is the Gaussian process of zero-mean, its covariance matrix is respectively Q ＞ 0 and R ＞ 0, AskControllable, (A, C) is considerable, and measurement matrix C is row full rank, i.e. rank (C)=m≤n, the original state of system x0It is that average is 0 and covariance matrix is P0＞ 0 Gaussian random vector, further, wk, vkAnd x0It is separate.
Step 2：The measurement y of systemkThrough insecure traffic channel to long-range estimator, due to fading channel or gather around Plug, packet there may be Loss in the communication process of channel.Intuitively, drop probabilities are bigger, and information loss is tighter Weigh, then predicting covariance matrix Pk|k-1The possibility of diverging is bigger.For independent identically distributed packet loss process, it is existing As long as a critical packet receiving probability critical causes packet receiving probability to be more than the value, evaluated error covariance matrix can reach flat Equal boundedness.
First obtain the characteristic value of matrix A：
Eigvalues=eig (A)
Then the spectral radius of matrix A is calculated again：
SpectrumofA=max (abs (eigvalues))
Finally obtain critical packet receiving probability：
Step 3：Using Kalman filtering algorithm, calculate respectively at single channel and multichannel center the predicted value of sensor with Predicting covariance matrix：
Standard Kalman filtering equations：
It can be obtained by above formula：
P can so be drawnk|kThe limit, it meets equation X=g (h (X)).For single channel wireless networks control system In multisensor, due to fading channel or congestion, packet there may be Loss in the communication process of channel, can be with Obtain, the predicting covariance of multisensor will be increased very in maximum probability with exponential type in single channel wireless networks control system It is long.
Step 4：Relatively pass through two transmission performance comparisions between two sensors now：
Assuming that channel 1：
Assuming that channel 2：
Why the transition probability i.e. T of two channel is defined1(1,1) ＞ 0.5, T1(2,2) ＞ 0.5, T2(1,1) ＞ 0.5, T2(2,2) ＞ 0.5 is this channel in general existing note because such channel is called Gilbert-Elliott channels The transition probability that the property recalled is depended between state.In the Markov proposition some factor transfer processes of one system, n-th result It is solely dependent upon the influence of the result of the N-1 times, that is, only and current state correlation is unrelated with state before, therefore work as T1(1, 1) ＞ 0.5, T1When (2,2) ＞ 0.5, the packet receiving rate of two elements of transfer matrix at next moment is still higher to be avoided with this It is more prone to error.
By taking two channels as an example, it is assumed that channel 1 is first passed through between sensor and is transmitted, channel is unsuccessfully converted to after successful n1 times 2 transmission, then channel 1 is unsuccessfully gained after successful n2 time, why use as scheduling mode be because in first channel biography System starts to produce error after inputing mistake by mistake, if continuing, by the transmission, then its packet loss is even larger than packet receiving rate, easily makes Error amount starts to increase with exponential type, has at this time changed a channel system and has restarted the finger that transmission then avoids this error Number type increases.Similar, it can be generalized in multichannel system.
Step 5：Determine that multichannel switching transmission is better than single channel transmission and provides channel to cut according to estimate is calculated Method is changed, completes the optimization of multisensor estimation performance in wireless network control system.
Further, channel switching method resulting in the step 4 can ensure have in multichannel transmission systems The scheduling sensor of effect is to reduce evaluated error.
Technical scheme is further elaborated below by instantiation.In experiment, using single channel and The contrast of double-channel wireless network control system carries out proof of algorithm.Specifically, using following experiment parameter：
Sytem matrix is assumed respectivelyObserving matrix C=[3 2], system covariance matrixCovariance matrix R=0.6 is observed, it is 1000 to set cycle-index, takes the random number between 0~1 As estimate xk|k, evaluated error covariance matrix Pk|kWith actual value xkIt is initial, define initial observation value yk=0, actual value xkSecond is classified as [1 0.4], sets channel parameter
Kalman filtering is carried out to single channel and double-channel wireless network control system, for exporting one in single-channel system Individual value, sensor 2 is reached after reaching sensor 1, wherein have the presence of packet loss and time delay, between sensor 1,2 during success, The value that sensor 2 obtains multiplies coefficient of combination A for 1 output valve, and the probability of success isIf it fails, then by last moment 2 acquired value of sensor is calculated.It can obtain, the prediction mistake of multisensor in single channel wireless networks control system Poor covariance will be increased with exponential type；For dual channel system, it is assumed that channel 1 is first passed through between sensor and is transmitted, success n1 times Unsuccessfully be converted to channel 2 afterwards to transmit, then channel 1 unsuccessfully gained after successful n2 times, why use as scheduling mode be because For after first channel transmission errors system start produce error, if continue by the transmission then it packet loss it is bigger In packet receiving rate, easily make error amount start to increase with exponential type, at this time changed a channel system restart transmission then keep away The exponential type for having exempted from this error increases.
Fig. 3,4,5,6,7,8 are the simulation results figure to designed method by Matlab systems.
Fig. 3 gives in single-channel system actual value, the ratio of estimate at sensor 1 at estimate and sensor 2 Compared with graph of a relation, Fig. 4 gives the evaluated error in single-channel system at sensor 1 and the evaluated error at sensor 2 compares Graph of a relation, Fig. 5 give the averaged power spectrum error at sensor 2 in single-channel system.As can be seen from the figure at sensor 2 Evaluated error and average evaluated error it is relatively large.
Fig. 6 gives in dual channel system actual value, the ratio of estimate at sensor 1 at estimate and sensor 2 Compared with graph of a relation, Fig. 7 gives the evaluated error in dual channel system at sensor 1 and the evaluated error at sensor 2 compares Graph of a relation, Fig. 8 give the averaged power spectrum error at sensor 2 in dual channel system.As can be seen from the figure at sensor 2 Evaluated error and average evaluated error it is relatively small.
The inventive method initially sets up the dynamic characteristic and sensor measurement equation of process, obtains systematic parameter；Then count Critical packet receiving probability is calculated, i.e., error can dissipate with elapsing constantly in the case that packet receiving rate is smaller than critical value；Then distinguish Iteratively solve the predicted value of sensor and predicting covariance matrix at single channel and multichannel center；Finally according to calculating Obtained estimate and evaluated error, multichannel switching transmission is determined better than single channel transmission and provides switching method, is completed The optimization of multisensor estimation performance in wireless network control system.The present invention utilizes Kalman filtering algorithm design wireless network Multisensor estimates performance methodology in control system, can ensure in multichannel transmission systems efficient scheduling sensor to drop Low evaluated error.
In a word, presently preferred embodiments of the present invention, all equalizations made according to scope of the present invention patent be the foregoing is only Change and modification, it should all belong to the covering scope of patent of the present invention.

Claims (2)

1. multisensor estimates performance methodology in a kind of wireless network control system, it is characterised in that comprises the following steps：
(1) wireless network control system sensor parameters are obtained, establish the dynamic characteristic and sensor measurement equation of process：
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>x</mi> <mrow> <mi>k</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <msub> <mi>Ax</mi> <mi>k</mi> </msub> <mo>+</mo> <msub> <mi>w</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>y</mi> <mi>k</mi> </msub> <mo>=</mo> <msub> <mi>Cx</mi> <mi>k</mi> </msub> <mo>+</mo> <msub> <mi>v</mi> <mi>k</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow>
Wherein, A ∈ Rn*nRepresent sytem matrix, C ∈ Rm*nRepresent the observing matrix of row full rank, xk∈RnAnd yk∈RmRepresent to pass respectively The state of sensor and measurement, wk∈RnAnd vk∈RmIt is the Gaussian process of zero-mean, Q is wkCovariance matrix, R is vkAssociation side Poor matrix, meet Q ＞ 0 and R ＞ 0；
(2) multiple sensors are connected, two channels are set between each two sensor, while set the parameter of every channel； Channel using wherein one connection carries out data transmission, if this channel starts packet loss, channel is switched over；Set Cycle-index, initialize x0, P0, using Kalman filtering algorithm, the pre- of sensor at single channel and multichannel center is calculated respectively Measured value and predicting covariance matrix, accounting equation are：
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WhereinCentered on locate sensor predicted value, Pk|kFor predicting covariance matrix, P is calculatedk|kThe limit Value, until circulation is completed；
(3) to, by two transmission performance comparisions, being completed between two sensors according to step (2) to channel in circulation Predicted value and predicting covariance matrix data situation data cases, the comparison for generating multiple channels are drawn.
2. multisensor estimates performance methodology in a kind of wireless network control system according to claim 1, its feature exists In：In step (1), the original state x of system0It is that average is 0 and covariance matrix is P0＞ 0 Gaussian random vector, wk, vk And x0It is separate.
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CN110366232A (en) * 2019-06-19 2019-10-22 东南大学 Sensor transmissions energy control method for remote status estimation
CN110366232B (en) * 2019-06-19 2022-02-11 东南大学 Sensor transmission energy control method for remote state estimation
CN115276917A (en) * 2022-07-29 2022-11-01 东北大学 Remote state estimation transmission control method using historical information
CN115276917B (en) * 2022-07-29 2023-06-20 东北大学 Remote state estimation transmission control method utilizing historical information

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