CN117255359A - Robust collaborative state estimation method and system with event triggering mechanism - Google Patents
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Abstract
The invention discloses a robust collaborative state estimation method and a robust collaborative state estimation system with an event triggering mechanism, and relates to the technical field of sensor network state estimation. According to the invention, by introducing an event triggering mechanism, when the error of two adjacent measurements reaches a triggering threshold value, the prediction is updated and corrected. Compared with the traditional fixed time interval updating method, the event triggering mechanism can effectively reduce the airborne computing resources of each sensor and the communication resources among nodes, and greatly improves the algorithm efficiency. In addition, aiming at the situation of large maneuvering of the target, suboptimal fading factors are introduced to realize accurate estimation of the large maneuvering target, filtering gain can be adjusted to be strong so that residual sequences are orthogonal, influence of past moment data on a current moment filtering value is reduced, accurate estimation of a target state is realized, influence on filtering during abrupt maneuvering of the target is further controlled, and filtering precision is improved. And finally, fusing the local filtering result by using a consistency algorithm to realize accurate estimation of the state.
Description
Technical Field
The invention relates to the technical field of sensor network state estimation, in particular to a robust collaborative state estimation method and system with an event trigger mechanism.
Background
With the development of sensor network technology, the distributed state estimation based on the mobile sensor network is widely applied in the directions of environment monitoring, information fusion, target tracking and the like, and attracts great attention. The distributed kalman filter algorithm becomes one of the most commonly used algorithms for state estimation in environments where noise is handled and measured, and solves the problem of a discrete time system with gaussian white noise. The distributed Kalman filtering algorithm not only generates less communication burden than centralized one, but also has stronger robustness to topology switching when the sensor or sensors work abnormally. The full development of the mobile sensor network makes it possible to obtain more accurate measurement information, thereby improving the accuracy of the maneuvering target state estimation. The application of the distributed sensor network technology to the distributed kalman filtering algorithm has received great attention in the research of recent years. To facilitate accurate estimation of the state of a target, distributed kalman filtering algorithms based on distributed sensor networks are widely used in maneuvering target tracking. At the same time, consistency-based algorithms are one of the most popular approaches to solving the distributed state estimation problem. The distributed state estimation algorithm based on consistency enables the information pair obtained by the local filtering algorithm to reach the same value by fusing the latest measured value or estimated value of the adjacent nodes. Thus, consistency-based distributed state estimation algorithms can be divided into three categories: measurement consistency, estimation consistency, and blend consistency.
Although distributed state estimation of consistency-based algorithms on mobile sensor networks (distributed sensor networks) can be used to solve the distributed state estimation problem, accurate state estimation of highly mobile targets remains challenging. Because of uncertain environment and mobility characteristics of the target, it is difficult to estimate the state of the target when conditions such as communication network congestion, noise interference, intermittent observation or data packet loss are encountered. In general, it is difficult to accurately obtain the motion state of a target directly from a distributed sensor network. In addition, the sensors of a distributed sensor network are typically battery powered, which makes this process also dependent on battery life. However, intermittent monitoring of sensors and reduced data processing remain a major issue. There are many documents related to this, such as a consistency-based distributed kalman filter algorithm for information fusion, an event trigger mechanism for reducing energy consumption, and a strong tracking filter algorithm for neutral target tracking. Olfati-Saber R proposes a distributed kalman filter algorithm for linear system state estimation and a distributed extended kalman filter for nonlinear systems, which also forms the basic framework of the distributed volumetric kalman filter algorithm. In the distributed framework, nodes in the sensor network switch only the latest local sensing data with their neighboring nodes. Battisteli G and Chisci L analyze the stability of the distributed extended Kalman filter algorithm under certain constraints, however, when the system has a highly nonlinear characteristic, the distributed extended Kalman filter algorithm still has some limitations, which promote the development of the distributed Kalman filter. To overcome the communication failure problem, ge QB analyzes the correlated noise and proposes a distributed volumetric kalman filter algorithm. Based on the intermittent observation of weighted average consistency, tan QK proposes a distributed event triggered volumetric kalman filter algorithm. Meanwhile, based on an event triggering mechanism, people have published a lot of related articles and have done a lot of related works. Miskowicz M proposes event-triggered filtering based on a time interval signaling mechanism to reduce power consumption. Zhang C proposes a sensor-to-estimator schedule that can alleviate the communication burden. Li proposes a distributed unscented kalman filter algorithm by introducing an event trigger mechanism, but only a single sensor is considered instead of a distributed sensor network. In addition, in order to track maneuver targets, a strong tracking algorithm is proposed by the Zhou DH. The strong tracking algorithm introduces multiple sub-optimal attenuation factors that orthogonalize the remaining sequences to adjust the filter gain and covariance to further obtain an accurate state estimate of the high maneuver target. The strong tracking volume filtering algorithm developed by Zhang HW not only has the advantages of self-adaption and strong tracking algorithm, but also considers the self-adaption current statistical model of maneuvering target tracking. To date, distributed filtering algorithms continue to be a challenging task for intermittent observation tracking of maneuver targets.
Therefore, how to accurately realize intermittent observation tracking of maneuvering targets, and especially how to realize accurate estimation of robust collaborative states, becomes a technical problem to be solved in the field.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a robust collaborative state estimation method and a robust collaborative state estimation system with an event triggering mechanism.
In order to achieve the above object, the present invention provides the following.
A robust collaborative state estimation method with event triggering mechanism, comprising: and introducing an event triggering mechanism to obtain an observation equation of the nonlinear system. The nonlinear system is a sensing network.
And introducing a suboptimal fading factor to adjust an error covariance matrix of the nonlinear system in real time.
And determining a Kalman filtering gain based on the adjusted error covariance matrix.
And filtering the local node based on the Kalman filtering gain and the observation equation to obtain a local filtering result.
And taking the estimated value and the error covariance value in the local filtering result as an information pair, and carrying out information fusion by adopting a consistency algorithm to obtain a state estimated result of the nonlinear system.
Optionally, the event trigger factor of the event trigger mechanism is:。
in the method, in the process of the invention,indicate->Event trigger factor of individual sensor at time k,/->Indicate->Target measurement value of the individual sensors at time k, < >>Indicate->The individual sensors are->Time target measurement,/->The trigger threshold is represented and T represents the transpose.
Optionally, the observation equation of the nonlinear system obtained by introducing the event triggering mechanism is:。
in the method, in the process of the invention,indicate->The individual sensors are->A target measurement of time of day.
Optionally, the suboptimal fading factor is:。
in the method, in the process of the invention,representing the suboptimal fading factor at time k, < ->Residual weakening equation representing time k, +.>Covariance weakening equation representing k time, +.>Representing intermediate values +.>Representation +.>Is a mathematical operation of the trace of (a).
Optionally, the local filtering result is:。
in the method, in the process of the invention,indicate->Target state estimate of individual sensors at time k,/->Indicate->Sample point state prediction value of each sensor at k-1 time,/for each sensor>Representing Kalman filtering gain, < >>Indicate->Posterior observations of the individual sensors at time k,/->Indicate->Intermediate variable of the individual sensors at time k, < >>Indicate->The individual sensors are->Time target measurement,/->Indicate->Error covariance of the target state estimate by each sensor at time k,/>Indicate->Error covariance of target state estimation at time k-1 of each sensor, +.>Representing the error covariance of the target measurement at time k for the ith sensor.
A robust collaborative state estimation system with event triggering mechanism, comprising: a sensor, a memory, and a processor.
And the sensor is used for acquiring the state value and the measured value.
And a memory for storing a computer program.
And the processor is respectively connected with the sensor and the memory, and is used for calling the computer program and executing the computer program based on the state value and the measured value so as to realize state estimation of the nonlinear system. The computer program is used for implementing the robust collaborative state estimation method with the event triggering mechanism. The nonlinear system is a sensing network formed by a plurality of sensors.
Optionally, the processor includes: the system comprises an event triggering mechanism introducing module, a suboptimal fading factor introducing module, a Kalman filtering gain determining module, a local node filtering module and a state estimating module.
The event trigger mechanism introducing module is used for introducing the event trigger mechanism to obtain an observation equation of the nonlinear system.
The suboptimal fading factor introduction module is used for introducing suboptimal fading factors to adjust the error covariance matrix of the nonlinear system in real time.
And the Kalman filtering gain determining module is used for determining Kalman filtering gain based on the adjusted error covariance matrix.
And the local node filtering module is used for filtering the local node based on the Kalman filtering gain and the observation equation to obtain a local filtering result.
And the state estimation module is used for taking the estimated value and the error covariance value in the local filtering result as information pairs, and carrying out information fusion by adopting a consistency algorithm to obtain a state estimation result of the nonlinear system.
Optionally, the memory is a computer readable storage medium.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention introduces an event triggering mechanism, and can reduce the communication burden among the sensors. In the application process, airborne computing resources and communication resources are limited, and when the adjacent two measurement errors reach a trigger threshold value by introducing an event trigger mechanism, the prediction is updated and corrected. Compared with the traditional fixed time interval updating method, the event triggering mechanism can effectively reduce the airborne computing resources of each sensor and the communication resources among nodes, and greatly improves the algorithm efficiency. In addition, aiming at the situation of large maneuvering of the target, suboptimal fading factors are introduced to realize accurate estimation of the large maneuvering target, filtering gain can be adjusted to be strong so that residual sequences are orthogonal, influence of past moment data on a current moment filtering value is reduced, accurate estimation of a target state is realized, influence on filtering during abrupt maneuvering of the target is further controlled, and filtering precision is improved. And finally, fusing the local filtering result by using a consistency algorithm to realize accurate estimation of the state.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an implementation of a robust collaborative state estimation method with an event triggering mechanism according to the present invention.
Fig. 2 is a diagram of a sensor network and a topology relationship provided by the present invention.
FIG. 3 is a schematic diagram of exemplary high-speed macro maneuvering target tracking results provided by the invention.
Fig. 4 is a graph of tracking error in the x-direction provided by the present invention.
Fig. 5 is a graph of tracking error in the y-direction provided by the present invention.
Fig. 6 is a graph of tracking error in the z-direction provided by the present invention.
Fig. 7 is a schematic diagram of a sensor triggering time provided by the present invention.
Fig. 8 is a schematic diagram of algorithm convergence provided in the present invention.
Fig. 9 is a graph showing the root mean square error of a typical high-speed large maneuver target provided by the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a robust collaborative state estimation method and a robust collaborative state estimation system with an event triggering mechanism, which can effectively reduce airborne computing resources of each carrier node and communication resources among nodes, greatly improve state estimation efficiency, well balance weights of state predicted values and measured values, control influences on filtering when a target is in maneuver abrupt change, improve filtering precision and further realize accurate estimation of the target state.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
One nonlinear system (e.g., a sensor network as shown in fig. 2) is:. Wherein,representing the state of the system at time k +.>Representative sensor->And observing the system state at the time k. />Representing a nonlinear state transition equation,/->Observation transfer equation representing system->And->Can be used to determine the number domain +.>To the number domain->Is transferred from the first to the second transfer station. Wherein (1)>Is process noise with a mean value of 0 and a variance of +.>。/>Is observation noise with a mean value of 0 and a variance of +.>，/>And->The number domains with indices m and n, respectively. Based on this, as shown in fig. 1, the robust collaborative state estimation method with event triggering mechanism provided by the present invention includes two stages: a local filtering stage and a consistency fusion stage. The local filtering stage comprises two parts of one-step prediction and measurement updating.
In the practical application process, based on the architecture, the specific implementation flow of the robust collaborative state estimation method with the event triggering mechanism is shown as follows.
Before data processing, the state value and the error covariance value need to be initialized, specifically: firstly, the motion state (state for short) and the estimated error covariance of the target are defined as. The state of the target is information such as real-time position, speed, acceleration and the like in the process of moving the target, and the state can be selected according to actual requirements in simulation and actual application. The error covariance of the target is defined as: />Wherein->An estimated value representing the state of motion of the target, i.e. the state of the target estimated (calculated) by using the proposed algorithm,/->For the true state of the object, +.>Representing a mathematical expectation. The final objective is to make the target estimate approach as close as possible to the target real state value using the proposed algorithm.
First in distributed sensor networkThe estimated values of the targets and the initial values of the covariance matrix of the individual sensors at the initial time (time 0) are defined as follows: />。
Wherein,indicating +.>State estimation of individual sensors to target。/>A value (any value given manually, in order to start the algorithm) initialized at time 0 is indicated. />Indicating +.>Error covariance of individual sensor-to-target state estimates, defined as: mathematical expectations are found for the product of the difference between the target state estimate and the initialization value and the transpose of the difference between the target state estimate and the initialization value.
Assume that the distributed sensor networks are commonIndividual sensors, and individual sensors->Only with neighboring sensors in its communication topology.
Based on the above, an event triggering mechanism is introduced to obtain an observation equation of the nonlinear system. For example, a robust co-tracking process that incorporates event triggering mechanisms to determine high speed, large maneuver targets is described below.
Step one: an event-triggered observation mechanism is introduced.
First, define the event trigger as:。
in the method, in the process of the invention,indicate->Event trigger factor of individual sensor at time k,/->Indicate->The target measurement values of the individual sensors at time k (i.e., information about the targets detected by the sensors, such as the relative distance of the targets detected by the distance sensors, and the relative angle of the targets detected by the radar sensors). />Indicate->The individual sensors are->A target measurement of time of day. />Indicates a trigger threshold value +_>Is a predetermined scalar value that can be used to adjust the frequency of event triggers. T represents the transpose. In the present invention, the->Time represents +.>The moment of last triggering of the individual sensor, which satisfies +.>Meaning that in k-1 moments the last time +.>The time value when the condition is satisfied is defined as +.>. The moment when the sensor event triggers can be as shown in fig. 7.
In summary, the observation equation of the nonlinear system after the event trigger is introduced is as follows:。
in the method, in the process of the invention,indicate->The individual sensors are->A target measurement of time of day.
To this end, the system state equation and the observation equation of the nonlinear system become:
。
wherein,representing a nonlinear state transition equation,/->Represents->System noise at time->Indicate->The target state of the moment.
Step two: the local node is independent of the filtering process.
First, a suboptimal fading factor is introduced to adjust an error covariance matrix of a nonlinear system in real time. Wherein the suboptimal fading factor is:。
in the method, in the process of the invention,representing the suboptimal fading factor at time k, < ->Representing intermediate values +.>Representation +.>Is a mathematical operation of the trace of (a). In linear algebra, a trace (or trace number) of a matrix or equation refers to the sum of the elements on its main diagonal (the diagonal from top left to bottom right, based on the page). />Residual weakening equation representing time k, +.>Covariance weakening equation representing k time, residual weakening equation +.>And covariance weakening equation->The calculation formula of (2) is as follows:。
in the method, in the process of the invention,representing a weakening factor (typically a constant set by man that is greater than 1). R represents the covariance matrix of the observed noise. />The jacobian matrix representing the observation equation can be found by a linear approximation of the observation equation:。/>the observation residual representing time k is defined as: />。/>Observation equation representing time k +.>Represents the state value +.>Is a function of the observation equation of (2).
Wherein,represents a forgetting factor, usually->，/>Represents the residual sequence,/->An initialization value (a scalar value set by man) representing the residual sequence,/or->Residual sequence value representing the k moment, +.>，/>Represents the k moment sensor->Is>Represents the state value at time k>Representing the observation residual at time k-1.
Then, a Kalman filtering gain is determined based on the adjusted error covariance matrix. The Kalman (Kalman) filter gain is:. Wherein (1)>Represents time k>Kalman filtering gain of individual sensors, < ->Represents time k>Covariance matrix of states and observations of individual sensors,/->Represents time k>Covariance matrix of the observations of the individual sensors.
Computing Kalman filter gainIt is necessary to calculate +.>And->，/>And->The calculation process of (2) is as follows.
1) Generating sampling points, namely:. Wherein (1)>Representation->The individual sensors are->Target state sampling value obtained at the moment,/->Indicate->The individual sensors are->A time-of-day target state estimate. />Representation->The individual sensors are->Estimation error covariance of the target state estimate at the time instant. The following is further described: superscript ∈>Is a grouping mark, specifically defined as the +.sup.th generated during sampling>The group samples data, and the mark is only used for representing the data packet, and is eliminated in the subsequent process.
Representing the +.>Column, sampling selection matrix->The method comprises the following steps:。
in the method, in the process of the invention,representing the dimension of the state.
2) Calculating a predicted value of a sampling point, wherein the predicted value is as follows:。
wherein,is->Time->The individual sensor is at->The predicted value of the sampling point of the group is a group of state prediction data obtained after the calculation of the state transition equation (the state prediction data at the moment k of the sensor is the sensor in +.>State prediction data obtained after all sampling points at the moment pass through a state transition equation), which is an intermediate variable. Then, the intermediate variable is used for completing one-step prediction, and a predicted value of the target state is obtained.
3) Generating an observed value, namely:. Wherein (1)>Is->Observations of the individual sensors at time k,representing sensor +.>(i.e.)>Individual sensors).
4) Constructing a pseudo-observation matrix as follows:. Wherein (1)>Is->The individual sensors are->A pseudo-observation matrix of time instants.
Based on the descriptions of the above steps 1) to 4), the calculation expressions of the parameters are obtained as follows:。
wherein,is->The individual sensors are->The posterior observations at time can also be understood as an intermediate variable, for which the covariance matrix in the following two formulae is calculated.
And finally, filtering the local node based on the Kalman filtering gain and the observation equation to obtain a local filtering result. The local filtering result is:。
in the method, in the process of the invention,indicate->Target state estimate of individual sensors at time k,/->Indicate->The sample point state prediction values of the individual sensors at time k-1. />Indicate->The intermediate parameter of each sensor at time k,，/>，/>，，/>and->Is a positive real number. />Representing the observation equation hessian matrix,>。indicate->The individual sensors are->Time target measurement,/->Indicate->Error covariance of the target state estimate by each sensor at time k,/>Indicate->Error covariance of target state estimation at time k-1 of each sensor, +.>Indicate->Error covariance of the target measurement values of the individual sensors at time k,/>Indicate->K+1 time state of the individual sensor->Is a function of the observation equation of (2).
Step three: and (5) data fusion process of each node. In this process, the estimated value and the error covariance value in the local filtering result are taken as information pairsAnd carrying out information fusion by adopting a consistency algorithm to obtain a state estimation result of the nonlinear system.
Specific: first, data is initialized. Let the integration timesAt this time, the estimated value and the error covariance value are initialized to: />. Wherein (1)>Indicate->The estimated value of each sensor at the 0 th fusion time of k time,indicate->Error covariance value of each sensor at 0 th fusion time at k time.
When the times of fusionWhen the method is used, the following steps are executed, and fusion is carried out by using a consistency algorithm.
(i) OrderAs an information pair, transmission is performed between adjacent sensor nodes. Wherein (1)>Represent the firstThe individual sensors are at the time k +.>Estimate at the time of the number of subsegmentations, +.>Indicate->The individual sensors are at the time k +.>Error covariance value at times of subsynthesis.
(ii) And (3) a data fusion process:. Wherein (1)>Indicate->The individual sensors are at the time k +.>Estimate at the time of the number of subsegmentations, +.>Indicate->The individual sensors are at the time k +.>Error covariance value at times of subsynthesis, < ->Indicate->The transmission parameters of the individual sensors, +.>Indicate->Individual sensors and->Transmission parameters between individual sensors, +.>And the number of the sensors in the sensor network is represented.
(iii) Repeating steps (i) - (ii) untilThe final fusion result is obtained and output as follows:。
based on the above description, in practical application, the on-board computing resource of each sensor and the communication resource between the sensors are limited, and the conventional fixed time interval triggering mechanism causes a lot of unnecessary resource expenditure. On the one hand, in a complex and high-uncertainty environment, the on-board resource can be saved to provide necessary support for long-endurance and high-reliability work of the carrier. On the other hand, less resource loss can effectively reduce the thermal effect of the carrier, thereby reducing the probability of being detected by enemy and enhancing the survivability. Therefore, the robust collaborative state estimation method with the event triggering mechanism provided by the invention can greatly enhance the robustness of a distributed system. By adding an event trigger controller in the filtering updating stage, when the difference value of two adjacent measurement values reaches a certain preset threshold value, filtering updating is carried out, and when the difference value is smaller than the threshold value, the moment measurement is regarded as invalid measurement, so that the calculation and communication burden is greatly reduced, and the system robustness is enhanced. In addition, aiming at the abrupt change condition in the target motion process, the filter gain can be adjusted to be strong so as to lead the residual sequences to be orthogonal by introducing the suboptimal fading factor, and the influence of the past moment data on the current moment filter value is lightened, so that the accurate estimation of the target state is realized. When a bad observation value appears in the system, an adaptive factor can be introduced to well balance the weights of the state predicted value and the measured value, so that the influence on filtering when the target maneuver changes suddenly is controlled, and the filtering precision is improved. Wherein, the tracking result and tracking error of a typical high-speed large maneuvering target are shown in fig. 3 to 6. For tracking results, the industry commonly uses minimum mean square error as a measure, and the RMSE result of the method provided by the invention for high-speed large maneuvering targets is shown in fig. 9. The convergence of this method provided by the present invention is shown in fig. 8.
Furthermore, the invention also provides a robust collaborative state estimation system with an event triggering mechanism. The system comprises: a sensor, a memory, and a processor.
The sensor is used to obtain status and measurement values.
The memory is used for storing a computer program.
And the processor is respectively connected with the sensor and the memory, and is used for calling the computer program and executing the computer program based on the state value and the measured value so as to realize the state estimation of the nonlinear system. The computer program is used for implementing the robust collaborative state estimation method with the event triggering mechanism. The nonlinear system is a sensing network formed by a plurality of sensors.
Wherein the processor comprises: the event trigger mechanism introducing module is used for introducing the event trigger mechanism to obtain an observation equation of the nonlinear system.
The suboptimal fading factor introduction module is used for introducing suboptimal fading factors to adjust an error covariance matrix of the nonlinear system in real time.
The Kalman filter gain determination module is used for determining Kalman filter gain based on the adjusted error covariance matrix.
The local node filtering module is used for filtering the local node based on the Kalman filtering gain and the observation equation to obtain a local filtering result.
The state estimation module is used for taking the estimated value and the error covariance value in the local filtering result as an information pair, and carrying out information fusion by adopting a consistency algorithm to obtain a state estimation result of the nonlinear system.
Furthermore, the computer program in the above-described memory may be stored in a computer-readable storage medium when it is implemented in the form of a software functional unit and sold or used as a separate product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. A method for robust collaborative state estimation with event triggering mechanism, comprising:
introducing an event triggering mechanism to obtain an observation equation of a nonlinear system; the nonlinear system is a sensing network;
introducing a suboptimal fading factor to adjust an error covariance matrix of the nonlinear system in real time;
determining a Kalman filtering gain based on the adjusted error covariance matrix;
filtering the local node based on the Kalman filtering gain and the observation equation to obtain a local filtering result;
and taking the estimated value and the error covariance value in the local filtering result as an information pair, and carrying out information fusion by adopting a consistency algorithm to obtain a state estimated result of the nonlinear system.
2. The method for robust collaborative state estimation with event triggering mechanism according to claim 1, wherein the event triggering factor of the event triggering mechanism is:
；
in the method, in the process of the invention,indicate->Event trigger factor of individual sensor at time k,/->Indicate->Target measurement value of the individual sensors at time k, < >>Indicate->The individual sensors are->Time target measurement,/->The trigger threshold is represented and T represents the transpose.
3. The method for estimating a robust collaborative state with an event triggering mechanism according to claim 2, wherein the observation equation of a nonlinear system obtained by introducing the event triggering mechanism is:
；
in the method, in the process of the invention,indicate->The individual sensors are->A target measurement of time of day.
4. The method for robust collaborative state estimation with event triggering mechanism according to claim 1 wherein the suboptimal fading factors are:
；
in the method, in the process of the invention,representing the suboptimal fading factor at time k, < ->Residual weakening equation representing time k, +.>Covariance weakening equation representing k time, +.>Representing intermediate values +.>Representation +.>Is a mathematical operation of the trace of (a).
5. The robust collaborative state estimation method with event triggering mechanism according to claim 2, wherein the local filtering results are:
；
in the method, in the process of the invention,indicate->Target state estimate of individual sensors at time k,/->Indicate->Sample point state prediction value of each sensor at k-1 time,/for each sensor>Representing Kalman filtering gain, < >>Indicate->Posterior observations of the individual sensors at time k,/->Indicate->Intermediate variable of the individual sensors at time k, < >>Indicate->The individual sensors are->Time target measurement,/->Indicate->Error covariance of the target state estimate by each sensor at time k,/>Indicate->Error covariance of target state estimation at time k-1 of each sensor, +.>Representing the error covariance of the target measurement at time k for the ith sensor.
6. A robust collaborative state estimation system with event triggering mechanism, comprising:
the sensor is used for acquiring a state value and a measured value;
a memory for storing a computer program;
the processor is respectively connected with the sensor and the memory, and is used for calling the computer program and executing the computer program based on the state value and the measured value so as to realize state estimation of a nonlinear system; the computer program is configured to implement the robust collaborative state estimation method with event triggering mechanism of any of claims 1-5; the nonlinear system is a sensing network formed by a plurality of sensors.
7. The robust collaborative state estimation system with event triggering mechanism according to claim 6, wherein the processor comprises:
the event trigger mechanism introducing module is used for introducing an event trigger mechanism to obtain an observation equation of the nonlinear system;
the suboptimal fading factor introduction module is used for introducing suboptimal fading factors to adjust an error covariance matrix of the nonlinear system in real time;
the Kalman filtering gain determining module is used for determining Kalman filtering gain based on the adjusted error covariance matrix;
the local node filtering module is used for filtering the local node based on the Kalman filtering gain and the observation equation to obtain a local filtering result;
and the state estimation module is used for taking the estimated value and the error covariance value in the local filtering result as information pairs, and carrying out information fusion by adopting a consistency algorithm to obtain a state estimation result of the nonlinear system.
8. The robust collaborative state estimation system with event triggering mechanism according to claim 6, wherein the memory is a computer readable storage medium.
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