CN112034713B - Method and system for estimating optimal state of moving target in non-ideal network environment - Google Patents
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Abstract
The invention provides an optimal state estimation method and system of a moving target under a non-ideal network environment, which comprises the steps of establishing a kinematic model of the moving target and an instantaneous measurement and delay measurement model of the moving target; reconstructing a measurement sequence without time lag by adopting a measurement and innovation reconstruction method; constructing a moving target state recursive optimal state estimator, and predicting the position information of the moving target according to the instantaneous measurement information and the delayed measurement information; the invention effectively avoids the complex calculation problem of the traditional dimension extending method and solves the problems of unknown uncertainty, output time lag and the like of the measured signal.
Description
Technical Field
The invention belongs to the technical field of target tracking, and relates to an optimal state estimation method and system for a moving target in a non-ideal network environment.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the continuous development of society and the continuous progress of science and technology, people have higher requirements on the information acquisition mode. The wireless sensor network is generated and developed rapidly just because of the urgent need of information and the future development of sensors. The wireless sensor network highly crosses multiple disciplines, integrates wireless communication technology, sensor technology, automatic control technology and the like, and is widely applied to multiple fields such as target tracking nowadays.
The target tracking means that the time and the position of a moving target are recorded and uploaded to a base station in a monitoring area of a wireless sensor network. The process requires that the sensor nodes overcome the influence of the surrounding environment, and the tracking is finally completed through mutual cooperation in combination with the change of the network topology structure. The moving target tracking is widely applied to the fields of military affairs, traffic, video monitoring and the like, in particular to military vehicle tracking, multi-target detection, battlefield area monitoring and the like. The main task of the target tracking technology is to acquire information by using a sensor design, and then analyze and process the obtained state information of a moving target, such as the position and the speed of the moving target, so as to form a target track to determine the target position. With the increasingly complex measuring environment, the design and optimization of a filter, which is one of the key technologies affecting the tracking performance of a moving target, has become a difficult point and a key point of relevant research at home and abroad.
In a wireless sensor network, in order to obtain information more accurately, a large number of sensors are usually deployed in a monitoring area, and measurement data is often periodically sent to a sink node by a sensor node. The periodic data transmission mechanism inevitably generates a transmission time lag phenomenon. The occurrence of the time lag may cause the system to be unstable, and may even cause the overall performance of the system to be poor. On the other hand, detection of states or signals in wireless sensor networks often carries uncertainty (e.g., noise). The existence of uncertainty causes errors in the detection of the target state, and the original state cannot be accurately obtained.
Disclosure of Invention
The invention provides an optimal state estimation method and system for moving targets in a non-ideal network environment, aiming at solving the problems, the invention can more accurately restore signals or states, solves the problems of high-dimensional calculation and poor real-time performance brought by the traditional method, effectively avoids the complex calculation problem of the traditional dimension expansion method, and solves the problems of unknown uncertainty, output time lag and the like of measured signals.
According to some embodiments, the invention adopts the following technical scheme:
an optimal state estimation method for a moving target in a non-ideal network environment comprises the following steps:
establishing a kinematic model of a moving target and an instantaneous measurement and delay measurement model of the moving target;
reconstructing a measurement sequence without time lag by adopting a measurement and innovation reconstruction method;
and constructing a moving target state recursive optimal state estimator, and predicting the position information of the moving target according to the instantaneous measurement information and the delayed measurement information.
As an alternative embodiment, the specific process of establishing the kinematic model of the moving object includes: and determining the state vector of the next moment according to the state vector of the moving target at a certain moment and the disturbance existing in the motion process.
As an alternative embodiment, the influence of additive measurement noise and unknown multiplicative noise is considered in the process of establishing the instantaneous measurement and delay measurement model of the moving target.
As an alternative embodiment, a measurement and innovation reconstruction method is adopted, and a specific process for reconstructing a measurement sequence without time lag comprises the following steps: describing an original measurement sequence with time lag, and reconstructing a measurement sequence without time lag by adopting a measurement and innovation reconstruction method.
As an alternative embodiment, the specific process of constructing the moving target state recursive optimal state estimator includes: and performing state prediction, calculating an autocovariance matrix and an interactive covariance matrix of the state estimation error, the measurement information estimation error and the multiplicative noise product estimation error, and updating and optimizing parameters of the optimal state estimator according to the calculated matrixes.
An optimal state estimation system for a moving target in a non-ideal network environment, comprising:
the mobile target model building module is configured to build a kinematic model of the mobile target and a transient measurement and delay measurement model of the mobile target;
the measurement sequence reconstruction module is configured to reconstruct a measurement sequence without time lag by adopting a measurement and innovation reconstruction method;
and the optimal state estimator is configured to recur the state of the moving target according to the instantaneous measurement information and the delayed measurement information, realize updating optimization and predict the position information of the moving target.
As an alternative embodiment, the moving target model building module is configured to determine the state vector of the moving target at the next moment according to the state vector of the moving target at a certain moment and the disturbance existing in the motion process, and build the instantaneous measurement and delay measurement model of the moving target by considering the influence of the additive measurement noise and the unknown multiplicative noise.
In an alternative embodiment, the metrology sequence reconstruction module is configured to describe an original metrology sequence with a time lag, and reconstruct a time-lag-free metrology sequence by using metrology and innovation reconstruction methods.
As an alternative embodiment, the optimal state estimator is configured to perform state prediction, calculate an auto-covariance matrix and an cross-covariance matrix of the state estimation error, the metrology information estimation error, and the multiplicative noise product estimation error, and update and optimize parameters of the optimal state estimator according to the calculated matrices.
A computer readable storage medium, having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to execute the method for estimating an optimal state of a moving object in a non-ideal network environment.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the optimal state estimation method for the moving target in the non-ideal network environment.
Compared with the prior art, the invention has the beneficial effects that:
aiming at the problems of uncertain modeling and measurement time lag of measurement information in a non-ideal network environment, the invention provides a modeling method of the measurement information with unknown uncertainty and a reconstruction method of measurement and innovation, and solves the problems of poor high-dimensional calculation and real-time performance brought by the traditional method; the design method of the coupling optimal estimator is provided, so that the system can well eliminate the influence of time delay and unknown colored uncertainty and obtain more accurate original data.
The invention provides a design method of a recursive coupling estimator, namely the design of a moving target state estimator and the design of an estimator of a product of a moving target and unknown colored multiplicative noise, wherein the two types of estimators can perform recursive calculation on line to obtain the optimal state estimation of the moving target, thereby effectively avoiding the complex calculation problem of the traditional dimension expansion method and solving the problems of unknown uncertainty, output time lag and the like of a measurement signal.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of the design of the present invention;
FIG. 2 is a flow chart of the optimum state estimator of the present invention;
FIG. 3 is a graph showing a comparison between the ground true value and the target trajectory estimated value of the x-axis direction position px of the moving target;
FIG. 4 is a graph showing the comparison of the ground true value of the y-axis position py of the moving target with the estimated value of the target trajectory;
FIG. 5 is a plot of the root mean square error (RMS) of a moving target x-axis position px;
FIG. 6 is a plot of the root mean square error (RMS) of the y-axis position py of the moving target.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Aiming at the problems of unknown uncertainty and output time lag of measured signals in the non-ideal network environment, the optimal state estimation method of the moving target is provided to reduce the calculated amount and realize the accurate acquisition of target information in the non-ideal network environment.
The method mainly comprises the following steps: modeling unknown uncertainty of a measurement signal aiming at a non-ideal network environment; secondly, the problems of high calculation amount and poor real-time performance of the traditional state dimension extending method are avoided, and a new method for measuring and reconstructing information is provided to solve the problem of output time lag; and finally, based on the coupling idea of the moving target state and the unknown uncertainty estimator, providing an optimal state estimation method of the moving target in the mean square sense.
The optimal state estimation method of the moving target comprises the following steps:
step one, system modeling: and respectively establishing a kinematic model of the moving target and an instantaneous measurement and delay measurement model of the moving target.
1) Establishing a moving target state model:
moving target state model:
x(k+1)＝Ax(k)+Dw(k) (1)
wherein: x (k) ε RnIs the state vector of the moving target at time k, w (k) e RnIs a small disturbance that exists during the motion of the moving object. The values of the coefficient matrices a and D are determined experimentally.
2) Establishing a moving target measurement signal model
Measuring a signal model:
aiming at the problem of output time lag existing in a measurement signal, the following two-sensor measurement model is considered
y0(k)＝[B+B0ζ0(k)]x(k)+υ0(k) (2)
y1(k)＝[C+C0ζ1(k-d)]x(k-d)+υ1(k) (3)
Wherein:
and
respectively representing instantaneous and delayed measurements of the sensor;
and
are all additive measurement noise; zeta0(k) And ζ1(k) For unknown multiplicative noiseThe modeling uncertainty problem existing in the measurement model is characterized, and the time correlation of the front time and the rear time is characterized by the following formula:
ζi(k+1)＝Hiζi(k)+εi(k)，i＝0，1 (4)
wherein: epsilon0(k) And ε1(k) Are all additive white noise. B, B0，C，C0，H0And H1Is a matrix of appropriate dimensions, the specific values being determined experimentally.
Step two, processing of output time lag: the invention adopts a measurement and innovation reconstruction method to reconstruct a measurement sequence without time lag.
First we describe the original metrology sequence of the system with time lag. For convenience of description, let y (k) denote the measurement signal received by the estimator at time k, and may be written as follows:
wherein: y is1(k) Is a measurement signal for a moving target state x (k-d), x (k-d) is a state with a time lag d at time k, then the measurement sequence is performed when k ≧ d
Including time lag, standard kalman filtering methods cannot be used directly to solve such estimation problems.
In order to solve the problem, the invention adopts a method of measurement and innovation reconstruction to reconstruct a measurement sequence without time lag. Let k be for ease of discussion1Assuming k ≧ d, the case where k < d can be similarly discussed. In a known measurement sequence
In the case of (2), the optimum state estimator
Refers to the linearity of state x (k) in the measurement sequenceSpace(s)
Projection of (2). The system originally contained the linear space spanned by the time-lapse measurement sequence
Is equivalent to
It is clear that the following description of the preferred embodiments,
i 1, 2 satisfies the following equation
Wherein:
from equation (6), the new measurement
There is no time lag. In addition, the first and second substrates are,
wherein:
after reconstitution
And
are zero-mean white noise which are uncorrelated with each other and have variances of
And
in addition, the first and second substrates are,
and
are also zero-mean white noise which are uncorrelated and have variances of
And
reconstructed innovation
Indicating measurement errors, i.e. measurement information
The difference value of the predicted value of the one step,
Satisfy the requirement of
Wherein:
and
(s≤k1+1) denotes x(s) and
in the measurement sequence
The state prediction value of the state. In the same way, the method for preparing the composite material,
and
(s＞k1+1) denotes x(s) and
in the measurement sequence
And (5) predicting the next step.
Represents the state x(s) and the state prediction value
The error value of (2);
representing the product of multiplicative noise and state
And a step prediction value
The error of (2).
From the above description, we can know the reconstructed innovation sequence
The constructed linear space is equivalent to
Or
That is, the reconstructed sequence without lag time innovation is equivalent to the original sequence. And this sequence will play a key role in the design of the recursive optimal state estimator.
Step three, designing a moving target state recursion optimal state estimator: and predicting the position information of the moving target in real time according to the instantaneous measurement information and the delay measurement information of the system by referring to a standard Kalman filtering method.
The moving target state recursion optimal state estimator is designed as follows:
wherein when the time s is k1Time, one-step prediction
And
are equal.
And 2, for a given k > d, calculating the auto-covariance matrix and the cross covariance matrix of the system as follows:
when s is more than or equal to 0 and less than or equal to k1For a given initial value P2(0)＝P20 
And
when k is1S is less than or equal to k, since P1(k1+1)＝P2(k1+1)，
And
Wherein: pi(s +1) an autocovariance matrix representing the state estimation error at time s + 1;
an interactive covariance matrix representing the state at the s +1 th time and the state estimation error and the multiplicative noise product estimation error;
an autocovariance matrix representing the s +1 th state and the multiplicative noise product estimation error. In order to be able to find the autocovariance matrix and the cross-covariance matrix in step 2, we also need some update terms in the calculation formula
Ni(s)，
Ξi(s)，
Wherein:
an interactive covariance matrix representing the state estimation error and the measurement information estimation error at the s-th moment; n is a radical ofi(s) an autocovariance matrix representing an estimated error of the measurement information at time s;
an interactive covariance matrix representing the state at the s-th moment, the multiplicative noise product estimation error and the measurement information estimation error; xii(s) an autocovariance matrix representing the multiplicative noise at time s;
a covariance matrix representing the state at time s.
in the above step, Ki(s) is the filter gain calculated at time s; q is the covariance matrix of the system noise, which is given by the following equations
Q＝E[w(s)w(s)T] (30)
The specific implementation process is realized by means of a simulation tool Matlab. The effects of the present invention can be further illustrated by the following experimental simulations.
The estimated object of the invention is directed at the vehicle moving at a constant speed in a two-dimensional space, so that the estimation target can estimate the accurate real-time position information of the vehicle moving at the constant speed according to the instantaneous measurement information and the delay measurement information of the sensor. The main design process is described as follows, firstly, modeling is carried out on a vehicle which moves at a constant speed in a two-dimensional space, and a proper mathematical model is established.
The design flow of the present invention is shown in fig. 1, and the flow of the optimum state estimator is shown in fig. 2; the estimation method aims to estimate the vehicle moving at a constant speed in a two-dimensional space in real time so as to acquire accurate position information of the vehicle.
In the present embodiment, a target vehicle motion state x ═ p is definedx py vx vy]. By pxAnd pyRespectively representing the position of the vehicle in the x-direction and the y-direction in a two-dimensional space by vxAnd vyRepresenting the speed in the x-direction and the y-direction, respectively, and a discrete step size h, the vehicle dynamics is of the form
Where w (k) is the perturbation term of the acceleration, modeled as zero-mean white gaussian noise, and the covariance matrix satisfies Q ═ diag (0.6 ). In the numerical example, h is 0.2.
Measured value y of instantaneous sensor and time delay sensor0And y1Is described as follows
Wherein: measurement noise v0(k)，v1(k) White Gaussian noise with zero mean and covariance with a value of R0＝diag(1.4,0.3),R1Biag (0.7, 1.4). Unknown multiplicative noise ζ0(k) And ζ1(k) The time dependence of the preceding and following moments is characterized by the following dynamic equation
Additive noise epsilon in the dynamic equationi(k) White Gaussian noise with zero mean value and covariance satisfying sigmai(k)＝I4. Then we set the initial values of the other system estimated covariance matrix and the cross covariance matrix to
The system measures the time lag d to 20. In this state estimation experiment, the vehicle first travels at a constant speed at an initial speed of 0.8m/s in the x-axis direction and 0.4m/s in the y-axis direction at a position 3m in the x-axis direction from the start point and 3m in the y-axis direction from the start point. We estimated the vehicle state in the first 200 seconds as is evident from fig. 3 and 4, the estimated value is very close to the true position during the entire tracking period. From FIGS. 5 and 6, we can see thatThe root mean square position error of the filter is within 1 m. Therefore, the designed coupling filter has good response to the maneuvering target, can effectively eliminate the influence of time lag and noise on the vehicle state, and can obtain relatively accurate original data.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A method for estimating the optimal state of a moving target in a non-ideal network environment is characterized by comprising the following steps: the method comprises the following steps:
establishing a kinematic model of a moving target and an instantaneous measurement and delay measurement model of the moving target;
reconstructing a measurement sequence without time lag by adopting a measurement and innovation reconstruction method;
constructing a moving target state recursion coupling optimal state estimator, and predicting the position information of the moving target according to the instantaneous measurement information and the delayed measurement information; the moving target state recursive coupling optimal state estimator is formed by coupling a moving target state estimator and an estimator of the product of a moving target and unknown colored multiplicative noise, and obtaining the optimal state estimation of the moving target through online recursive calculation of the two types of estimators.
2. The method as claimed in claim 1, wherein the mobile object is moved in a non-ideal network environment, and the method comprises: the specific process of establishing the kinematic model of the moving target comprises the following steps: and determining the state vector of the next moment according to the state vector of the moving target at a certain moment and the disturbance existing in the motion process.
3. The method as claimed in claim 1, wherein the mobile object is moved in a non-ideal network environment, and the method comprises: in the process of establishing the instantaneous measurement and delay measurement model of the moving target, the influence of additive measurement noise and unknown multiplicative noise is considered.
4. The method as claimed in claim 1, wherein the mobile object is moved in a non-ideal network environment, and the method comprises: the specific process of reconstructing the measurement sequence without time lag by using the measurement and innovation reconstruction method comprises the following steps: describing an original measurement sequence with time lag, and reconstructing a measurement sequence without time lag by adopting a measurement and innovation reconstruction method.
5. The method as claimed in claim 1, wherein the mobile object is moved in a non-ideal network environment, and the method comprises: the specific process for constructing the moving target state recursive optimal state estimator comprises the following steps: and performing state prediction, calculating an autocovariance matrix and an interactive covariance matrix of the state estimation error, the measurement information estimation error and the multiplicative noise product estimation error, and updating and optimizing parameters of the optimal state estimator according to the calculated matrixes.
6. An optimal state estimation system of a moving target in a non-ideal network environment is characterized in that: the method comprises the following steps:
the mobile target model building module is configured to build a kinematic model of the mobile target and a transient measurement and delay measurement model of the mobile target;
the measurement sequence reconstruction module is configured to reconstruct a measurement sequence without time lag by adopting a measurement and innovation reconstruction method;
the optimal state estimator is configured to recur the state of the moving target according to the instantaneous measurement information and the delayed measurement information, realize updating optimization and predict the position information of the moving target; the moving target state recursive coupling optimal state estimator is formed by coupling a moving target state estimator and an estimator of the product of a moving target and unknown colored multiplicative noise, and obtaining the optimal state estimation of the moving target through online recursive calculation of the two types of estimators.
7. The system of claim 6, wherein the mobile object's optimal state estimation system in a non-ideal network environment comprises: the moving target model building module is configured to determine a state vector of a next moment according to the state vector of the moving target at a certain moment and disturbance existing in a motion process, and build an instantaneous measurement and delay measurement model of the moving target by considering the influence of additive measurement noise and unknown multiplicative noise.
8. The system of claim 6, wherein the mobile object's optimal state estimation system in a non-ideal network environment comprises: the optimal state estimator is configured to perform state prediction, calculate an auto-covariance matrix and an cross-covariance matrix of a state estimation error, a metrology information estimation error, and a multiplicative noise product estimation error, and update and optimize parameters of the optimal state estimator according to the calculated matrices.
9. A computer-readable storage medium characterized by: a plurality of instructions are stored, the instructions are suitable for being loaded by a processor of a terminal device and executing the optimal state estimation method for the moving target in the non-ideal network environment according to any one of claims 1-5.
10. A terminal device is characterized in that: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; the computer readable storage medium is used for storing a plurality of instructions, the instructions are suitable for being loaded by a processor and executing the optimal state estimation method for the moving target in the non-ideal network environment of any one of claims 1-5.
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