CN114236480A - Airborne platform sensor system error registration algorithm - Google Patents

Airborne platform sensor system error registration algorithm Download PDF

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CN114236480A
CN114236480A CN202111358235.4A CN202111358235A CN114236480A CN 114236480 A CN114236480 A CN 114236480A CN 202111358235 A CN202111358235 A CN 202111358235A CN 114236480 A CN114236480 A CN 114236480A
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sensor system
system error
airborne platform
registration algorithm
noise
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程然
张存
张敏
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Leihua Electronic Technology Research Institute Aviation Industry Corp of China
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    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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    • G01S7/4004—Means for monitoring or calibrating of parts of a radar system
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Abstract

The application provides an airborne platform sensor system error registration algorithm, belongs to the technical field of information fusion and target tracking, and specifically comprises: step 1, performing mathematical modeling aiming at a process of performing fusion tracking on target information by a dual-computer platform; step 2, performing UKF algorithm processing on the mathematical model in the step 1 when the noise mean value is nonzero; step 3, carrying out suboptimal unbiased MAP constant noise statistical algorithm processing on the unknown noise statistical characteristics on the basis of the step 2; and 4, obtaining parameter estimation of the sensor system error in a mode of repeatedly iterating the steps 1-3 through analysis and updating. Through the processing scheme, the target tracking precision is improved, and meanwhile, the reliability and stability of target tracking are improved.

Description

Airborne platform sensor system error registration algorithm
Technical Field
The application relates to the field of information fusion and target tracking, in particular to an airborne platform sensor system error registration algorithm.
Background
The airborne multi-platform multi-sensor information fusion technology can fully utilize the characteristics of various data to acquire complementary data of different visual angles, different sensors and different characteristics, greatly expands the coverage area of a detection area, improves the information utilization efficiency, and accelerates the data updating rate, thereby being capable of remarkably improving the tracking capability of airplane formation on targets, providing faster track initial speed, shorter track convergence time, more accurate track tracking precision and more stable track continuity.
The traditional nonlinear filter requires the prior statistical characteristic of accurately known noise, and in practical application, the prior statistical characteristic of the noise is not known or accurate due to the limitation of various aspects such as a test sample, or the prior statistical characteristic of the noise is accurately known, but the system is in an actual operation environment and is influenced by internal and external uncertain factors, the noise statistical characteristic is easy to change, and the characteristic of strong time variation is achieved. Unfortunately, the conventional non-linear filter has no adaptive capability to cope with the variation of the noise statistical characteristic, and is prone to the reduction and even divergence of the filtering accuracy under the condition that the noise statistics are unknown and time-varying, which is the limitation of the conventional non-linear filter.
Disclosure of Invention
In view of this, the application provides an airborne platform sensor system error registration algorithm, which solves the problems in the prior art, improves the tracking precision of a target, and simultaneously improves the reliability and stability of target tracking.
The airborne platform sensor system error registration algorithm provided by the application adopts the following technical scheme:
an airborne platform sensor system error registration algorithm comprising:
step 1, performing mathematical modeling aiming at a process of performing fusion tracking on target information by a dual-computer platform;
step 2, performing UKF algorithm processing on the mathematical model in the step 1 when the noise mean value is nonzero;
step 3, carrying out suboptimal unbiased MAP constant noise statistical algorithm processing on the unknown noise statistical characteristics on the basis of the step 2;
and 4, obtaining parameter estimation of the sensor system error in a mode of repeatedly iterating the steps 1-3 through analysis and updating.
Optionally, step 1 includes: consider a discrete nonlinear system in the form of a state space:
Figure BDA0003358091270000021
xk∈Rnand zk∈RmRespectively representing the state vector and the measurement vector at time k, n and m respectively representing the state dimension and the measurement dimension, fk-1(. and h)k(. represents the state transfer function and the measurement function of the system, respectively, wk∈RnAnd vk∈RmRespectively representing the process noise vector and the measurement noise vector of the system, and respectively obeying that the mean value is zero and the variance is QkAnd RkAre not related to each other, initial state vector x0Obey mean value of
Figure BDA0003358091270000031
Variance is P0Is in a Gaussian distribution of and with wkAnd vkAnd q represents an unknown process noise mean value, and r represents an unknown measurement system error vector.
Optionally, step 2 includes:
according to UT transform Sigma point deterministic sampling strategy based on
Figure BDA0003358091270000032
And Pk-1To construct the Sigma point ξi,k-1I is 0,1, L,2n, which is passed through a nonlinear state function fk-1(. o) spread as gammai,k|k-1From γi,k|k-1Available state prediction mean
Figure BDA0003358091270000033
And prediction error covariance matrix Pk|k-1：
Figure BDA0003358091270000034
According to UT transform Sigma point deterministic sampling strategy based on
Figure BDA0003358091270000035
And Pk|k-1To construct the Sigma point ξi,k|k-1I is 0,1, L,2n, which is measured by a non-linear measurement function hk(.) + r spread as chii,k|k-1From chii,k|k-1The measured predicted mean value can be obtained
Figure BDA0003358091270000036
And auto-covariance matrix
Figure BDA0003358091270000037
Sum cross covariance matrix
Figure BDA0003358091270000038
Figure BDA0003358091270000041
After obtaining a new measurement zkAnd then, carrying out filtering updating:
Figure BDA0003358091270000042
wherein, KkIs the filter gain matrix.
Optionally, step 3 includes:
based on the principle of maximum posterior estimation and the measured value zkThe calculation formula of the suboptimal MAP constant noise statistic estimator is as follows:
Figure BDA0003358091270000043
Figure BDA0003358091270000044
optionally, the step 4 includes:
Figure BDA0003358091270000051
Figure BDA0003358091270000052
Figure BDA0003358091270000053
Figure BDA0003358091270000054
Figure BDA0003358091270000055
Figure BDA0003358091270000056
Figure BDA0003358091270000057
Figure BDA0003358091270000058
optionally, the method further includes step 5, verifying the algorithm by constructing a simulation scene in which the dual-computer platform performs fusion tracking on the synthetic target.
Optionally, the setting method of the simulation scene includes: the distance between the carriers is 20km, the distance between the target and the carriers is 220km, the speed of the carriers is 300m/s, the target speed is 300m/s, the radar data rate is 0.5s, and the system error of each radar is set to be eta which is [40m,0.3 degrees ] and 0.3 degrees °]T。
Optionally, the target point track and the flight track generated by simulation are both realized by simulation software MATLAB.
Optionally, the simulation flow time is set to 1000 radar frames.
To sum up, the application comprises the following beneficial technical effects:
1. the application discloses an unbiased MAP constant value noise statistic estimator, and a self-adaptive nonlinear filter is constructed by combining the noise statistic estimator with a traditional nonlinear filter. The adaptive nonlinear filter can estimate and correct the mean value and covariance of noise in real time according to measurement information, has adaptive capacity for coping with noise change, and solves the problem that a traditional online joint estimation method needs an accurate known system error state space model;
2. the method obtains the parameter estimation of the system error of the sensor by analyzing, updating and iterating repeatedly, does not need to calculate a Jacobian matrix, has strong numerical stability in recursive operation, solves the problem that the approximation precision of the traditional nonlinear Gaussian filter to the posterior probability density function of the system state is not high under the conditions of high measurement precision, high system dimension and complex strong nonlinearity, improves the estimation precision of the system error and the tracking precision of a target, not only effectively realizes the system error registration of the airborne platform sensor, but also improves the reliability and the stability of target tracking.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used 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 application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart of an airborne platform sensor system error registration algorithm of the present application;
FIG. 2 is a longitude and latitude map of the simulation environment of the present application;
FIG. 3 is a block diagram of the present application distance system error estimation;
FIG. 4 is a schematic diagram of the orientation system error estimation of the present application;
fig. 5 shows the pitch system error estimation of the present application.
Detailed Description
The embodiments of the present application will be described in detail below with reference to the accompanying drawings.
The following description of the embodiments of the present application is provided by way of specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure herein. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. The present application is capable of other and different embodiments and its several details are capable of modifications and/or changes in various respects, all without departing from the spirit of the present application. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the present application, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present application, and the drawings only show the components related to the present application rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
The embodiment of the application provides an airborne platform sensor system error registration algorithm.
As shown in fig. 1, an airborne platform sensor system error registration algorithm is characterized by comprising:
step 1, performing mathematical modeling aiming at the process of performing fusion tracking on target information by a dual-computer platform.
The step 1 specifically comprises the following steps: the maneuvering radar system with unknown system error is mathematically modeled, taking into account a discrete nonlinear system in the form of a state space: modeling the measurement characteristics of the motion state:
Figure BDA0003358091270000091
wherein x isk∈RnAnd zk∈RmRespectively representing the state vector and the measurement vector at time k, n and m respectively representing the state dimension and the measurement dimension, fk-1(. and h)k(. represents the state transfer function and the measurement function of the system, respectively, wk∈RnAnd vk∈RmRespectively representing the process noise vector and the measurement noise vector of the system, and respectively obeying that the mean value is zero and the variance is QkAnd RkAre not related to each other, initial state vector x0Obey mean value of
Figure BDA0003358091270000092
Variance is P0Is in a Gaussian distribution of and with wkAnd vkAnd q represents an unknown process noise mean value, and r represents an unknown measurement system error vector.
And 2, performing UKF algorithm processing on the mathematical model in the step 1 when the noise mean value is nonzero.
The step 2 specifically comprises the following steps: and (3) realizing a UKF algorithm when the noise mean value is nonzero, and processing the model to determine q and r. The UKF is short for the Unscented Kalman Filter, and the Chinese definition is lossless Kalman filtering, Unscented Kalman filtering or dearomatized Kalman filtering.
According to a UT transform Sigma point deterministic sampling strategy, the UT is an unknown Transformation short; based on
Figure BDA0003358091270000093
And Pk-1To construct the Sigma point ξi,k-1I is 0,1, L,2n, which is passed through a nonlinear state function fk-1(. o) spread as gammai,k|k-1From γi,k|k-1Available state prediction mean
Figure BDA0003358091270000094
And prediction error covariance matrix Pk|k-1：
Figure BDA0003358091270000101
According to UT transform Sigma point deterministic sampling strategy based on
Figure BDA0003358091270000102
And Pk|k-1To construct the Sigma point ξi,k|k-1I is 0,1, L,2n, which is measured by a non-linear measurement function hk(.) + r spread as chii,k|k-1From chii,k|k-1The measured predicted mean value can be obtained
Figure BDA0003358091270000103
And auto-covariance matrix
Figure BDA0003358091270000104
Sum cross covariance matrix
Figure BDA0003358091270000105
Figure BDA0003358091270000106
After obtaining a new measurement zkAnd then, carrying out filtering updating:
Figure BDA0003358091270000107
wherein, KkIs the filter gain matrix.
And 3, performing suboptimal unbiased MAP constant noise statistical algorithm processing on the unknown noise statistical characteristics on the basis of the step 2. MAP is short for Maximum a poster.
The step 3 specifically comprises the following steps: based on the principle of maximum posterior estimation and the measured value zkThe calculation formula of the suboptimal MAP constant noise statistic estimator is as follows:
Figure BDA0003358091270000111
Figure BDA0003358091270000112
and 4, obtaining parameter estimation of the sensor system error in a mode of repeatedly iterating the steps 1-3 through analysis and updating.
The step 4 specifically comprises the following steps:
Figure BDA0003358091270000113
Figure BDA0003358091270000114
Figure BDA0003358091270000115
Figure BDA0003358091270000116
Figure BDA0003358091270000117
Figure BDA0003358091270000118
Figure BDA0003358091270000121
Figure BDA0003358091270000122
the application discloses an unbiased MAP constant value noise statistic estimator, and a self-adaptive nonlinear filter is constructed by combining the noise statistic estimator with a traditional nonlinear filter. The adaptive nonlinear filter can estimate and correct the mean value and covariance of noise in real time according to measurement information, has adaptive capacity for responding to noise change, and solves the problem that a traditional online joint estimation method needs an accurate known system error state space model. Secondly, the method provided by the application obtains the parameter estimation of the system error of the sensor through a mode of analysis, updating and repeated iteration, does not need to calculate a Jacobian matrix, has strong numerical stability in recursive operation, solves the problem that the approximation precision of a posterior probability density function of a system state is not high under the conditions of high measurement precision, high system dimension and complex strong nonlinearity of a traditional nonlinear Gaussian filter, improves the estimation precision of the system error and the tracking precision of a target, not only effectively realizes the system error registration of an airborne platform sensor, but also improves the reliability and stability of target tracking.
The error registration algorithm of the airborne platform sensor system based on the noise statistic estimator disclosed by the application is a difficult problem to be solved urgently in the field of information fusion by multi-machine cooperative positioning and tracking. The method has good expansibility and adaptability, and can be widely applied to a multi-platform active/passive radar information fusion tracking system with higher requirements on stable target tracking.
Step 5 may also be included, as shown in fig. 2, verifying the algorithm by constructing a simulation scenario in which a dual-machine platform performs fusion tracking on the synthetic target. The setting method of the simulation scene comprises the following steps: the distance between the carriers is 20km, the distance between the target and the carriers is 220km, the speed of the carriers is 300m/s, the target speed is 300m/s, the radar data rate is 0.5s, and the system error of each radar is set to be eta which is [40m,0.3 degrees ] and 0.3 degrees °]T. The simulation flow time is set to 1000 radar frames.
And the target point track and the flight track generated by simulation are realized by MATLAB simulation software.
The results of fig. 2 to 4 and table 1 were obtained.
TABLE 1 systematic error estimation accuracy
Figure BDA0003358091270000131
As can be seen from fig. 2 to 4 and table 1, the estimation accuracy of the airborne platform sensor system error registration algorithm based on the noise statistics estimator to each system error basically reaches more than 95%, and algorithm convergence can be realized. Therefore, the airborne platform sensor system error registration algorithm based on the noise statistical estimator has a good estimation effect on the airborne radar system error, and the problem of system error registration of the airborne radar is well solved.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (9)

1. An airborne platform sensor system error registration algorithm, comprising:
step 1, performing mathematical modeling aiming at a process of performing fusion tracking on target information by a dual-computer platform;
step 2, performing UKF algorithm processing on the mathematical model in the step 1 when the noise mean value is nonzero;
step 3, carrying out suboptimal unbiased MAP constant noise statistical algorithm processing on the unknown noise statistical characteristics on the basis of the step 2;
and 4, obtaining parameter estimation of the sensor system error in a mode of repeatedly iterating the steps 1-3 through analysis and updating.
2. The airborne platform sensor system error registration algorithm of claim 1, wherein said step 1 comprises: consider a discrete nonlinear system in the form of a state space:
Figure FDA0003358091260000011
xk∈Rnand zk∈RmRespectively representing the state vector and the measurement vector at time k, n and m respectively representing the state dimension and the measurement dimension, fk-1(. and h)k(. represents the state transfer function and the measurement function of the system, respectively, wk∈RnAnd vk∈RmRespectively representing the process noise vector and the measurement noise vector of the system, and respectively obeying that the mean value is zero and the variance is QkAnd RkAre not related to each other, initial state vector x0Obey mean value of
Figure FDA0003358091260000012
Variance is P0Is in a Gaussian distribution of and with wkAnd vkAnd q represents an unknown process noise mean value, and r represents an unknown measurement system error vector.
3. The airborne platform sensor system error registration algorithm of claim 1, wherein said step 2 comprises:
according to UT transform Sigma point deterministic sampling strategy based on
Figure FDA0003358091260000021
And Pk-1To construct the Sigma point ξi,k-1I is 0,1, L,2n, which is passed through a nonlinear state function fk-1(. o) spread as gammai,k|k-1From γi,k|k-1Available state prediction mean
Figure FDA0003358091260000022
And prediction error covariance matrix Pk|k-1：
Figure FDA0003358091260000023
According to UT transform Sigma point deterministic sampling strategy based on
Figure FDA0003358091260000024
And Pk|k-1To construct the Sigma point ξi,k|k-1I is 0,1, L,2n, which is measured by a non-linear measurement function hk(.) + r spread as chii,k|k-1From chii,k|k-1The measured predicted mean value can be obtained
Figure FDA0003358091260000025
And auto-covariance matrix
Figure FDA0003358091260000026
Sum cross covariance matrix
Figure FDA0003358091260000027
Figure FDA0003358091260000028
After obtaining a new measurement zkThen, filtering is carried outWave updating:
Figure FDA0003358091260000031
wherein, KkIs the filter gain matrix.
4. The airborne platform sensor system error registration algorithm of claim 1, wherein said step 3 comprises:
based on the principle of maximum posterior estimation and the measured value zkThe calculation formula of the suboptimal MAP constant noise statistic estimator is as follows:
Figure FDA0003358091260000032
Figure FDA0003358091260000033
5. the airborne platform sensor system error registration algorithm of claim 1, wherein said step 4 comprises:
Figure FDA0003358091260000034
Figure FDA0003358091260000035
Figure FDA0003358091260000041
Figure FDA0003358091260000042
Figure FDA0003358091260000043
Figure FDA0003358091260000044
Figure FDA0003358091260000045
Figure FDA0003358091260000046
6. the error registration algorithm of the airborne platform sensor system according to any of claims 1-5, further comprising a step 5 of verifying the algorithm by constructing a simulation scenario in which the dual-machine platform performs fusion tracking on a synthetic target.
7. The error registration algorithm of the airborne platform sensor system according to claim 6, wherein the setting method of the simulation scenario comprises: the distance between the carriers is 20km, the distance between the target and the carriers is 220km, the speed of the carriers is 300m/s, the target speed is 300m/s, the radar data rate is 0.5s, and the system error of each radar is set to be eta which is [40m,0.3 degrees ] and 0.3 degrees °]T。
8. The airborne platform sensor system error registration algorithm of claim 6, wherein the target point trace and the flight trace generated by simulation are both implemented by simulation software MATLAB.
9. The airborne platform sensor system error registration algorithm of claim 6, wherein the simulation flow time is set to 1000 radar frames.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114963879A (en) * 2022-05-20 2022-08-30 中国电子科技集团公司电子科学研究院 Unmanned aerial vehicle comprehensive countering system and comprehensive countering method
CN116520281A (en) * 2023-05-11 2023-08-01 兰州理工大学 DDPG-based extended target tracking optimization method and device

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114963879A (en) * 2022-05-20 2022-08-30 中国电子科技集团公司电子科学研究院 Unmanned aerial vehicle comprehensive countering system and comprehensive countering method
CN114963879B (en) * 2022-05-20 2023-11-17 中国电子科技集团公司电子科学研究院 Comprehensive control system and method for unmanned aerial vehicle
CN116520281A (en) * 2023-05-11 2023-08-01 兰州理工大学 DDPG-based extended target tracking optimization method and device
CN116520281B (en) * 2023-05-11 2023-10-24 兰州理工大学 DDPG-based extended target tracking optimization method and device

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