CN105071892A - Method and system for time synchronization calibration of wireless sensor network - Google Patents

Abstract

The invention provides a method for time synchronization calibration of a wireless sensor network, wherein the wireless sensor network comprises a plurality of wireless sensor nodes. The method comprises the following steps of: creating a clock model of the wireless sensor nodes to determine clock skew in a local clock of the wireless sensor nodes; estimating optimal state of a standard clock of the wireless sensor nodes by a predetermined filtering mode, and calibrating the clock skew in the local clock; executing linear quadratic optimal control to realize synchronization of the local clock and the standard clock of the wireless sensor nodes. According to the invention, firstly, standard time information received by the nodes is subjected to noise filtering by Kalman filtering, precision of the standard block is improved, and consequently, precision of local time synchronization is improved effectively; then, by a linear quadratic tracking method of optimum control, under premise of ensuring system stability, error is kept around zero with the cost of lower control energy, and the aim of time synchronization is achieved.

Description

A kind of time synchronized calibration steps of radio sensing network and system

Technical field

The invention belongs to technology of wireless sensing network field, relate to a kind of calibration steps and system, particularly relate to a kind of time synchronized calibration steps and system of radio sensing network.

Background technology

ZigBee-network receives the concern of height with its higher monitoring accuracy, lower power consumption and cost, low complex degree and feature flexibly of disposing, and has application widely in a lot of fields in such as Smart Home, Industry Control, military surveillance, intelligent transportation, environmental monitoring.But because its monitoring environment is general comparatively complicated and unstable, node is generally with powered battery, make energy supply more difficult, the continuous working period of node becomes the key issue of restriction network, how to save the key that energy consumption becomes design radio sensing network.IEEE1588 is a kind of accurate clock synchronous protocol, is applicable to the demand of industrial network communication system, can reach the synchronization accuracy of less than 1 millisecond.Why this agreement can reach higher precision, and a key factor is that it have employed high-precision timestamp.Due to Conventional temporal synchronous method timestamp record be the time that packet enters protocol stack, the queuing time of packet in protocol stack and processing time are not taken into account, therefore timestamp and packet actual sends the time comparatively big error, causes the precise decreasing that it is synchronous.

The acquisition position of timestamp is moved down into the protocol stack bottom by PTP, sync message and Time delay measurement message is sent at twice, and the correct time making clock to be synchronized can obtain master clock to send datagram, improves synchronization accuracy.Use PTP Time synchronization algorithm to correct local clock time, due to the existence of standard time clock observation error, add the deviation of local clock frequency, synchronized result convergence is bad, and local clock can not follow the tracks of standard time clock preferably.

Therefore, how a kind of time synchronized calibration steps and system of radio sensing network are provided, local clock time is corrected to solve use PTP Time synchronization algorithm of the prior art, due to the existence of standard time clock observation error, add the deviation of local clock frequency, synchronized result convergence is bad, and local clock can not follow the tracks of the many disadvantages such as standard time clock preferably, has become the technical problem that practitioner in the art is urgently to be resolved hurrily.

Summary of the invention

The shortcoming of prior art in view of the above, the object of the present invention is to provide a kind of time synchronized calibration steps and system of radio sensing network, local clock time is corrected with PTP Time synchronization algorithm for solving in prior art, due to the existence of standard time clock observation error, add the deviation of local clock frequency, synchronized result convergence is bad, and local clock can not follow the tracks of the problem of standard time clock preferably.

For achieving the above object and other relevant objects, one aspect of the present invention provides a kind of time synchronized calibration steps of radio sensing network, described radio sensing network comprises some wireless sensing nodes, and the time synchronized calibration steps of described radio sensing network comprises the following steps: the clock models creating described wireless sensing node is with clock jitter in the local clock determining described wireless sensing node; Utilize predetermined filtering mode to estimate described wireless sensing node standard time clock optimum state, and correct clock jitter in local clock; Perform Linear quadratic gaussian control and realize the local clock of described wireless sensing node and the synchronous of standard time clock.

Alternatively, in described local clock, clock jitter comprises: with the phase deviation of described standard time clock, with the frequency departure of described standard time clock, and/or random noise.

Alternatively, the clock models of described wireless sensing node is expressed as: C (t)=φ+ω t+ ε, wherein, φ represents the phase deviation with described standard time clock, and ω represents the frequency departure with described standard time clock, and ε represents random noise.

Alternatively, described predetermined filtering mode refers to take Minimum Mean Square Error as criterion, adopts the state space equation of signal and noise, utilizes the upper discreet value of a moment k-1 and the measured value of current time k to calculate the discreet value of current time k.

Alternatively, the step utilizing predetermined filtering mode to estimate described wireless sensing node standard time clock optimum state is completed by following five formula that prestore; Wherein, five formula that prestore are respectively first and prestore formula, and second prestores formula, and the 3rd prestores formula, and the 4th prestores formula, and the 5th prestores formula; First prestores formula: X (k|k-1)=AX (k-1|k-1)+BU (k); Second prestores formula: P (k|k-1)=AP (k-1|k-1) A '+Q; 3rd prestores formula: X (k|k)=X (k|k-1) Kg (k) (Z (k)-HX (k|k-1)); 4th prestores formula: Kg (k)=P (k|k-1) H '/(HP (k|k-1) H '+R); 5th prestores formula: P (k|k)=(1-Kg (k)) P (k|k-1); Wherein, described first formula and second formula that prestores that prestores is prior estimate to current time k; Described first X (k|k-1) in formula that prestores is prediction current time state, and X (k-1|k-1) is the optimal result of a upper moment k-1; Described second P (k|k-1) and P (k|k) in formula that prestores is the covariance that X (k|k-1) and X (k|k) is corresponding respectively, and Q is sensing node process covariance; Described 3rd formula, the 4th formula and the 5th that prestores that prestores prestores formula to calculating optimization discreet value X (k|k) to complete correction, K _gk () is kalman gain, H is a constant, and H '=1/H, R is another constant, and A and B is conventional matrix equivalent each other, and U (k) is optimal state feed-back control device.。

Alternatively, the step of described execution Linear quadratic gaussian control comprises and tries to achieve quadratic model object function by Linear quadratic gaussian control tracking and make quadratic performance index minimum.

Another invention of the present invention also provides a kind of time synchronized calibration system of radio sensing network, described radio sensing network comprises some wireless sensing nodes, the time synchronized calibration system of described radio sensing network comprises: clock models creation module, for the clock models that creates described wireless sensing node with clock jitter in the local clock determining described wireless sensing node; Processing module, is connected with described clock models creation module, for utilizing predetermined filtering mode to estimate described wireless sensing node standard time clock optimum state, and corrects clock jitter in local clock; Control module, is connected with described clock models creation module and processing module, realizes the local clock of described wireless sensing node and the synchronous of standard time clock for performing Linear quadratic gaussian control.

Alternatively, the clock models of the wireless sensing node that described clock models creation module creates is expressed as: C (t)=φ+ω t+ ε wherein, φ represents the phase deviation with described standard time clock, and ω represents the frequency departure with described standard time clock, and ε represents random noise; In described local clock, clock jitter comprises: with the phase deviation of described standard time clock, with the frequency departure of described standard time clock, and/or random noise.

Alternatively, it is criterion that the described predetermined filtering mode be pre-stored in described processing module refers to Minimum Mean Square Error, adopt the state space equation of signal and noise, utilize the upper discreet value of a moment k-1 and the measured value of current time k to calculate the discreet value of current time k.

Alternatively, described processing module comprises the computing unit being respectively used to calculate five formula that prestore prestored; Wherein, first computing unit to prestore formula for calculating first, second computing unit to prestore formula for calculating second, 3rd computing unit to prestore formula for calculating the 3rd, 4th computing unit to prestore formula for calculating the 4th, and the 5th computing unit to prestore formula for calculating the 5th, first prestores formula: X (k|k-1)=AX (k-1|k-1)+BU (k); Second prestores formula: P (k|k-1)=AP (k-1|k-1) A '+Q; 3rd prestores formula: X (k|k)=X (k|k-1) Kg (k) (Z (k)-HX (k|k-1)); 4th prestores formula: Kg (k)=P (k|k-1) H '/(HP (k|k-1) H '+R); 5th prestores formula: P (k|k)=(1-Kg (k)) P (k|k-1) wherein, and described first formula and second formula that prestores that prestores is prior estimate to current time k; Described first X (k|k-1) in formula that prestores is prediction current time state, and X (k-1|k-1) is the optimal result of a upper moment k-1; Described second P (k|k-1) and P (k|k) in formula that prestores is the covariance that X (k|k-1) and X (k|k) is corresponding respectively, and Q is sensing node process covariance; Described 3rd formula, the 4th formula and the 5th that prestores that prestores prestores formula to calculating optimization discreet value X (k|k) to complete correction, K _gk () is kalman gain, H is a constant, and H '=1/H, R is another constant, and A and B is conventional matrix equivalent each other, and U (k) is optimal state feed-back control device.

Alternatively, described control module also makes quadratic performance index minimum for trying to achieve quadratic model object function by Linear quadratic gaussian control tracking.

Another aspect of the invention also provides a kind of wireless sensing node, is connected with a routing device, stores standard time clock in described routing device, and described wireless sensing node comprises: the time synchronized calibration system of described radio sensing network.

Alternatively, the state of described wireless sensing node is divided into resting state and wake-up states, described wireless sensing node by the conversion of resting state described in a Timer Controlling and wake-up states to coordinate the synchronous of described wireless sensing node and described routing device clock

As mentioned above, the time synchronized calibration steps of radio sensing network of the present invention and system, have following beneficial effect:

Can realizing synchronously by sensor node of the present invention without the need to any transmission of messages, by reducing the temporal information quantity in synchronizing process, significantly can reduce the energy loss of system.First application card Kalman Filtering carries out noise filtering to the standard time information that node receives, by improving standard time clock precision, thus the precision that effective raising local zone time is synchronous.Then apply the Linear-Quadratic Problem tracking of optimal control, under the prerequisite ensureing system stability, with less control energy for cost, make error remain near zero, reach the object of time synchronized.

Accompanying drawing explanation

Fig. 1 is shown as the time synchronized calibration steps schematic flow sheet of radio sensing network of the present invention.

Fig. 2 is shown as the theory structure schematic diagram of the time synchronized calibration system of radio sensing network of the present invention.

Fig. 3 is shown as the theory structure schematic diagram of processing module in the time synchronized calibration system of radio sensing network of the present invention.

Fig. 4 is shown as the theory structure schematic diagram of wireless sensing node of the present invention.

Element numbers explanation

The time synchronized calibration system of 1 radio sensing network

11 clock module creation modules

12 processing modules

13 control modules

121 first computing units

122 second computing units

123 the 3rd computing units

124 the 4th computing units

125 the 5th computing units

2 wireless sensing nodes

The time synchronized calibration system of 21 radio sensing networks

S1 ~ Sn step

Embodiment

Below by way of specific instantiation, embodiments of the present invention are described, those skilled in the art the content disclosed by this specification can understand other advantages of the present invention and effect easily.The present invention can also be implemented or be applied by embodiments different in addition, and the every details in this specification also can based on different viewpoints and application, carries out various modification or change not deviating under spirit of the present invention.It should be noted that, when not conflicting, the feature in following examples and embodiment can combine mutually.

It should be noted that, the diagram provided in following examples only illustrates basic conception of the present invention in a schematic way, then only the assembly relevant with the present invention is shown in graphic but not component count, shape and size when implementing according to reality is drawn, it is actual when implementing, and the kenel of each assembly, quantity and ratio can be a kind of change arbitrarily, and its assembly layout kenel also may be more complicated.

Embodiment one

The present embodiment provides a kind of time synchronized calibration steps of radio sensing network, and described radio sensing network comprises some wireless sensing nodes.Refer to Fig. 1, be shown as the time synchronized calibration steps schematic flow sheet of radio sensing network.As shown in Figure 1, the time synchronized calibration steps of described radio sensing network comprises following step:

S1, the clock models creating described wireless sensing node is with clock jitter in the local clock determining described wireless sensing node.In described radio sensing network, each wireless sensing node has the local clock of oneself, and the precision of local clock is decided by local crystal oscillator frequency and clock threshold value.The clock models of wireless sensing node ideally can be expressed as:

C (t)=t formula (1)

Wherein, t represents the time.

But due to the defect of node crystal oscillator, actual clock models is:

C (t)=φ+ω t+ ε formula (2)

Wherein, φ represents the phase deviation with described standard time clock, and ω represents the frequency departure with described standard time clock, and ε represents random noise.Clock synchronous is exactly comprise the phase deviation with described standard time clock by eliminating, with the frequency departure of described standard time clock, and/or clock jitter in the local clock of random noise.

S2, utilizes predetermined filtering mode to estimate described wireless sensing node standard time clock optimum state, and corrects clock jitter in local clock.The principal element affecting clock synchronization accuracy in reality is the transmission delay of message, is carried out the correction of clock by migration and drift compensation.In this step, described predetermined filtering mode is Kalman filtering, described Kalman filtering refers to take Minimum Mean Square Error as criterion, adopts the state space equation of signal and noise, utilizes the upper discreet value of a moment k-1 and the measured value of current time k to calculate the discreet value of current time k.(comprise standard time clock state equation according to clock status equation and observational equation and there is local clock state equation during described Kalman filtering, standard time clock observational equation and local clock observational equation) to needing the signal estimated to do the estimation meeting Minimum Mean Square Error, this filtering is the process of " estimating-calculate-estimation ".Need the Mathematical Modeling setting up sensing node before filtering, suppose that the cycle of the P sensing node broadcast in several sensing nodes is T.Standard time clock state equation and observational equation are as shown in formula (3) and formula (4):

X (k)=AX (k-1)+BU (k)+W (k) formula (3)

Y (k)=CX (k) formula (4)

X (k) is the state variable of standard time clock, and Y (k) is the observational variable of standard time clock, and W (k) is the white noise of normal distribution.In this step, estimate during Kalman filtering and the process corrected, mainly completed by following five formula that prestore; Wherein, five formula that prestore are respectively first and prestore formula, and second prestores formula, and the 3rd prestores formula, and the 4th prestores formula, and the 5th prestores formula;

First prestores formula:

X (k|k-1)=AX (k-1|k-1)+BU (k) formula (5)

Second prestores formula:

P (k|k-1)=AP (k-1|k-1) A '+Q formula (6)

3rd prestores formula:

X (k|k)=X (k|k-1) Kg (k) (Z (k)-HX (k|k-1)) formula (7)

4th prestores formula:

Kg (k)=P (k|k-1) H '/(HP (k|k-1) H '+R) formula (8)

5th prestores formula:

P (k|k)=(1-Kg (k)) P (k|k-1) formula (9)

Wherein, described first formula and second formula that prestores that prestores is prior estimate to current time k; Described first X (k|k-1) in formula that prestores is prediction current time state, and X (k-1|k-1) is the optimal result of a upper moment k-1; Described second P (k|k-1) and P (k|k) in formula that prestores is the covariance that X (k|k-1) and X (k|k) is corresponding respectively, and Q is sensing node process covariance; Described 3rd formula, the 4th formula and the 5th that prestores that prestores prestores formula to calculating optimization discreet value X (k|k) to complete correction, Kg (k) is kalman gain, H is a constant, H '=1/H, R is another constant, A and B is conventional matrix equivalent each other, and U (k) is optimal state feed-back control device.

Wherein, $X\left(k\right)=\left[\begin{array}{c}\mathrm{\θ}\left(k\right)\\ \mathrm{\α}\left(k\right)\end{array}\right],$ θ (k) represents the clock of current time k, and α (k) represents the oscillation frequency clock of current time; A, B are conventional matrix, $A=\left[\begin{array}{cc}1& T\\ 0& 1\end{array}\right];$ When not having controlled quentity controlled variable, U (k) is 0. $Q=\left[\begin{array}{cc}0.1& 0\\ 0& 0\end{array}\right],$ R=2, P (0|0) are initial covariance, $P\left(0\right|0)=\left[\begin{array}{cc}10& 0\\ 0& 10\end{array}\right],$ The initial condition of sensing node $X\left(0\right|0)=\left[\begin{array}{c}0\\ 1\end{array}\right].$

S3, performs Linear quadratic gaussian control and realizes the local clock of described wireless sensing node and the synchronous of standard time clock.In the present embodiment, optimal solution can be write as unified analytical expression, obtains the feedback form that optimal control law is state variable.In this step, optimum U is sought according to tracking ^*k (), makes quadratic model object function to obtain minimum value.

Target function is as follows:

$J=\frac{1}{2}E\left(N\right){\mathrm{FE}}^T\left(N\right)+\frac{1}{2}\underset{k=0}{\overset{N-1}{\Σ}}\left(E\right(N\left)Q\right(k\left)E^T\right(N)+U(N\left)R\right(k\left)U^T\right(N\left)\right)$ Formula (10)

Wherein, E (N)=Y _r(N)-Y (N), N are corrected time, and k is current time, Y _r(N) desired output vector when be corrected time being N, F, Q are positive semidefinite symmetrical matrix, and R is positive definite symmetric matrices.By principle of minimum, final control law is as follows:

P (k) A+A ^tp (k)-P (k) BR ^-1b ^tp (k)+C ^tqC=0 formula (11)

(P (k) BR ^-1b ^t-A ^t) ξ (k)-C ^tqYr (k)=0 formula (12)

Associating solution formula (11) and formula (12), obtaining optimal control solution is:

U ^*(k)=-R ^-1b ^t(P (k) X (k)-ξ (k)) formula (13)

State equation is set up as follows according to radio sensing network:

$\left[\begin{array}{c}\mathrm{\θ}\left(k\right)\\ \mathrm{\α}\left(k\right)\end{array}\right]=\left[\begin{array}{cc}1& T\\ 0& 1\end{array}\right]\left[\begin{array}{c}\mathrm{\θ}(k-1)\\ \mathrm{\α}(k-1)\end{array}\right]+\left[\begin{array}{cc}1& 0\\ 0& 1\end{array}\right]U\left(k\right)+W\left(k\right)$ Due to system

$Y\left(k\right)=\left[\begin{array}{cc}1& 0\end{array}\right]\left[\begin{array}{c}\mathrm{\θ}\left(k\right)\\ \mathrm{\α}\left(k\right)\end{array}\right]$ Formula (14)

{ A, C} are completely observable, then unique exist U ^*k () obtains optimal control solution.Linear quadratic gaussian control tracking is utilized to try to achieve U ^*k () makes quadratic performance index obtain minimum.Wherein P (k) is tried to achieve by the method for iteration, and general weighting matrices Q, R are set to respectively: $Q=\left[\begin{array}{cc}1& 10\\ 0& 1\end{array}\right],$ R＝1。

The time synchronized calibration steps of the radio sensing network that the present embodiment provides is a kind of synchronous method of only receiving terminal, sensor node wherein can realize synchronously without the need to any transmission of messages, by reducing the temporal information quantity in synchronizing process, the energy loss of system significantly can be reduced.First application card Kalman Filtering carries out noise filtering to the standard time information that node receives, by improving standard time clock precision, thus the precision that effective raising local zone time is synchronous.Then apply the Linear-Quadratic Problem tracking of optimal control, under the prerequisite ensureing system stability, with less control energy for cost, make error remain near zero, reach the object of time synchronized.

Embodiment two

The present embodiment provides a kind of time synchronized calibration system of radio sensing network, and described radio sensing network comprises some wireless sensing nodes.Refer to Fig. 2, be shown as the theory structure schematic diagram of the time synchronized calibration system of radio sensing network.As shown in Figure 2, the time synchronized calibration system of described radio sensing network comprises: clock models creation module 11, processing module 12 and control module 13.

Described clock models creation module 11 for the clock models that creates described wireless sensing node with clock jitter in the local clock determining described wireless sensing node.In described radio sensing network, each wireless sensing node has the local clock of oneself, and the precision of local clock is decided by local crystal oscillator frequency and clock threshold value.The clock models of wireless sensing node ideally can be expressed as:

C (t)=t formula (1)

Wherein, t represents the time.

But due to the defect of node crystal oscillator, actual clock models is:

C (t)=φ+ω t+ ε formula (2)

Wherein, φ represents the phase deviation with described standard time clock, and ω represents the frequency departure with described standard time clock, and ε represents random noise.Clock synchronous is exactly comprise the phase deviation with described standard time clock by eliminating, with the frequency departure of described standard time clock, and/or clock jitter in the local clock of random noise.

The processing module 12 be connected with described clock module creation module 11 estimates described wireless sensing node standard time clock optimum state for utilizing predetermined filtering mode, and corrects clock jitter in local clock.The principal element affecting clock synchronization accuracy in reality is the transmission delay of message, is carried out the correction of clock by migration and drift compensation.In the present embodiment, the described predetermined filtering mode be pre-stored in described processing module 12 is Kalman filtering, described Kalman filtering refers to take Minimum Mean Square Error as criterion, adopt the state space equation of signal and noise, utilize the upper discreet value of a moment k-1 and the measured value of current time k to calculate the discreet value of current time k.(comprise standard time clock state equation according to clock status equation and observational equation and there is local clock state equation during described Kalman filtering, standard time clock observational equation and local clock observational equation) to needing the signal estimated to do the estimation meeting Minimum Mean Square Error, this filtering is the process of " estimating-calculate-estimation ".Need the Mathematical Modeling setting up sensing node before filtering, suppose that the cycle of the P sensing node broadcast in several sensing nodes is T.Standard time clock state equation and observational equation are as shown in formula (3) and formula (4):

X (k)=AX (k-1)+BU (k)+W (k) formula (3)

Y (k)=CX (k) formula (4)

X (k) is the state variable of standard time clock, and Y (k) is the observational variable of standard time clock, and W (k) is the white noise of normal distribution.In the present embodiment, estimate and the process corrected during Kalman filtering, described processing module comprises to be estimated respectively by the computing unit for calculating five formula that prestore prestored and corrects.Refer to Fig. 3, be shown as the theory structure schematic diagram of processing module.Described processing module 12 comprises the first computing unit 121, second computing unit 122, the 3rd computing unit 123, the 4th computing unit 124, the 5th computing unit 125.Wherein, first computing unit 121 to prestore formula for calculating first, second computing unit 122 to prestore formula for calculating second, 3rd computing unit 123 to prestore formula for calculating the 3rd, 4th computing unit 124 to prestore formula for calculating the 4th, and the 5th computing unit 125 to prestore formula for calculating the 5th; Wherein, five formula that prestore are respectively first and prestore formula, and second prestores formula, and the 3rd prestores formula, and the 4th prestores formula, and the 5th prestores formula;

First prestores formula:

X (k|k-1)=AX (k-1|k-1)+BU (k) formula (5)

Second prestores formula:

P (k|k-1)=AP (k-1|k-1) A '+Q formula (6)

3rd prestores formula:

X (k|k)=X (k|k-1) Kg (k) (Z (k)-HX (k|k-1)) formula (7)

4th prestores formula:

Kg (k)=P (k|k-1) H '/(HP (k|k-1) H '+R) formula (8)

5th prestores formula:

P (k|k)=(1-Kg (k)) P (k|k-1) formula (9)

Wherein, described first formula and second formula that prestores that prestores is prior estimate to current time k; Described first X (k|k-1) in formula that prestores is prediction current time state, and X (k-1|k-1) is the optimal result of a upper moment k-1; Described second P (k|k-1) and P (k|k) in formula that prestores is the covariance that X (k|k-1) and X (k|k) is corresponding respectively, and Q is sensing node process covariance; Described 3rd formula, the 4th formula and the 5th that prestores that prestores prestores formula to calculating optimization discreet value X (k|k) to complete correction, Kg (k) is kalman gain, H is a constant, H '=1/H, R is another constant, A and B is conventional matrix equivalent each other, and U (k) is optimal state feed-back control device.

Wherein, $X\left(k\right)=\left[\begin{array}{c}\mathrm{\θ}\left(k\right)\\ \mathrm{\α}\left(k\right)\end{array}\right],$ θ (k) represents the clock of current time k, and α (k) represents the oscillation frequency clock of current time; A and B is conventional matrix equivalent each other, $A=\left[\begin{array}{cc}1& T\\ 0& 1\end{array}\right];$ When not having controlled quentity controlled variable, U (k) is 0. $Q=\left[\begin{array}{cc}0.1& 0\\ 0& 0\end{array}\right],$ R=2, P (0|0) are initial covariance, $P\left(0\right|0)=\left[\begin{array}{cc}10& 0\\ 0& 10\end{array}\right],$ The initial condition of sensing node $X\left(0\right|0)=\left[\begin{array}{c}0\\ 1\end{array}\right].$

The control module 13 connected with described clock module creation module 11 and processing module 12 realizes the local clock of described wireless sensing node and the synchronous of standard time clock for performing Linear quadratic gaussian control.In the present embodiment, optimal solution can be write as unified analytical expression, obtains the feedback form that optimal control law is state variable.In this step, optimum U is sought according to tracking ^*k (), makes quadratic model object function to obtain minimum value.

Target function is as follows:

$J=\frac{1}{2}E\left(N\right){\mathrm{FE}}^T\left(N\right)+\frac{1}{2}\underset{k=0}{\overset{N-1}{\Σ}}\left(E\right(N\left)Q\right(k\left)E^T\right(N)+U(N\left)R\right(k\left)U^T\right(N\left)\right)$ Formula (10)

Wherein, E (N)=Y _r(N)-Y (N), N are corrected time, and k is current time, Y _r(N) desired output vector when be corrected time being N, F, Q are positive semidefinite symmetrical matrix, and R is positive definite symmetric matrices.By principle of minimum, final control law is as follows:

P (k) A+A ^tp (k)-P (k) BR ^-1b ^tp (k)+C ^tqC=0 formula (11)

(P (k) BR ^-1b ^t-A ^t) ξ (k)-C ^tqYr (k)=0 formula (12)

Associating solution formula (11) and formula (12), obtaining optimal control solution is:

U ^*(k)=-R ^-1b ^t(P (k) X (k)-ξ (k)) formula (13)

State equation is set up as follows according to radio sensing network:

$\left[\begin{array}{c}\mathrm{\θ}\left(k\right)\\ \mathrm{\α}\left(k\right)\end{array}\right]=\left[\begin{array}{cc}1& T\\ 0& 1\end{array}\right]\left[\begin{array}{c}\mathrm{\θ}(k-1)\\ \mathrm{\α}(k-1)\end{array}\right]+\left[\begin{array}{cc}1& 0\\ 0& 1\end{array}\right]U\left(k\right)+W\left(k\right)$

$Y\left(k\right)=\left[\begin{array}{cc}1& 0\end{array}\right]\left[\begin{array}{c}\mathrm{\θ}\left(k\right)\\ \mathrm{\α}\left(k\right)\end{array}\right]$ Formula (14)

Due to system, { A, C} are completely observable, then unique exist U ^*k () obtains optimal control solution.Linear quadratic gaussian control tracking is utilized to try to achieve U ^*k () makes quadratic performance index obtain minimum.Wherein P (k) is tried to achieve by the method for iteration, and general weighting matrices Q, R are set to respectively: $Q=\left[\begin{array}{cc}1& 10\\ 0& 1\end{array}\right],$ R＝1。

The present embodiment also provides a kind of wireless sensing node 2, this wireless sensing node is connected with a routing device, store standard time clock in described routing device, described wireless sensing node 2 comprises: described in the present embodiment the time synchronized calibration system 21 of radio sensing network.In the present embodiment, described wireless sensing node 2 also comprises CPU part, reset circuit part, radio-frequency antenna part, power circuit part, part of data acquisition.The state of described wireless sensing node is divided into resting state and wake-up states, described wireless sensing node by the conversion of resting state described in a Timer Controlling and wake-up states to coordinate the synchronous of described wireless sensing node and described routing device clock.Energy-saving mode acquiescence opens (can close energy-saving mode in compile option), and under energy-saving mode, the state of terminal node is divided into two kinds: resting state and wake-up states, and two states alternately occurs.By the conversion of its dormancy of Timer Controlling and wake-up states.The operating system of described wireless sensing node 2 is based on built-in Linux operating system, software is communicated with ZigBee telegon by serial ports, communicated with host computer by Ethernet or WIFI (arranging in host computer), realize the transparent transmission of low-layer tester to host computer.The prerequisite of synchronous dormancy mechanism is all terminal nodes and telegon time synchronized in system, and all-network node uses unified wake-up period, and dormancy time wakes up to rear simultaneously.

The time synchronized calibration steps of radio sensing network provided by the invention and system are a kind of synchronous method and system of only receiving terminal, sensor node wherein can realize synchronously without the need to any transmission of messages, by reducing the temporal information quantity in synchronizing process, the energy loss of system significantly can be reduced.First application card Kalman Filtering carries out noise filtering to the standard time information that node receives, by improving standard time clock precision, thus the precision that effective raising local zone time is synchronous.Then apply the Linear-Quadratic Problem tracking of optimal control, under the prerequisite ensureing system stability, with less control energy for cost, make error remain near zero, reach the object of time synchronized.So the present invention effectively overcomes various shortcoming of the prior art and tool high industrial utilization.Above-described embodiment is illustrative principle of the present invention and effect thereof only, but not for limiting the present invention.Any person skilled in the art scholar all without prejudice under spirit of the present invention and category, can modify above-described embodiment or changes.Therefore, such as have in art usually know the knowledgeable do not depart from complete under disclosed spirit and technological thought all equivalence modify or change, must be contained by claim of the present invention.

Claims (13)

1. a time synchronized calibration steps for radio sensing network, described radio sensing network comprises some wireless sensing nodes, it is characterized in that, the time synchronized calibration steps of described radio sensing network comprises the following steps:

The clock models creating described wireless sensing node is with clock jitter in the local clock determining described wireless sensing node;

Utilize predetermined filtering mode to estimate described wireless sensing node standard time clock optimum state, and correct clock jitter in local clock;

Perform Linear quadratic gaussian control and realize the local clock of described wireless sensing node and the synchronous of standard time clock.

2. the time synchronized calibration steps of radio sensing network according to claim 1, is characterized in that: in described local clock, clock jitter comprises: with the phase deviation of described standard time clock, with the frequency departure of described standard time clock, and/or random noise.

3. the time synchronized calibration steps of radio sensing network according to claim 2, is characterized in that: the clock models of described wireless sensing node is expressed as:

C(t)＝φ+ωt+ε

Wherein, φ represents the phase deviation with described standard time clock, and ω represents the frequency departure with described standard time clock, and ε represents random noise.

4. the time synchronized calibration steps of radio sensing network according to claim 2, it is characterized in that: described predetermined filtering mode refers to take Minimum Mean Square Error as criterion, adopt the state space equation of signal and noise, utilize the upper discreet value of a moment k-1 and the measured value of current time k to calculate the discreet value of current time k.

5. the time synchronized calibration steps of radio sensing network according to claim 4, is characterized in that: the step utilizing predetermined filtering mode to estimate described wireless sensing node standard time clock optimum state is completed by following five formula that prestore; Wherein, five formula that prestore are respectively first and prestore formula, and second prestores formula, and the 3rd prestores formula, and the 4th prestores formula, and the 5th prestores formula;

First prestores formula:

X(k|k-1)＝AX(k-1|k-1)+BU(k)；

Second prestores formula:

P(k|k-1)＝AP(k-1|k-1)A'+Q；

3rd prestores formula:

X(k|k)＝X(k|k-1)Kg(k)(Z(k)-HX(k|k-1))；

4th prestores formula:

Kg(k)＝P(k|k-1)H'/(HP(k|k-1)H'+R)；

5th prestores formula:

P(k|k)＝(1-Kg(k))P(k|k-1)；

Wherein, described first formula and second formula that prestores that prestores is prior estimate to current time k; Described first X (k|k-1) in formula that prestores is prediction current time state, and X (k-1|k-1) is the optimal result of a upper moment k-1; Described second P (k|k-1) and P (k|k) in formula that prestores is the covariance that X (k|k-1) and X (k|k) is corresponding respectively, and Q is sensing node process covariance; Described 3rd formula, the 4th formula and the 5th that prestores that prestores prestores formula to calculating optimization discreet value X (k|k) to complete correction, K _gk () is kalman gain, H is a constant, and H'=1/H, R are another constant, and A and B is conventional matrix equivalent each other, and U (k) is optimal state feed-back control device.

6. the time synchronized calibration steps of radio sensing network according to claim 3, is characterized in that: the step of described execution Linear quadratic gaussian control comprises tries to achieve quadratic model object function by Linear quadratic gaussian control tracking and make quadratic performance index minimum.

7. a time synchronized calibration system for radio sensing network, described radio sensing network comprises some wireless sensing nodes, it is characterized in that, the time synchronized calibration system of described radio sensing network comprises:

Clock models creation module, for the clock models that creates described wireless sensing node with clock jitter in the local clock determining described wireless sensing node;

Processing module, is connected with described clock models creation module, for utilizing predetermined filtering mode to estimate described wireless sensing node standard time clock optimum state, and corrects clock jitter in local clock;

Control module, is connected with described clock models creation module and processing module, realizes the local clock of described wireless sensing node and the synchronous of standard time clock for performing Linear quadratic gaussian control.

8. the time synchronized calibration system of radio sensing network according to claim 7, is characterized in that: the clock models of the wireless sensing node that described clock models creation module creates is expressed as:

C(t)＝φ+ωt+ε

Wherein, φ represents the phase deviation with described standard time clock, and ω represents the frequency departure with described standard time clock, and ε represents random noise; In described local clock, clock jitter comprises: with the phase deviation of described standard time clock, with the frequency departure of described standard time clock, and/or random noise.

9. the time synchronized calibration system of radio sensing network according to claim 7, it is characterized in that: it is criterion that the described predetermined filtering mode be pre-stored in described processing module refers to Minimum Mean Square Error, adopt the state space equation of signal and noise, utilize the upper discreet value of a moment k-1 and the measured value of current time k to calculate the discreet value of current time k.

10. the time synchronized calibration system of radio sensing network according to claim 9, is characterized in that: described processing module comprises the computing unit being respectively used to calculate five formula that prestore prestored; Wherein, the first computing unit to prestore formula for calculating first, and the second computing unit to prestore formula for calculating second, 3rd computing unit to prestore formula for calculating the 3rd, 4th computing unit to prestore formula for calculating the 4th, and the 5th computing unit to prestore formula for calculating the 5th

First prestores formula:

X(k|k-1)＝AX(k-1|k-1)+BU(k)

Second prestores formula:

P(k|k-1)＝AP(k-1|k-1)A'+Q

3rd prestores formula:

X(k|k)＝X(k|k-1)Kg(k)(Z(k)-HX(k|k-1))

4th prestores formula:

Kg(k)＝P(k|k-1)H'/(HP(k|k-1)H'+R)

5th prestores formula:

P(k|k)＝(1-Kg(k))P(k|k-1)

Wherein, described first formula and second formula that prestores that prestores is prior estimate to current time k; Described first X (k|k-1) in formula that prestores is prediction current time state, and X (k-1|k-1) is the optimal result of a upper moment k-1; Described second P (k|k-1) and P (k|k) in formula that prestores is the covariance that X (k|k-1) and X (k|k) is corresponding respectively, and Q is sensing node process covariance; Described 3rd formula, the 4th formula and the 5th that prestores that prestores prestores formula to calculating optimization discreet value X (k|k) to complete correction, K _gk () is kalman gain, H is a constant, and H'=1/H, R are another constant, and A and B is conventional matrix equivalent each other, and U (k) is optimal state feed-back control device.

The time synchronized calibration system of 11. radio sensing networks according to claim 7, is characterized in that: described control module also makes quadratic performance index minimum for trying to achieve quadratic model object function by Linear quadratic gaussian control tracking.

12. 1 kinds of wireless sensing nodes, is characterized in that, are connected with a routing device, store standard time clock in described routing device, and described wireless sensing node comprises:

The time synchronized calibration system of the radio sensing network as described in as arbitrary in claim 7-11.

13. wireless sensing nodes according to claim 12, it is characterized in that: the state of described wireless sensing node is divided into resting state and wake-up states, described wireless sensing node by the conversion of resting state described in a Timer Controlling and wake-up states to coordinate the synchronous of described wireless sensing node and described routing device clock.