CN113790727A - Pulse maneuver detection method based on auxiliary state parameters - Google Patents

Pulse maneuver detection method based on auxiliary state parameters Download PDF

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CN113790727A
CN113790727A CN202111043910.4A CN202111043910A CN113790727A CN 113790727 A CN113790727 A CN 113790727A CN 202111043910 A CN202111043910 A CN 202111043910A CN 113790727 A CN113790727 A CN 113790727A
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state
auxiliary
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value
parameters
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黄普
淡鹏
汪彬
王奥
张重阳
李海玥
张杨
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China Xian Satellite Control Center
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation

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Abstract

The invention discloses a pulse maneuvering detection method based on auxiliary state parameters, which comprises the specific steps of 1, establishing a maneuvering dynamics model of a target under a geocentric J2000 inertial system, and adding auxiliary parameters in a state vector; step 2, performing tasteless Kalman filtering according to the maneuvering dynamics model established in the step 1, and estimating maneuvering detection auxiliary parameters; and 3, judging the maneuvering of the detection target according to the characteristics of the auxiliary variables under the condition of obtaining the auxiliary parameter estimation result in the step 2. Aiming at the problem that proper auxiliary parameters are difficult to design in the traditional method, the invention combines the auxiliary parameters before and after the pulse maneuver, obvious pulse changes can occur, and based on the changes, the probability of the maneuver of the target is calculated, and finally whether the target is maneuvered or not is determined, so that the target maneuver can be effectively detected.

Description

Pulse maneuver detection method based on auxiliary state parameters
Technical Field
The invention belongs to the technical field of a maneuvering orbit of a space vehicle, and particularly relates to a pulse maneuvering detection method based on auxiliary state parameters.
Background
Maneuver detection is the core problem of maneuvering target tracking, and the calculation idea of maneuvering detection in the prior art is as follows: firstly, establishing a target dynamic model, then designing a filter, receiving measurement data, analyzing the change of specified information in the filtering process, and judging whether the target is mobile or not according to a corresponding detection criterion. Wherein, the specified information and the detection criterion are important factors of the quality of the target maneuvering detection method. Therefore, the scholars at home and abroad research and analyze the two aspects from different angles, and the designated information aspect is mainly filtering residual information or extended state information; the filtering residual innovation means that after a target is maneuvered, an original dynamic model is not matched any more, and the maneuvering detection of the target can be completed through the drastic change of the filtering residual (also called innovation) in the filtering process, the maneuvering detection process of the method is more intuitive, however, the filtering residual does not undergo mathematical treatment, so that more false alarms and decision-making difficulties are brought, and therefore relevant scholars perform corresponding statistical treatment on the residual, such as obtaining normalized residual square sum, residual cumulative sum and the like, so as to obtain better detection effect; the method has the advantages that the maneuvering detection accuracy is higher than that of a filtering residual innovation method, the state characteristics of the target need to be analyzed, the target maneuvering can be accurately detected only through proper auxiliary parameters, and the auxiliary parameters are difficult to design.
Disclosure of Invention
The invention aims to provide a pulse maneuver detection method based on auxiliary state parameters, which solves the problem that the auxiliary parameters are difficult to design in the traditional target maneuver detection method.
The invention adopts the technical scheme that a pulse maneuver detection method based on auxiliary state parameters is characterized by comprising the following steps of:
step 1, establishing a maneuvering dynamics model of a target under a geocentric J2000 inertial system, and adding auxiliary parameters in a state vector;
step 2, performing tasteless Kalman filtering according to the maneuvering dynamics model established in the step 1, and estimating maneuvering detection auxiliary parameters;
and 3, judging the maneuvering of the detection target according to the characteristics of the auxiliary variables under the condition of obtaining the auxiliary parameter estimation result in the step 2.
Yet another feature of the present invention is that,
in the step 1, auxiliary parameters are introduced into the dynamic model on the basis of position, speed and acceleration state quantity, so that first-order model establishment of acceleration is completed.
Step 1, the first-order model building process of the acceleration is as follows:
step 1.1, setting the maneuvering target state vector as [ r, v, a, b ═ X](ii) a r is the target position, r ═ x, y, z)TAnd v is the target speed, v is,
Figure BDA0003250441770000021
a is the target acceleration, b is the auxiliary parameter,
Figure BDA0003250441770000022
Figure BDA0003250441770000023
mass consumed per second, m is the target mass;
the auxiliary parameters are derived to obtain:
Figure BDA0003250441770000024
considering the target specific impulse as
Figure BDA0003250441770000025
When F is constant, the specific impulse Isp is also constant, i.e.
Figure BDA0003250441770000026
Establishing a differential model b' ═ b of auxiliary parameters2；
Step 1.2, establishing a target maneuvering dynamics model:
Figure BDA0003250441770000031
in the formula (1), the reaction mixture is,
Figure BDA0003250441770000032
gris the component of gravitational acceleration in the radial direction of the earth's center,
Figure BDA0003250441770000033
gwis the component of gravitational acceleration on the earth's axis of rotation vector, where J2＝1.08263×10-3Is a second order harmonic coefficient; mu-3.986005X 1014m3/s2Is the constant of the earth's gravity; re6378140.0m is the equator radius of the earth; r is the modulus of the target position vector, z is the value in the z direction of the target position vector,
Figure BDA0003250441770000034
is ECI celestial North Pole; p is a thrust direction vector.
The method for completing state estimation by adopting the unscented Kalman filtering UKF algorithm comprises the following specific steps:
step 2.1, determining state initialization by adopting a cooperative target UKF filtering track;
initial value of state variable filtering:
Figure BDA0003250441770000035
wherein r and
Figure BDA0003250441770000036
is the initial position and velocity, a is the initial acceleration, b is the initial auxiliary variable, the default is 0; initial variance matrix: p0Diag (1.0e4,1.0e4,1.0e4,10,10,1, 0.0002), diag is diagonal matrix operation; process noise covariance matrix: q ═ diag (0,0,0,1.0e-5,1.0e-5,1.0e-5, 0, 0); observing a noise covariance matrix: r ═ diag (100,0.05,0.05, 1);
2.2, performing tasteless transformation, calculating sampling points, and calculating corresponding sampling points and weight values of the initial value of the filtering state according to the formulas (2) and (3), wherein the number of the sampling points is 2n +1, n is the number of state variables, the number of the sampling points is 8, and the number of the sampling points is 17;
Figure BDA0003250441770000041
in the formula (2), the reaction mixture is,
Figure BDA0003250441770000042
is (n + kappa) PxxIth row or column of the square root matrix of (1);
the corresponding weight value is as follows:
Figure BDA0003250441770000043
wherein, ω isiThe weight value of the ith Sigma point is; kappa is a proportional parameter and is used for adjusting the distance between a Sigma point and x and only influencing the deviation brought by a high-order moment after the second order; a scaling factor with alpha being a positive value controls the subsection range of the Sigma point, usually a smaller value of (0, 1) is taken, and beta is a parameter for introducing f (-) high-order item information; in the present invention, κ ═ 5, α ═ 0.5, and β ═ 2.
Step 2.3, predicting the state;
obtaining a one-step prediction state value and a state variance matrix of the state vector through sampling point weighting processing, and meanwhile, calculating a one-step prediction observation value according to the one-step prediction state value;
Figure BDA0003250441770000044
in the formula, f (-) is a state equation, and H (-) is an observation equation;
step 2.4, measurement and prediction are carried out, and a one-step prediction variance matrix and a state observation covariance matrix according to the observed values are obtained through sampling point weighting processing;
Figure BDA0003250441770000051
step 2.5, gain calculation, namely calculating the filtering gain of the unscented Kalman filter according to the one-step prediction variance matrix and the state observation covariance matrix;
Figure BDA0003250441770000052
step 2.6, updating the state, and calculating a state value and a state variance matrix at the next moment according to the calculation result of the step;
Figure BDA0003250441770000053
the specific process of the step 3 is as follows: performing hypothesis testing according to the estimation result of the odorless Kalman filtering in the step 2; firstly, recording the mean value and variance of auxiliary parameters after the filter is stabilized, taking the filtering residual as a mark for filtering stabilization, and when the filtering residual is continuously in P for 180 secondsYYWithin 3 times of a threshold value of a diagonal line of (k +1, k), the filtering enters a stable state at the moment; secondly, calculating the distribution probability of the auxiliary variables in normal distribution, wherein the mean value of the normal distribution is the mean value of the auxiliary parameters, the variance is the variance of the auxiliary parameters, and the calculation result is pdf (b); finally, judging whether the target is mobile or not according to the value of 1-pdf (b), and constructing a hypothesis function; when H0: a value of 1-pdf (b) greater than 95% indicates maneuver; when H1: a value of 1-pdf (b) of less than 95% indicates no maneuver.
The method has the advantages that aiming at the problem that proper auxiliary parameters are difficult to design in the traditional method, an auxiliary parameter is extracted according to the characteristic of target pulse maneuver and the relation existing between target specific impulse and acceleration change before and after the pulse maneuver, the auxiliary parameter is closely related to the target maneuver acceleration change, particularly obvious pulse change occurs before and after the pulse maneuver, the probability of the target maneuver is calculated on the basis of the change, and whether the target is maneuvered or not is finally determined. The method is simple and clear, can effectively detect the target maneuvering, greatly improves the efficiency, and meets the requirement of rapid planning and calculation; has wide applicability.
Drawings
FIG. 1 is a graph of altitude change over the course of an aircraft maneuver of the present invention;
FIG. 2 is a three-dimensional ballistic graph of the overall maneuver of the aircraft of the present invention;
FIG. 3 is a graphical representation of the maneuver control acceleration profile of the aircraft of the present invention;
FIG. 4 is a graph of a change in an auxiliary parameter of the aircraft of the present invention;
FIG. 5 is a graph of the change in maneuver probability for an aircraft according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention discloses a pulse maneuver detection method based on auxiliary state parameters, which comprises the following steps of:
step 1, establishing a maneuvering dynamics model of a target under a geocentric J2000 inertial system, and adding auxiliary parameters in a state vector;
the maneuvering dynamics model introduces auxiliary parameters on the basis of position, speed and acceleration state quantity to complete the first-order model building of the acceleration, and the first-order model building process of the acceleration is as follows:
step 1.1, setting the maneuvering target state vector as [ r, v, a, b ═ X](ii) a r is the target position, r ═ x, y, z)TAnd v is the target speed, v is,
Figure BDA0003250441770000061
a is the target acceleration, b is the auxiliary parameter,
Figure BDA0003250441770000062
Figure BDA0003250441770000063
for mass consumed per second, m is the targetQuality;
the auxiliary parameters are derived to obtain:
Figure BDA0003250441770000064
considering the target specific impulse as
Figure BDA0003250441770000071
When F is constant, the specific impulse Isp is also constant, i.e.
Figure BDA0003250441770000072
Establishing a differential model b' ═ b of auxiliary parameters2The estimation accuracy of the auxiliary parameters is improved;
step 1.2, establishing a target maneuvering dynamics model:
Figure BDA0003250441770000073
in the formula (1), the reaction mixture is,
Figure BDA0003250441770000074
gris the component of gravitational acceleration in the radial direction of the earth's center,
Figure BDA0003250441770000075
gwis the component of gravitational acceleration on the earth's axis of rotation vector, where J2＝1.08263×10-3Is a second order harmonic coefficient; mu-3.986005X 1014m3/s2Is the constant of the earth's gravity; re6378140.0m is the equator radius of the earth; r is the modulus of the target position vector, z is the value in the z direction of the target position vector,
Figure BDA0003250441770000076
is ECI celestial North Pole; p is a thrust direction vector.
Step 2, performing tasteless Kalman filtering according to the maneuvering dynamics model established in the step 1, and estimating maneuvering detection auxiliary parameters;
the method comprises the following specific steps of finishing state estimation by adopting an unscented Kalman filtering UKF algorithm:
step 2.1, determining state initialization by adopting a cooperative target UKF filtering track;
initial value of state variable filtering:
Figure BDA0003250441770000077
wherein r and
Figure BDA0003250441770000078
is the initial position and velocity, a is the initial acceleration, b is the initial auxiliary variable, the default is 0; initial variance matrix: p0Diag (1.0e4,1.0e4,1.0e4,10,10,1, 0.0002), diag is diagonal matrix operation; process noise covariance matrix: q ═ diag (0,0,0,1.0e-5,1.0e-5,1.0e-5, 0, 0); observing a noise covariance matrix: r ═ diag (100,0.05,0.05, 1).
2.2, performing tasteless transformation, calculating sampling points, and calculating corresponding sampling points and weight values of the initial value of the filtering state according to the formulas (2) and (3), wherein the number of the sampling points is 2n +1, n is the number of state variables, the number of the sampling points is 8, and the number of the sampling points is 17;
Figure BDA0003250441770000081
in the formula (2), the reaction mixture is,
Figure BDA0003250441770000082
is (n + kappa) PxxIth row or column of the square root matrix of (1);
the corresponding weight value is as follows:
Figure BDA0003250441770000083
wherein, ω isiThe weight value of the ith Sigma point is; kappa is a proportional parameter and is used for adjusting the distance between a Sigma point and x and only influencing the deviation brought by a high-order moment after the second order; the scaling factor with alpha being a positive value controls the fractional range of the Sigma point, usually taking a smaller value of (0, 1), and beta is the one introducing f (-) high orderA parameter of the item information. In this embodiment, κ ═ 5, α ═ 0.5, and β ═ 2.
And 2.3, predicting the state. And obtaining a one-step prediction state value and a state variance matrix of the state vector through sampling point weighting processing, and meanwhile, calculating a one-step prediction observation value according to the one-step prediction state value.
Figure BDA0003250441770000084
In the formula, f (-) is a state equation, and H (-) is an observation equation;
step 2.4, measurement and prediction are carried out, and a one-step prediction variance matrix and a state observation covariance matrix according to the observed values are obtained through sampling point weighting processing;
Figure BDA0003250441770000091
step 2.5, gain calculation, namely calculating the filtering gain of the unscented Kalman filter according to the one-step prediction variance matrix and the state observation covariance matrix;
Figure BDA0003250441770000092
step 2.6, updating the state, and calculating a state value and a state variance matrix at the next moment according to the calculation result of the step;
Figure BDA0003250441770000093
and 3, judging the maneuvering of the detection target according to the characteristics of the auxiliary variables under the condition of the auxiliary parameter estimation result obtained in the step 2, and finishing the pulse maneuvering detection by adopting the auxiliary variables.
The specific process of the step 3 is as follows: performing hypothesis testing according to the estimation result of the odorless Kalman filtering in the step 2; firstly, recording the mean value and variance of the auxiliary parameters after the filter is stabilized, and filteringThe stable is marked by the filtering residual error, and when the filtering residual error is continuously 180 seconds at PYYWithin 3 times of a threshold value of a diagonal line of (k +1, k), the filtering enters a stable state at the moment; secondly, calculating the distribution probability of the auxiliary variables in normal distribution, wherein the mean value of the normal distribution is the mean value of the auxiliary parameters, the variance is the variance of the auxiliary parameters, and the calculation result is pdf (b); finally, judging whether the target is mobile or not according to the value of 1-pdf (b), and constructing a hypothesis function; when H0: a value of 1-pdf (b) greater than 95% indicates maneuver; when H1: a value of 1-pdf (b) of less than 95% indicates no maneuver.
Examples
In this embodiment, the measurement data is joint simulation data (R, dR) of four ground stations, and random errors equivalent to the performance of each device are loaded, ranging: 10m, speed measurement: 0.2m/s, the simulated trajectory totals 400s, and the initial filtering error is taken into account by adding constant errors of 20m and 1m/s to the position and the speed in three directions respectively.
The altitude change curve of the whole process of the aircraft maneuver is shown in fig. 1, and it can be seen from the graph that the altitude of the aircraft gradually rises from the initial stage, and the rising rate of the aircraft altitude suddenly accelerates in about 180 seconds, which shows that the aircraft has the maneuver at the moment; the three-dimensional ballistic curve of the whole process of the aircraft maneuver is shown in FIG. 2, and the change trend of the aircraft in the three XYZ directions in the geocentric inertia J2000 coordinate system can be seen; the change curve of the maneuvering acceleration of the aircraft is shown in FIG. 3, and the acceleration of the aircraft has sudden change at 180s, namely, pulse maneuvering occurs, the maneuvering acceleration is 12.6m/s2, and the duration is 10 s; the change condition of the auxiliary parameter of the aircraft is shown in fig. 4, and it can be seen that the auxiliary parameter of the aircraft changes after the target maneuvers, and the change is gradual and violent, and reflects the maneuvering characteristics of the target. The change of the maneuver probability of the aircraft is shown in fig. 5, and it can be seen that the target maneuver probability calculated by the auxiliary parameters is greatly changed after 180 seconds, and the maneuver probability is more than 95% until 220 seconds later, and the target maneuver can be confirmed at this moment.

Claims (5)

1. A pulse maneuver detection method based on auxiliary state parameters is characterized by comprising the following steps:
step 1, establishing a maneuvering dynamics model of a target under a geocentric J2000 inertial system, and adding auxiliary parameters in a state vector;
step 2, performing tasteless Kalman filtering according to the maneuvering dynamics model established in the step 1, and estimating maneuvering detection auxiliary parameters;
and 3, judging the maneuvering of the detection target according to the characteristics of the auxiliary variables under the condition of obtaining the auxiliary parameter estimation result in the step 2.
2. The pulse maneuver detection method based on auxiliary state parameters of claim 1, wherein the maneuver dynamics model in step 1 introduces auxiliary parameters based on the state quantities of position, velocity and acceleration to complete the first-order model building of acceleration.
3. An impulse maneuver detection method based on auxiliary state parameters as defined in claim 2, wherein the first-order model of acceleration in step 1 is established as follows:
step 1.1, setting the maneuvering target state vector as [ r, v, a, b ═ X](ii) a r is the target position, r ═ x, y, z)TAnd v is the target speed, v is,
Figure FDA0003250441760000011
a is the target acceleration, b is the auxiliary parameter,
Figure FDA0003250441760000012
Figure FDA0003250441760000013
mass consumed per second, m is the target mass;
the auxiliary parameters are derived to obtain:
Figure FDA0003250441760000014
considering the target specific impulse as
Figure FDA0003250441760000015
When F is constant, the specific impulse Isp is also constant, i.e.
Figure FDA0003250441760000016
Establishing a differential model b' ═ b of auxiliary parameters2；
Step 1.2, establishing a target maneuvering dynamics model:
Figure FDA0003250441760000021
in the formula (1), the reaction mixture is,
Figure FDA0003250441760000022
gris the component of gravitational acceleration in the radial direction of the earth's center,
Figure FDA0003250441760000023
gwis the component of gravitational acceleration on the earth's axis of rotation vector, where J2＝1.08263×10-3Is a second order harmonic coefficient; mu-3.986005X 1014m3/s2Is the constant of the earth's gravity; re6378140.0m is the equator radius of the earth; r is the modulus of the target position vector, z is the value in the z direction of the target position vector,
Figure FDA0003250441760000024
is ECI celestial North Pole; p is a thrust direction vector.
4. The pulse maneuver detection method based on the auxiliary state parameters as claimed in claim 1, wherein the state estimation using unscented kalman filter UKF algorithm comprises the specific steps of:
step 2.1, determining state initialization by adopting a cooperative target UKF filtering track;
initial value of state variable filtering:
Figure FDA0003250441760000025
wherein r and
Figure FDA0003250441760000026
is the initial position and velocity, a is the initial acceleration, b is the initial auxiliary variable, the default is 0; initial variance matrix: p0Diag (1.0e4,1.0e4,1.0e4,10,10,1, 0.0002), diag is diagonal matrix operation; process noise covariance matrix: q ═ diag (0,0,0,1.0e-5,1.0e-5,1.0e-5, 0, 0); observing a noise covariance matrix: r ═ diag (100,0.05,0.05, 1);
2.2, performing tasteless transformation, calculating sampling points, and calculating corresponding sampling points and weight values of the initial value of the filtering state according to the formulas (2) and (3), wherein the number of the sampling points is 2n +1, n is the number of state variables, the number of the sampling points is 8, and the number of the sampling points is 17;
Figure FDA0003250441760000031
in the formula (2), the reaction mixture is,
Figure FDA0003250441760000032
is (n + kappa) PxxIth row or column of the square root matrix of (1);
the corresponding weight value is as follows:
Figure FDA0003250441760000033
wherein, ω isiThe weight value of the ith Sigma point is; kappa is a proportional parameter and is used for adjusting the distance between a Sigma point and x and only influencing the deviation brought by a high-order moment after the second order; a scaling factor with alpha being a positive value controls the subsection range of the Sigma point, usually a smaller value of (0, 1) is taken, and beta is a parameter for introducing f (-) high-order item information; in the present invention, κ ═ 5, α ═ 0.5, β ═ 2;
step 2.3, predicting the state;
obtaining a one-step prediction state value and a state variance matrix of the state vector through sampling point weighting processing, and meanwhile, calculating a one-step prediction observation value according to the one-step prediction state value;
Figure FDA0003250441760000034
in the formula, f (-) is a state equation, and H (-) is an observation equation;
step 2.4, measurement and prediction are carried out, and a one-step prediction variance matrix and a state observation covariance matrix according to the observed values are obtained through sampling point weighting processing;
Figure FDA0003250441760000041
Figure FDA0003250441760000042
step 2.5, gain calculation, namely calculating the filtering gain of the unscented Kalman filter according to the one-step prediction variance matrix and the state observation covariance matrix;
Figure FDA0003250441760000043
step 2.6, updating the state, and calculating a state value and a state variance matrix at the next moment according to the calculation result of the step;
Figure FDA0003250441760000044
5. the pulse maneuver detection method based on auxiliary status parameters as claimed in claim 4, wherein the specific process of step 3 is: based on the estimation result of the unscented kalman filter in step 2,carrying out hypothesis testing; firstly, recording the mean value and variance of auxiliary parameters after the filter is stabilized, taking the filtering residual as a mark for filtering stabilization, and when the filtering residual is continuously in P for 180 secondsYYWithin 3 times of a threshold value of a diagonal line of (k +1, k), the filtering enters a stable state at the moment; secondly, calculating the distribution probability of the auxiliary variables in normal distribution, wherein the mean value of the normal distribution is the mean value of the auxiliary parameters, the variance is the variance of the auxiliary parameters, and the calculation result is pdf (b); finally, judging whether the target is mobile or not according to the value of 1-pdf (b), and constructing a hypothesis function; when H0: a value of 1-pdf (b) greater than 95% indicates maneuver; when H1: a value of 1-pdf (b) of less than 95% indicates no maneuver.
CN202111043910.4A 2021-09-07 2021-09-07 Pulse maneuver detection method based on auxiliary state parameters Pending CN113790727A (en)

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