CN111193528B - Gaussian filtering method based on non-linear network system under non-ideal condition - Google Patents
Gaussian filtering method based on non-linear network system under non-ideal condition Download PDFInfo
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Abstract
The invention relates to a Gaussian filtering method of a nonlinear network system based on a Gaussian filtering method of the nonlinear network system under a non-ideal condition. The invention aims to solve the problems that the existing method does not consider the related noise, one-step random delay measurement and data packet loss which may occur in a nonlinear network system, and the problem that the estimation accuracy of a filter is reduced or even diverged due to model-based linear approximation or neglect of delay measurement. The Gaussian filtering method based on the nonlinear network system under the nonideal condition comprises the following steps: firstly, establishing a system model and a sensor measurement model; step two, providing hypothesis and lemma; thirdly, designing a Gaussian filter based on the second step; and step four, based on a third-order sphere diameter volume rule, approximating the Gaussian weighted integral in the step three to obtain a numerical form of the designed filter. The invention can be applied to the technical field of spacecraft and aircraft navigation.
Description
Technical Field
The invention relates to a Gaussian filtering method of a nonlinear network system, in particular to a state estimation method for a nonlinear system with correlated noise, one-step random delay measurement and data packet loss.
Background
In recent years, the estimation problem of network systems has attracted extensive attention[1-3]([1]L.Schenato,“Optimal estimation in networked control systems subject to random delay and packet drop,”IEEE transactions on automatic control,vol.53,no.5,pp.1311,2008.[2]W.A.Zhang,L.Yu,G.Feng,“Optimal linear estimation for networked systems with communication constraints,”Automatica,vol.47,no.9,pp.1992-2000,2011.[3]R.Caballero-Hermoso-Carazo, J.Linares-P rez, "Optimal state estimation for network Systems with random parameter matrices, corrected non-esses and delayed measurements," International Journal of General Systems, vol.44, No.2, pp.142-154, 2015.). Kalman Filter (KF)[4](R.E.Kalman,“A new approach to linear fiteration and prediction schemes, "Journal of basic Engineering, vol.82, No.1, pp.35-45, 1960") are effective methods for solving the state estimation problem. However, it is only suitable for ideal linear systems. In fact, non-linearity, correlated noise, random delay and packet loss are ubiquitous. In this context, the estimation problem of a non-linear network system with synchronous correlated noise and one-step random delay measurement and multi-packet loss is considered.
For non-linear systems, there are many estimation methods. For systems with weak non-linearity, Extended KF (EKF) based on first order Taylor series expansion can achieve acceptable accuracy[5](Y.Bar-Shalom,X.R.Li,T.Kirubarajan,“Estimation with applications to tracking and navigation:theory algorithms and software,”John Wiley&Sons, 2004.). Differential Filters (DDF) unlike EKF algorithms which require computation of the Jacobian matrix[6](M.N.k. poulsen, o.ravn, "New definitions in state estimation for nonlinear systems," Automatica, vol.36, No.11, pp.1627-1638, 2000.) the problem of the EKF algorithm possibly falling into local linearization is overcome by approximating the nonlinear function using stirling interpolation. Based on the fact that it is easier to approximate a probability distribution than to approximate any non-linear function or transformation, a series of sigma point filters, such as Particle Filters (PF) have been proposed[7](A.Doucet, S.Godsill, C.Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering," Statistics and computing, vol.10, No.3, pp.197-208, 2000.), Unscented KF (UKF)[8](S.J.Julie, J.K.Uhlmann, "unknown filtering and nonlinear evaluation," Proceedings of the IEEE, vol.92, No.3, pp.401-422, 2004.) and the volume KF (CKF)[9](I.Arasaratnam, S.Haykin, "Cubauture kalman filters," IEEE Transactions on automatic control, vol.54, No.6, pp.1254-1269, 2009.). Meanwhile, in order to adapt to different application scenarios, many improved versions of the above algorithm are proposed. E.g. based on second order Taylor expansionKF, unscented PF, DDF with second order stirling interpolation, adaptive UKF, high order CKF, square root CKF, etc. Because the UKF and the CKF have simpler forms and the numerical stability of the CKF algorithm is much higher than that of the UKF, the CKF algorithm is widely applied to the field of practical engineering, such as target tracking and mobile terminal positioning.
For systems with correlated noise, in[10](X.X.Wang, Y.Liang, Q.Pan, et al, "A Gaussian adaptive iterative filter for nonlinear systems with corrected noise," Automatica, vol.48, No.9, pp.2290-2297, September, 2012.), a new pseudo process equation was reconstructed in which the process noise is uncorrelated with the observation noise. In that[11](X.X.Wang, Y.Liang, Q.Pan, et al, "Design and implementation of Gaussian filter for nonlinear system with random delayed measurements and corrected reactions," Applied Mathematics and calculation, vol.232, pp.1011-1024, 2014.) A GASF of nonlinear system is proposed based on the projection theorem. Then, [11 ]]The method of (1) for designing a Gaussian filter for a nonlinear system with random delay measurements and associated noise[12](G.B. Chang, "Comments on" A Gaussian adaptive feedback filter for nonlinear systems with corrected noises, "Automatica, vol.50, No.2, pp.655-656, February, 2014.). In that[13](G.B.Chang,“Alternative formulation of the Kalman filter for correlated process and observation noise,”IET Science,Measurement&Technology, vol.8, No.5, pp.310-318, September, 2014.) [13]And [14]The two filtering algorithms in (a) have proven to be theoretically equivalent in a linear system. In that[14](H.Yu,X.J.Zhang,S.Wang,et al.,“Alternative framework of the Gaussian filter for non-linear systems with synchronously correlated noises,”IET Science,Measurement&Technology, vol.10, No.4, pp.306-315, July, 2016.) two alternative frameworks were developed based on state enhancement and conditional Gaussian distributions and demonstrated [14 []And [10-11]The equivalence of the algorithm (c). Then, at [15 ]](K.Zhao,S.M.Song,“Alternative framework of the Gaussian filter for non-linear systems with randomly delayed measurements and correlated noises,”IET Science,Measurement&Technology, vol.12, No.2, pp.161-168, 2017.) and [16](S.L.Sun, L.Xie, W.Xiao, et al, "Optimal linear estimation for systems with multiple packet routes," Automatica, vol.44, No.5, pp.1333-1342, May,2008.) the alternative framework of conditional Gaussian distributions is extended to nonlinear systems with correlated noise and one-step delay measurements.
For systems with one-step random delay measurement, the delay is typically described using a bernoulli-distributed random variable. For systems with multiple packet losses, zero input compensation, hold input compensation, and prediction compensation are separately proposed. For the problem of observed packets sent from the sensor to the filter, one or more packets arriving at the filter within one sampling period are discussed in [17] (S.L. Sun, "Optimal line filters for discrete-time Systems with random delay and lost measures with/without time stages," IEEE Transactions on Automatic Control, vol.58, No.6, pp.1551-1556, June,2013.), and [18] (S.L. Sun, G.H. Wang, "model and estimation for networked Systems with multiple transmission delays and packet locations)," Systems & controls, vol.73, pp.6-16, 20146, 2014. In [19] (S.L.Sun, J.Ma, "Linear estimation for network control systems with random transmission delays and packet routes," Information Sciences, vol.269, pp.349-365, June,2014.), a more general case is considered in which data packets are transmitted from the sensor and the controller to the filter and the actuator, respectively. In the above case, when packet loss occurs, the measurement is invalid. To avoid this, a hold-in compensation mechanism was developed. In [20] (y.liang, t.w.chen, q.pan, "Optimal linear state estimator with multiple packet drop," IEEE Transactions on Automatic Control, vol.55, No.6, pp.1428-1433, June,2010.), in the case where there is a plurality of packets lost, input compensation is kept for the best linear estimation problem. In [21] (j.ma, s.l.sun, "Information Fusion estimators for systems with multiple sensors of differential packet drop rates," Information Fusion, vol.12, No.3, pp.213-222, July,2011.), the same approach is considered in the centralized and distributed Fusion estimation problem in the sense of Linear Minimum Variance (LMV). In [22] (S.L.Sun, L.Xie, W.Xiao, et al, "Optimal linearity estimation for systems with multiple packet routes," Automatica, vol.44, No.5, pp.1333-1342, May,2008.), an Optimal linear estimator was developed based on a novel packet loss model. However, keeping the input compensation does not take into account the latest information of the system, and therefore a new compensation mechanism is proposed in which the latest observed predicted value is used as compensation. In [23] (J.Ding, S.L.Sun, J.Ma, N.Li, "Fusion optimization for Multi-Sensor network Systems with Packet Loss Compensation," Information Fusion, vol.45, pp.138-149, January,2019.), a centralized and distributed Fusion estimator was designed based on a new Compensation mechanism. In [24] (e.i. silver, m.a. solis, "alternative look at the constant-gain Kalman filter for state estimation over channels," IEEE Transactions on Automatic Control, vol.58, No.12, pp.3259-3265, Decumber, 2013.), a type of handover estimator is considered in which missing data is replaced by the best estimate.
It is worth mentioning that in [25-27] ([25] j.ma, s.l.sun, "Linear estimators for networked systems with one-step transmission delay and multiple packet drop based prediction compensation," IET Signal Processing, vol.11, No.2, pp.197-204, April,2017.[26] c.zhu, y.xia, l.xie, et al, "" optical line estimation for systems with transmission delay and packet, "IET Signal Processing, vol.7, No.9, pp.814-823, Decumber, 2013.[27] s.l.m. transmission delay and packet," audio transmission, pp.354, 349, latency loss, random loss, multiple data is taken into account. In [27], possible packet loss, delay measurement and compensation values are described by introducing two uncorrelated bernoulli random variables, which ensure that the system can receive one of the three quantities as a measurement value at any time in order to maintain the accuracy of the filtering algorithm. However, in [27], it does not consider the case where the delay measurement and the real-time measurement arrive in synchronization. In [26], the measurement model is changed because adjacent measurements may arrive at the same time, where the measurement received at the most recent time in the data processing center is used as a compensation value for the current epoch, and the superiority of the algorithm in [26] compared to [27] is shown in [26 ]. Further, in [25], a new measurement model is proposed by changing the compensation mechanism in which the measurements of the current epoch are used as a compensator instead of the one-step prediction of the latest measurements received by the data processing center. It has been demonstrated that the algorithm proposed in [25] has higher estimation accuracy and less computational burden than [26], since the model in [25] always uses the latest measurement information.
Because the existing method does not consider that the data processing center can receive two adjacent measurements simultaneously in the nonlinear system, information loss may occur when the actual system processes such problems, thereby affecting the estimation accuracy of the system, and even possibly causing filter divergence.
Disclosure of Invention
The invention aims to solve the problems that the existing method does not consider the related noise, one-step random delay measurement and data packet loss which may occur in a nonlinear network system, and the problem that the estimation accuracy of a filter is reduced or even diverged due to model linear approximation or neglect of delay measurement, and provides a Gaussian filtering method based on the nonlinear network system under the nonideal condition.
The Gaussian filtering method based on the non-linear network system under the non-ideal condition comprises the following specific processes:
firstly, establishing a system model and a sensor measurement model;
step two, providing hypothesis and lemma;
thirdly, designing a Gaussian filter based on the second step;
and step four, based on a third-order sphere diameter volume rule, approximating the Gaussian weighted integral in the step three to obtain a numerical form of the designed filter.
The invention has the beneficial effects that:
the invention provides a state estimation method of a nonlinear network system with synchronous correlated noise, one-step random delay and a plurality of data packet losses, which considers that the system is a general nonlinear system, designs a Gaussian recursive filtering algorithm aiming at the synchronous correlated noise, one-step random delay measurement and data packet losses which may occur in the system, can ensure that the system obtains a high-precision estimation value, avoids the divergence of the system and ensures the stability of the system.
Drawings
FIG. 1 is a diagram of the estimation of the state in the UNGM model by the algorithm of the present invention and the algorithm of document [23 ];
FIG. 2 is a diagram of the estimated root mean square error of the state in the UNGM model for the algorithm of the present invention and the algorithm in document [23 ];
FIG. 3 is a graph of the estimated error of the state in the UNGM model for the algorithm of the present invention and the algorithm of document [23 ];
FIG. 4 is a diagram of the RMS error estimation of the algorithm of the present invention and the algorithm of document [23] with respect to states in a strongly non-linear model;
FIG. 5 is a diagram of the estimation error of the algorithm of the present invention and the algorithm of document [23] with respect to a state in a strongly non-linear model.
Detailed Description
The first embodiment is as follows: the Gaussian filtering method based on the non-linear network system under the non-ideal condition in the embodiment comprises the following specific processes:
firstly, establishing a system model and a sensor measurement model;
step two, providing hypothesis and lemma;
thirdly, designing a Gaussian filter based on the second step;
and step four, based on a third-order sphere diameter volume rule, approximating the Gaussian weighted integral in the step three to obtain a numerical form of the designed filter.
The second embodiment is as follows: the first embodiment is different from the first embodiment in that a system model and a sensor measurement model are established in the first step; the specific process is as follows:
establishing a nonlinear discrete time system model with correlated noise:
xk+1=f(xk)+ωk (7)
establishing a general nonlinear measurement model:
zk=h(xk)+υk (8)
in the formula, xk+1System state at time k +1, xkSystem state at time k, xk,xk+1∈Rn,RnIs an n-dimensional real number space; z is a radical ofkIs the sensor model at time k, zk∈Rm,RmIs m-dimensional real number space; f (-) and h (-) are known non-linear functions; omegak∈RnAnd upsilonk∈RmIs correlated zero mean white Gaussian noise and has a covariance of
In the formula, deltaklIs the Kronecker delta function, QkAnd RkRespectively process noise and measurement noise covariance, SkIs the cross-covariance, l is the time l, ωl∈RnAnd upsilonl∈RmIs correlated zero mean gaussian white noise;
in the present invention, the following is considered: one step of random delay and packet loss may occur during data transmission; the data packet is sent only once; the estimator may receive a maximum of two measurement data simultaneously. That is, the estimator may receive zero, one or two packets. In practice, the measurement equation can be described by the following model. Considering communication bandwidth, delay measurement and data packet loss, a general nonlinear measurement model is further established as follows:
in the formula, zkIs the sensor model at time k; z is a radical ofk-1Is the sensor model at time k; z is a radical ofkk-1Is when z iskThe compensation amount when the compensation is lost is predicted for the measurement value at the k moment in one step; gamma raykAnd ηkIs an uncorrelated Bernoulli distribution variable and satisfies P is the probability; is an intermediate variable; y iskIs a k-time sensor model with measurement skew and packet loss.
Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the second embodiment is different from the first or second embodiment in that assumptions and reasoning are given in the second step; the specific process is as follows:
corresponding hypothesis and lemma are given, wherein the hypothesis is the premise of filter design, and the lemma is used for facilitating filter derivation;
hypothesis 1. hypothesis ωk,υk,γkAnd ηkAnd x0Is not related, and x0Satisfy the requirement of
In the formula, x0Is the initial value of the number of the first,an estimated value of the initial value, E [ ]]To meet expectations (·)TIs composed ofT is the transpose of the first image,the initial value corresponds to the covariance;
theory 1.A ═ aij]n×nIs a real valued matrix, B ═ diag { B }1,…,bnC and C ═ diag { C }1,…,cnIs a diagonal random matrix, defining
In the formula, aijIs the ith row and jth column element of the A matrix, [ a ]ij]n×nIs the ith row and the jth column element is aijN × n matrix of (d), diag denotes arranging the following elements thereof as a diagonal matrix, b1Is the 1 st element of the diagonal of the matrix, bnIs the nth element of the diagonal of the matrix, c1Is the 1 st element of the diagonal of the matrix, cnFor the nth element of the diagonal of the matrix, E { BACTIs a first pair matrix BACTMaking a multiplication operation and then asking for an expectation, an Hadamard product defining [ M ] N for arbitrary matrices M and N of the same dimension]ij=Mij·NijIt is clear that in the corresponding equation (12), even though A contains uncertainty, (12) still satisfies that A needs to be E [ A ] as long as A is uncorrelated with B, C]Replacing;
In the formula, Xk+1In order to augment the system with a wide range of systems,phi andare all intermediate variables;
Wherein I is an identity matrix, 0 is a zero matrix,is the expectation of phi, E phi]In order to make the expectation for phi,is composed ofIn the expectation that the position of the target is not changed,is a pair ofCalculating expectation;
a ≠ b means that a is not related to b. (a) (.)TWhere represents the same amount as a. T represents the rank when it is the upper-label. Y isk=L{yk,yk-1,…,y1Where L { · } represents a linear space formed by a · leaf. 0 and I represent the zero matrix and identity matrix of the appropriate dimensions. E [. C]Where E represents the mathematical expectation. E [ a | b ]]Representing the condition expectation of a under the condition of b. P represents a covariance matrix. N (-) represents the distribution function. Integral operation is represented by ^ integral.
Other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the difference between this embodiment and one of the first to third embodiments is that a gaussian filter is designed in the third step; the specific process is as follows:
the one-step prediction mean and covariance matrix are given as follows
Xk+1|k=Xk+1|k-1+Kkεk (1)
In the formula, Xk+1|k-1In order to perform the two-step prediction,predicting the covariance matrix, ε, for two stepskIn order to be a new message,is an innovation covariance matrix, T is transposed, KkFor the gain matrix, the following is defined
In the formula (I), the compound is shown in the specification,is X under the measurement condition of time k-1k+1And epsilonkCross covariance matrix of (a);
the mean and covariance matrices after measurement modification are as follows:
Xk+1|k+1=Xk+1|k+Mk+1εk+1 (4)
in the formula (I), the compound is shown in the specification,as an innovation covariance matrix, εk+1To be new, Mk+1Is a gain matrix;
in the formula (I), the compound is shown in the specification,is X under the measurement condition of time kk+1And epsilonk+1Cross covariance matrix of (a);
A. prediction
Theorem 1 for systems (13) - (14), the one-step prediction mean and covariance are as follows
Xk+1|k=Xk+1|k-1+Kkεk (17)
and (3) proving that:
the certification process is divided into two parts:
1) the two-step prediction mean and covariance are calculated.
According to GASF, there are
Xk+1|k=Xk+1|k-1+Kkεk (19)
Because of omegak⊥L{yk-1,…,y1Thus, E [ omega ]k|Yk-1]Is equal to 0, so
Bringing (20) into (19) to obtain (17);
A covariance-based definition sum (21) of
Rewriting Xk+1|k-1And with Xk+1Are subtracted to obtain
According to the definition of covariance, there are
Here, the
Substituting (24) into (22) to obtain (18).
2) A gain matrix is calculated.
The gain matrix is defined as
Here, the first and second liquid crystal display panels are,
Here, the
In (31), N1As follows
here, ωk-1|k-1And upsilonk-1|k-1The calculation is as follows.
yk-1And ωk-1The joint distribution of (A) can be as follows
According to the conditional Gaussian distribution theorem, obtaining
For upsilonk-1Available as above
Here, the first and second liquid crystal display panels are,
bringing (27) and (38) into (26) and (26) into (25), K can be obtainedk;
The certification is over.
B. Correction
Theorem 2 for systems (13) - (14), the state corrections for the mean and covariance of the Gaussian filter are as follows:
Xk+1|k+1=Xk+1|k+Mk+1εk+1 (43)
wherein the content of the first and second substances,
in the formula, yk+1For the k +1 moment sensor model with measurement lag and packet loss, N (-) is a distribution function, xk+1|kState x at time k +1k+1The one-step prediction value of (1),is a state xk+1One step of predicting covariance, xkkIs state x at time kkIs determined by the estimated value of (c),is state x at time kkCovariance matrix of (v)k|kMeasuring noise upsilon for time kkAn estimated value of (d);
the certification process is divided into two parts:
According to new message epsilonk+1Is defined as
εk+1=yk+1-yk+1|k (47)
Here, the
Overwrite yk+1|kIs provided with
Here upsilonk|kThe calculation process is as follows (36)k-1|k-1. Then, the user can use the device to perform the operation,
is obviously provided with
E[φAφT]=Φ⊙E[A] (52)
Through a series of algebraic operations, obtain
Hereinafter, the term on the right side is calculated (51).
Item 1.
Here, the first and second liquid crystal display panels are,
Item 2.
Where N is2And N1In the same form, but N1K in (1) needs to be replaced by k +1,
Item 3.
Item 4.
Item 6.
Item 7.
Item 8.
Item 9.
because of upsilonk+1And upsilonkIs not related, available
Item 11.
Item 12.
Item 13.
2) Computing a gain matrix Mk+1.
Here, the first and second liquid crystal display panels are,
The certification is complete.
Other steps and parameters are the same as those in one of the first to third embodiments.
The fifth concrete implementation mode: the difference between this embodiment and the first to the fourth embodiment is that the gaussian weighted integral in step four is approximated based on the third-order sphere diameter volume rule to obtain the numerical form of the designed filter; the specific process is as follows:
based on the sphere diameter volume rule algorithm, the numerical implementation of the algorithm provided by the invention is as follows.
And (3) prediction:
1. decomposition of
In the formula (I), the compound is shown in the specification,one-step predictive covariance matrix, M, for the state at time kk|k-1Is the intermediate variable(s) of the variable,augmenting system for time kThe one-step prediction of the covariance matrix,is an intermediate variable;
in N1In the middle, let
In the formula, N1Is a Gaussian distribution, Γk,k-1|k-1Being intermediate variables, is the state x of the constructkAnd noise vk-1Estimation based on measurements at time k-1, xk|k-1One-step prediction of state at time k, vk-1|k-1For measuring the estimated value of the noise at the time k-1, Πk,k-1|k-1Is the intermediate variable(s) of the variable,the covariance matrix is predicted for the state at time k in one step,is a state xkAnd noise vk-1Based on the cross covariance matrix measured at time k-1,covariance matrix, M, for the measured noise at time k-1k,k-1|k-1Is an intermediate variable;
2. calculating volume points
xi,k|k-1=Mk|k-1ξi+xk|k-1,i=1,…2n (88)
In the formula, xi,k|k-1Volumetric point, ξ, for time k with respect to a one-step predictori、ζi、Sigma points and dimensions of 2n, 4n and 2(n + m), respectively;for component x in joint estimationk|k-1The volume point of (a) is,for component x in joint estimationk-1|k-1Volume point of (1), Xi,k|k-1Augmenting system federation states for time kOne step of predicting the volume point, δi,k,k-1|k-1To correspond to gammak,k-1|k-1In xk|k-1The ith volume point of the part, gammai,k,k-1|k-1To correspond to gammak,k-1|k-1Is on vk-1|k-1Ith volume point of the part, Mk,k-1|k-1Being an intermediate variable, Γk,k-1|k-1Is a middleThe variable, n is the system state dimension, and m is the measurement noise dimension;
3. calculating propagated volume points
In the formula (I), the compound is shown in the specification,is xi,k|k-1The volume points after propagation through the system model,is xi,kk-1The volume point, h (-) after propagation through the metrology model is a known non-linear function,the volume points, f (-) after propagation through the system model are known non-linear functions,is composed ofThe volume points after propagation through the metrology model,is deltai,k,k-1|k-1Volume points after propagation through the system model;
definition of
In the formula I1、l2、l3、l4、l5、l6、l7、l8、l9、l10、l11、l12、l13、l14、l15Is an intermediate variable;
4. calculating the corresponding quantities in equations 1 and 2
where ω isk-1|k-1And upsilonk-1|k-1Given in (34) and (36);
in the formula, xk|k-1The mean is predicted for one step of the state at time k,the covariance is predicted for one step of the state at time k,is a state x under the measurement condition at the time k-1k+1The cross-covariance matrix with the innovation at time k,is a state x under the measurement condition at the time k-1kCross covariance matrix with innovation at time k, QkProcess noise at time k;
and (3) correction:
1. decomposition of
In the formula (I), the compound is shown in the specification,one-step prediction of covariance matrix, M, for state at time k +1k+1|kIs the intermediate variable(s) of the variable,estimating a covariance matrix, M, for a state at time kk|kIs the intermediate variable(s) of the variable,augmenting system joint states for k +1 momentsThe one-step prediction of the covariance matrix,is an intermediate variable;
in N2In the middle, let
In the formula, N2Is a Gaussian distribution, Γk+1,k|kIs an intermediate variable, Πk+1,k|kIs gammak+1,k|kThe corresponding covariance matrix is then used as a basis,the cross covariance matrix is predicted for one step at time k +1,is a state x under the measurement condition of time kk+1And measure noise vkThe cross-covariance matrix of (a) is,measuring a posterior covariance matrix of the noise at the time k;
2. calculating volume points
xi,k+1|k=Mk+1|kξi+xk+1|k,i=1,…2n (109)
xi,k|k=Mk|kξi+xk|k,i=1,…2n (110)
In the formula, xi,k+1|kOne-step prediction of the volume point, x, for the state quantity at the time k +1k+1|kFor one-step prediction of state quantities at the time k +1, xi,k|kCorresponding product point, x, for the estimated value of state quantity at time kk|kIs an estimate of the state quantity at time k, Xi,k+1|kAugmenting system joint state quantities for k +1 momentsThe volume point is predicted in one step,augmenting system joint state quantities for k +1 momentsCorresponding to xk+1|kThe volume point of the part of the volume,augmenting system joint state quantities for k +1 momentsCorresponding to xk|kThe volume point of the part of the volume,is an intermediate variable, Xk+1|kIs a joint state quantity at the time of k +1One step prediction, δi,k+1,k|kIs a combined quantityCorresponding to xk+1|kPartial volume point, gammai,k+1,k|kIs a combined quantityCorresponding upsilonk|kPartial volume points, Mk+1,k|kBeing an intermediate variable, Γk+1,k|kIs an intermediate variable;
3. calculating propagated volume points
In the formula (I), the compound is shown in the specification,is xi,k+1|kThe volume points propagated through the system model,is xi,k|kThe volume points propagated through the system model,is xi,k+1|kThe volume points propagated through the metrology model,is xi,k|kThe volume points propagated through the metrology model,is composed ofThe volume points propagated through the metrology model,is composed ofThe volume points propagated through the metrology model,is deltai,k+1,k|kVolume points propagated through the metrology model;
definition of
Of formula (II) to'1、l′2、l′3、l′4、l′5、l′6、l′7、l′8、l′9、l′10、l′11、l′12、l′13、l′14、l′15、l′16、l′17Is an intermediate variable;
4. calculating the corresponding quantities in equations 4, 5 and 6
In the formula, yk+1A k +1 moment sensor model with measurement time lag and data packet loss exists;
In the formula (I), the compound is shown in the specification,an innovation covariance matrix at the time k +1, phi is an intermediate variable, Hadamard product, psi is an intermediate variable, and xi is an intermediate variable,is a state x measured at time kkThe cross-covariance matrix with the innovation at time k +1,a cross-covariance matrix of the state at time k +1 and the innovation at time k +1 measured at time k, SkFor the cross-correlation noise at time k, omegak|kMeasuring process noise omega for time kkAn estimated value of (d);
Xk+1|k+1=Xk+1|k+Mk+1εk+1 (43)
other steps and parameters are the same as in one of the first to fourth embodiments.
The first embodiment is as follows:
numerical simulation analysis
To verify the validity of the control algorithm designed by the present invention, two non-linear models were used to test the validity of the proposed algorithm. Wherein, 'original', 'this paper' and '23' represent reference signals, the estimation result of the algorithm proposed herein and the estimation result of the algorithm in [23] are extended to a nonlinear system based on EKF.
1) Single variable non-static growth model (UNGM)
UNGM is represented as follows:
where ω iskAnd upsilonkIs zero mean white Gaussian noise and the covariance is satisfied
Initial value of x0Filter initial value is x ═ 0.30|00 and P0|0Probability satisfied by random variables of Bernoulli distributionAnda sub-independent monte carlo simulation is performed. And the Error at time k (Error) and the Root Mean Square Error (RMSE) are defined as follows
Here, theAndshowing the true and estimated values of the nth Monte Carlo method at time k, N being the total number of simulations, FIGS. 1-3 are corresponding simulation results, where FIG. 1 illustrates that the estimation results herein are closer to the true values and have better results than the literature [23]]Fig. 2 and 3 show the algorithm and document [23]]The advantages of the algorithm are further illustrated by the estimation error and the estimation root mean square error of the algorithm, the algorithm fluctuation is small, and the algorithm robustness is good.
2) Strongly non-linear model
The strong nonlinear model is given as follows:
zk=cos(x1,k)+x2,kx3,k+υk (135)
here, xkAnd zkIs the system state and quantity measurement, ωkAnd upsilonkIs zero mean white Gaussian noise and the covariance is satisfied
Initial state is x0=[-0.7 1 1]T. The initial value of the filter is x0|0=[0 0 0]TAnd P0|0=I3。Andas described above.
Fig. 4-5 are corresponding simulation results, where fig. 4 and 5 are respectively an estimation error and an estimated root mean square error of the algorithm in this document and the algorithm in document [23], and the simulation results show that the proposed algorithm still has a strong processing capability even for a strong nonlinear system, while the algorithm in document [23] hardly obtains a satisfactory true value, which illustrates that the algorithm has a stronger capability of processing problems when applied to an actual system.
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.
Claims (1)
1. The Gaussian filtering method based on the nonlinear network system under the nonideal condition is characterized by comprising the following steps: the method comprises the following specific processes:
firstly, establishing a system model and a sensor measurement model;
step two, providing hypothesis and lemma;
thirdly, designing a Gaussian filter based on the second step;
step four, based on a third-order sphere diameter volume rule, approximating the Gaussian weighted integral in the step three to obtain a numerical form of the designed filter;
establishing a system model and a sensor measurement model in the first step; the specific process is as follows:
establishing a nonlinear discrete time system model with correlated noise:
xk+1=f(xk)+ωk (7)
establishing a nonlinear measurement model:
zk=h(xk)+υk (8)
in the formula, xk+1System state at time k +1, xkSystem state at time k, xk,xk+1∈Rn,RnIs an n-dimensional real number space; z is a radical ofkIs the sensor model at time k, zk∈Rm,RmIs m-dimensional real number space; f (-) and h (-) are known non-linear functions; omegak∈RnAnd upsilonk∈RmIs correlated zero mean white Gaussian noise and has a covariance of
In the formula, deltaklIs the Kronecker delta function, QkAnd RkRespectively process noise and measurement noise covariance, SkIs the cross-covariance, l is the time l, ωl∈RnAnd upsilonl∈RmIs correlated zero mean gaussian white noise;
considering communication bandwidth, delay measurement and data packet loss, the nonlinear measurement model is further established as follows:
in the formula, zkIs the sensor model at time k; z is a radical ofk-1Is a k-1 moment sensor model; z is a radical ofk|k-1Is when z iskThe compensation amount when the compensation is lost is predicted for the measurement value at the k moment in one step; gamma raykAnd ηkIs an uncorrelated Bernoulli distribution variable and satisfiesP is the probability; is an intermediate variable; y iskThe k-time sensor model has measurement time lag and data packet loss;
giving assumptions and lemmas in the second step; the specific process is as follows:
hypothesis 1. hypothesis ωk,υk,γkAnd ηkAnd x0Is not related, and x0Satisfy the requirement of
In the formula, x0Is the initial value of the number of the first,an estimated value of the initial value, E [ ]]To meet expectations (·)TIs composed ofT is the transpose of the first image,the initial value corresponds to the covariance;
theory 1.A ═ aij]n×nIs a real valued matrix, B ═ diag { B }1,…,bnC and C ═ diag { C }1,…,cnIs a diagonal random matrix, defining
In the formula, aijIs the ith row and jth column element of the A matrix, [ a ]ij]n×nIs the ith row and the jth column element is aijN × n matrix of (d), diag denotes arranging the following elements thereof as a diagonal matrix, b1Is the 1 st element of the diagonal of the matrix, bnIs the nth element of the diagonal of the matrix, c1Is the 1 st element of the diagonal of the matrix, cnFor the nth element of the diagonal of the matrix, E { BACTIs a first pair matrix BACTA multiplication operation is performed and then an expectation is asserted, an Hadamard product;
In the formula, Xk+1In order to augment the system with a wide range of systems,phi andare all intermediate variables;
Wherein I is an identity matrix, 0 is a zero matrix,is the expectation of phi, E phi]In order to make the expectation for phi,is composed ofIn the expectation that the position of the target is not changed,is a pair ofCalculating expectation;
designing a Gaussian filter in the third step; the specific process is as follows:
the one-step prediction mean and covariance matrix are given as follows
Xk+1|k=Xk+1|k-1+Kkεk (1)
In the formula, Xk+1|k-1In order to perform the two-step prediction,predicting the covariance matrix, ε, for two stepskIn order to be a new message,is an innovation covariance matrix, T is transposed, KkFor the gain matrix, the following is defined
In the formula (I), the compound is shown in the specification,is X under the measurement condition of time k-1k+1And epsilonkCross covariance matrix of (a);
the mean and covariance matrices after measurement modification are as follows:
Xk+1|k+1=Xk+1|k+Mk+1εk+1 (4)
in the formula (I), the compound is shown in the specification,as an innovation covariance matrix, εk+1To be new, Mk+1Is a gain matrix;
in the formula (I), the compound is shown in the specification,is X under the measurement condition of time kk+1And epsilonk+1Cross covariance matrix of (a);
based on the third-order sphere diameter volume rule in the fourth step, the Gaussian weighted integral in the third step is approximated to obtain the numerical form of the designed filter; the specific process is as follows:
and (3) prediction:
1. decomposition of
In the formula (I), the compound is shown in the specification,one-step predictive covariance matrix, M, for the state at time kk|k-1Is the intermediate variable(s) of the variable,augmenting system for time kThe one-step prediction of the covariance matrix,is an intermediate variable;
in N1In the middle, let
In the formula, N1Is a Gaussian distribution, Γk,k-1|k-1Being intermediate variables, is the state x of the constructkAnd noise vk-1Estimation based on measurements at time k-1, xk|k-1One-step prediction of state at time k, vk-1|k-1For measuring the estimated value of the noise at the time k-1, Πk,k-1|k-1Is the intermediate variable(s) of the variable,the covariance matrix is predicted for the state at time k in one step,is a state xkAnd noise vk-1Based on the cross covariance matrix measured at time k-1,covariance matrix, M, for the measured noise at time k-1k,k-1|k-1Is an intermediate variable;
2. calculating volume points
xi,k|k-1=Mk|k-1ξi+xk|k-1,i=1,…2n (88)
In the formula, xi,k|k-1Volumetric point, ξ, for time k with respect to a one-step predictori、ζi、Sigma points and dimensions of 2n, 4n and 2(n + m), respectively;for component x in joint estimationk|k-1The volume point of (a) is,for component x in joint estimationk-1|k-1Volume point of (1), Xi,k|k-1Augmenting system federation states for time kOne step of predicting the volume point, δi,k,k-1|k-1To correspond to gammak,k-1|k-1In xk|k-1The ith volume point of the part, gammai,k,k-1|k-1To correspond to gammak,k-1|k-1Is on vk-1|k-1Ith volume point of the part, Mk,k-1|k-1Being an intermediate variable, Γk,k-1|k-1Is an intermediate variable, n is a system state dimension, and m is a measurement noise dimension;
3. calculating propagated volume points
In the formula (I), the compound is shown in the specification,is xi,k|k-1The volume points after propagation through the system model,is xi,k|k-1The volume point, h (-) after propagation through the metrology model is a known non-linear function,is composed ofThe volume points, f (-) after propagation through the system model are known non-linear functions,is composed ofThe volume points after propagation through the metrology model,is composed ofVolume points after propagation through the system model;
definition of
In the formula I1、l2、l3、l4、l5、l6、l7、l8、l9、l10、l11、l12、l13、l14、l15Is an intermediate variable;
4. calculating the corresponding quantities in equations 1 and 2
in the formula, xk|k-1The mean is predicted for one step of the state at time k,the covariance is predicted for one step of the state at time k,is a state x under the measurement condition at the time k-1k+1The cross-covariance matrix with the innovation at time k,is a state x under the measurement condition at the time k-1kCross covariance matrix with innovation at time k, QkProcess noise at time k;
and (3) correction:
1. decomposition of
In the formula (I), the compound is shown in the specification,one-step prediction of covariance matrix, M, for state at time k +1k+1|kIs the intermediate variable(s) of the variable,estimating a covariance matrix, M, for a state at time kk|kIs the intermediate variable(s) of the variable,augmenting system joint states for k +1 momentsThe one-step prediction of the covariance matrix,is an intermediate variable;
in N2In the middle, let
In the formula, N2Is a Gaussian distribution, Γk+1,k|kIs an intermediate variable, Πk+1,k|kIs gammak+1,k|kThe corresponding covariance matrix is then used as a basis,the cross covariance matrix is predicted for one step at time k +1,is a state x under the measurement condition of time kk+1And measure noise vkThe cross-covariance matrix of (a) is,measuring a posterior covariance matrix of the noise at the time k;
2. calculating volume points
xi,k+1|k=Mk+1|kξi+xk+1|k,i=1,…2n (109)
xi,k|k=Mk|kξi+xk|k,i=1,…2n (110)
In the formula, xi,k+1|kIs k +1One-step prediction of volume point, x, of state quantityk+1|kFor one-step prediction of state quantities at the time k +1, xi,k|kCorresponding product point, x, for the estimated value of state quantity at time kk|kIs an estimate of the state quantity at time k, Xi,k+1|kAugmenting system joint state quantities for k +1 momentsThe volume point is predicted in one step,augmenting system joint state quantities for k +1 momentsCorresponding to xk+1|kThe volume point of the part of the volume,augmenting system joint state quantities for k +1 momentsCorresponding to xk|kThe volume point of the part of the volume,is an intermediate variable, Xk+1|kIs a joint state quantity at the time of k +1One step prediction, δi,k+1,k|kIs a combined quantityCorresponding to xk+1|kPartial volume point, gammai,k+1,k|kIs a combined quantityCorresponding upsilonk|kPartial volume points, Mk+1,k|kBeing an intermediate variable, Γk+1,k|kIs an intermediate variable;
3. calculating propagated volume points
In the formula (I), the compound is shown in the specification,is xi,k+1|kThe volume points propagated through the system model,is xi,k|kThe volume points propagated through the system model,is xi,k+1|kThe volume points propagated through the metrology model,is xi,k|kThe volume points propagated through the metrology model,is composed ofThe volume points propagated through the metrology model,is composed ofThe volume points propagated through the metrology model,is deltai,k+1,k|kVolume points propagated through the metrology model;
definition of
Of formula (II) to'1、l′2、l′3、l′4、l′5、l′6、l′7、l′8、l′9、l′10、l′11、l′12、l′13、l′14、l′15、l′16、l′17Is an intermediate variable;
4. calculating the corresponding quantities in equations 4, 5 and 6
In the formula, yk+1A k +1 moment sensor model with measurement time lag and data packet loss exists;
in the formula (I), the compound is shown in the specification,an innovation covariance matrix at the time k +1, phi is an intermediate variable, Hadamard product, psi is an intermediate variable, and xi is an intermediate variable,is a state x measured at time kkThe cross-covariance matrix with the innovation at time k +1,a cross-covariance matrix of the state at time k +1 and the innovation at time k +1 measured at time k, SkFor the cross-correlation noise at time k, omegak|kMeasuring process noise omega for time kkAn estimated value of (d);
Xk+1|k+1=Xk+1|k+Mk+1εk+1 (43)
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