CN110749322B - Target tracking method based on speed measurement information - Google Patents

Target tracking method based on speed measurement information Download PDF

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CN110749322B
CN110749322B CN201911006408.9A CN201911006408A CN110749322B CN 110749322 B CN110749322 B CN 110749322B CN 201911006408 A CN201911006408 A CN 201911006408A CN 110749322 B CN110749322 B CN 110749322B
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target tracking
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tracking result
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CN110749322A (en
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梁源
任章
李清东
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Beihang University
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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
    • G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P3/00—Measuring linear or angular speed; Measuring differences of linear or angular speeds

Abstract

The invention provides a target tracking method based on speed measurement information, which comprises the steps that firstly, an airborne platform estimates the position information of a target by utilizing a received target-airborne relative angle, target-airborne relative distance information and a target positioning model to obtain a target tracking result in a target positioning stage; on the basis of the tracking result, the speed of the target is estimated by combining the axial speed of the target and utilizing a target speed measurement model, and the target tracking result of the target speed measurement stage is obtained. And synthesizing the target tracking result of the target positioning stage and the target tracking result of the target speed measuring stage to obtain and output a final target tracking result. The invention solves the problems of poor robustness and applicability of a system, high difficulty of parameter setting, large error of a target tracking algorithm and insufficient stability in a target tracking calculation mode of directly introducing axial speed observation into a traditional target tracking algorithm.

Description

Target tracking method based on speed measurement information
Technical Field
The invention belongs to the field of target tracking and positioning, and particularly relates to a target tracking method based on speed measurement information.
Background
Target tracking and positioning is a research hotspot in the field of aviation and has been paid much attention all the time. In a traditional target positioning mode, on the basis of carrying a related sensor observation platform, related information such as a distance and an angle of a target is acquired in an independent detection mode, and then a geometric constraint condition is combined to obtain target position information so as to complete tracking and positioning of the target. However, with the advancement of related sensor technologies, the current onboard sensors can acquire information such as relative distance, relative angle and the like of the target, and can also measure the relative axial velocity of the target (velocity component in the target-onboard direction) in real time to acquire the relative axial velocity of the target. Theoretically, the target tracking precision can be remarkably improved by introducing the relative axial speed of the target (representing the advance prediction information of the target position change) for target tracking, however, because the axial speed is not only dependent on the real moving speed of the target, but also dependent on the relative angle of the target and the carrier, the coupling degree between the state variables of the system can be greatly increased when the axial speed observation is introduced in the traditional target tracking algorithm, considering that the target tracking system is a system with stronger nonlinearity, the robustness and the applicability of the system can be remarkably reduced by increasing the coupling between the state variables, and the difficulty of parameter setting in the target tracking algorithm is increased, and unreasonable algorithm parameters can directly cause the error of the target tracking algorithm to be increased and even diverge, due to the reasons, the mode of directly introducing axial speed observation into the traditional target tracking algorithm is difficult to popularize and apply in engineering practice.
Disclosure of Invention
In view of the above problems in the prior art, an object of the present invention is to provide a target tracking method based on velocity measurement information, so as to realize target tracking and positioning in the presence of velocity measurement information.
The purpose of the invention is realized by the following technical scheme:
firstly, a target tracking model under the condition that speed measurement information exists is established, the target tracking model is further split into a target positioning model and a target speed measurement model, a target tracking process is divided into two stages of target positioning and target speed measurement according to the split result of the model, and the position of a target is determined by mainly utilizing the relative angle of the target and the carrier and the relative distance information of the target and the carrier in the target positioning stage. And the target speed measurement stage is mainly used for estimating the movement speed of the target by using the positioning result and the speed measurement information in the target positioning stage. And finally, integrating the result of the target positioning stage and the result of the target speed measuring stage, and outputting the result as a final target tracking result.
The target tracking method based on speed measurement information comprises the following steps:
step 1: and a target positioning stage: the method comprises the steps that an airborne platform estimates position information of a target by utilizing a target-airborne relative angle and target-airborne relative distance information received by the airborne platform, and a target tracking result of a target positioning stage is obtained;
step 2: a target speed measurement stage: the airborne platform estimates the speed information of the target by using the relative axial speed of the target received by the airborne platform and the target tracking result in the target positioning stage to obtain the target tracking result in the target speed measuring stage;
and step 3: and integrating the target tracking result of the target positioning stage and the target tracking result of the target speed measuring stage to obtain and output a final target tracking result.
Further, the specific method for estimating the position of the target in step 1 is as follows:
using a target positioning model, taking the relative distance of the target-carrier, the relative azimuth angle of the target-carrier and the relative pitch angle of the target-carrier at the moment k as filtering input, and performing filtering estimation by using an extended Kalman filtering algorithm (EKF) to obtain a target tracking result at the target positioning stage at the moment k
Figure BDA0002242915080000021
Further, the specific method of the target tracking result in the target speed measurement stage in step 2 is as follows:
target tracking result in target positioning stage at moment k
Figure BDA0002242915080000022
Calculating the relative azimuth angle and the relative pitch angle of the target-carrier, wherein the specific calculation mode is as follows:
Figure BDA0002242915080000023
Figure BDA0002242915080000024
wherein the content of the first and second substances,
Figure BDA0002242915080000025
and
Figure BDA0002242915080000026
are respectively as
Figure BDA0002242915080000027
1, 2, and 3 elements of theta1And
Figure BDA0002242915080000028
respectively obtaining a relative azimuth angle and a relative pitch angle through calculation; using the above-mentioned theta1And
Figure BDA0002242915080000029
meanwhile, the relative axial speed of the target-carrier at the moment k is used as filtering input, EKF calculation is carried out by using a target speed measurement model, and a target tracking result of a target speed measurement stage at the moment k is obtained
Figure BDA00022429150800000210
xo,yo,zoSequentially represents three shaft positions of the carrier under X, Y and Z axes.
Further, the target location model is:
x1(k+1)＝Φ1x1(k)+w1(k)
z1(k)＝H1(x1(k))+r1(k)
the corresponding state vector and state transition matrix are:
Figure BDA0002242915080000031
Figure BDA0002242915080000032
the corresponding observation vectors are:
Figure BDA0002242915080000033
namely the relative distance between the target and the carrier, the relative azimuth angle between the target and the carrier and the relative pitch angle between the target and the carrier; the corresponding observation matrix is:
Figure BDA0002242915080000034
w1(k) and r1(k) Is zero-mean white gaussian noise of the corresponding dimension.
Further, the target velocity measurement model is as follows:
x2(k+1)＝Φ2x2(k)+w2(k)
z2(k)＝H2(x2(k))+r2(k)
the corresponding state vector and state transition matrix are:
Figure BDA0002242915080000035
Figure BDA0002242915080000041
the corresponding observation vectors are: z2(k)＝[dv]TI.e. the relative axial speed of the target-vehicle; the corresponding observation matrix is:
Figure BDA0002242915080000042
wherein, theta1And
Figure BDA0002242915080000043
the calculated relative azimuth angle and the calculated relative pitch angle are both
Figure BDA0002242915080000044
And calculating to obtain the following results:
Figure BDA0002242915080000045
Figure BDA0002242915080000046
wherein
Figure BDA0002242915080000047
And
Figure BDA0002242915080000048
are respectively as
Figure BDA0002242915080000049
2 nd and 3 rd elements.
The input of the target tracking algorithm designed by the invention additionally introduces the relative axial speed of the target besides the relative angle and the relative distance information of the target and the carrier; the target tracking algorithm designed by the invention constructs a target tracking model under the condition of existence of target axial velocity, and splits the target tracking model into a target positioning model and a target velocity measurement model; the target tracking algorithm designed by the invention decomposes a target tracking flow into two stages of target positioning and target speed measurement, wherein the target positioning stage mainly utilizes the relative angle of a target-carrier and the relative distance information of the target-carrier to determine the position of a target; in the target speed measurement stage, the movement speed of the target is estimated mainly by using the calculation result in the target positioning stage and the axial speed of the target; the final target tracking result of the algorithm is the synthesis of the target positioning stage result and the target speed measuring stage result.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention solves the problems of poor robustness and applicability of a system, high difficulty in parameter setting, large error of a target tracking algorithm and insufficient stability in a target tracking calculation mode of directly introducing axial speed observation into a traditional target tracking algorithm, and provides the target tracking method which is easy to popularize and apply in engineering practice;
2. according to the method, the target tracking model under the condition that the target axial velocity observed quantity exists is constructed and is split into the target positioning model and the target speed measuring model, and target tracking calculation is performed in a two-stage mode based on the split model, so that on one hand, the calculation complexity is reduced, and the calculation efficiency is improved; on the other hand, the method realizes the reduction coupling among the parameters, and is beneficial to enhancing the stability of the algorithm.
Drawings
FIG. 1 is a block diagram of a target tracking algorithm according to the present invention;
FIG. 2 is a timing diagram of a target tracking algorithm according to the present invention;
FIG. 3 is a schematic diagram of simulation of the target tracking algorithm operating result of the present invention 1;
fig. 4 is a schematic diagram of simulation of the operation result of the target tracking algorithm according to the present invention 2.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The embodiment provides a target tracking method based on speed measurement information, and the overall flow block diagram of the method is shown in fig. 1, and the system timing diagram is shown in fig. 2.
For convenience of subsequent introduction, firstly, a target tracking model used in the invention under the condition of existence of target axial velocity observed quantity, and a target positioning model and a target velocity measurement model obtained by splitting the target tracking model are introduced:
the traditional target tracking model comprises the following steps:
x(k+1)＝Φx(k)+w(k)
z(k)＝H(x(k))+r(k)
the corresponding state vector is:
Figure BDA0002242915080000051
sequentially setting the target three-axis position and the target three-axis speed under X, Y and Z axes;
the system state transition matrix is:
Figure BDA0002242915080000061
t is the system sampling interval, and the state error w (k) is zero-mean white Gaussian noise of the corresponding dimension.
The observation vector is
Figure BDA0002242915080000062
Wherein the ratio of r, theta,
Figure BDA0002242915080000063
dv is in turn the relative distance of the target-vehicle, the relative azimuth angle of the target-vehicle, the relative pitch angle of the target-vehicle and the relative axial velocity of the target-vehicle, respectively. The observation matrix is:
Figure BDA0002242915080000064
wherein x iso,yo,zo,
Figure BDA0002242915080000065
Sequentially representing three-axis positions of the carrier and three-axis speeds of the carrier under X, Y and Z axes; atan is the inverse tangent function, r (k) is the observation noise, which is white gaussian noise corresponding to zero mean of the dimension, and H (x (k)) (2) and H (x (k)) (3) represent the 2 nd and 3 rd elements of H (x (k)), respectively.
It can be seen from the observation vector model that the observation vector corresponding to dv is a multivariable observation vector with severe cross coupling and extremely strong nonlinearity, and if the traditional method is adopted, the target tracking model is directly used for filtering, so that the situation of filtering divergence and tracking failure is easily caused.
Therefore, the invention splits the model, and can further obtain the following target positioning model and target speed measuring model:
a target positioning model:
x1(k+1)＝Φ1x1(k)+w1(k)
z1(k)＝H1(x1(k))+r1(k)
the corresponding state vector and state transition matrix are:
Figure BDA0002242915080000071
Figure BDA0002242915080000072
the corresponding observation vectors are:
Figure BDA0002242915080000073
namely the relative distance between the target and the carrier, the relative azimuth angle between the target and the carrier and the relative pitch angle between the target and the carrier; the corresponding observation matrix is:
Figure BDA0002242915080000074
w1(k) and r1(k) Is zero-mean white gaussian noise of the corresponding dimension.
For convenience of the following description, the target tracking result calculated by using the target positioning model is defined as:
Figure BDA0002242915080000075
a target speed measurement model:
x2(k+1)＝Φ2x2(k)+w2(k)
z2(k)＝H2(x2(k))+r2(k)
the corresponding state vector and state transition matrix are:
Figure BDA0002242915080000076
Figure BDA0002242915080000081
the corresponding observation vectors are: z2(k)＝[dv]TI.e. the relative axial speed of the target-vehicle; the corresponding observation matrix is:
Figure BDA0002242915080000082
wherein, theta1And
Figure BDA0002242915080000083
the calculated relative azimuth angle and the calculated relative pitch angle are both
Figure BDA0002242915080000084
And calculating to obtain the following results:
Figure BDA0002242915080000085
Figure BDA0002242915080000086
wherein
Figure BDA0002242915080000087
And
Figure BDA0002242915080000088
are respectively as
Figure BDA0002242915080000089
2 nd and 3 rd elements.
On the basis of the target tracking model, the target tracking process designed by the invention is introduced, and the specific process comprises the following steps:
step 1: and a target positioning stage: and the airborne platform estimates the position information of the target by utilizing the received relative angle of the target and the airborne and the relative distance information of the target and the airborne to obtain the target tracking result in the target positioning stage.
Using the target positioning model, taking the relative distance of the target-carrier, the relative azimuth angle of the target-carrier, and the relative pitch angle of the target-carrier at the time k as filtering inputs, and performing filtering estimation by using an extended kalman filtering algorithm (EKF) to obtain the target tracking result at the target positioning stage at the time k
Figure BDA00022429150800000810
The EKF algorithm is a commonly used nonlinear filtering algorithm, and is specifically described as follows:
for a nonlinear continuous discrete system as follows:
Xk+1＝f(Xk)+wk
Zk＝h(Xk)+vk
wherein, XkIs a system state, ZkFor systematic observation information, a random variable wkAnd vkThe noise is the process noise and the measurement noise of the system respectively, and is the zero mean value Gaussian white noise which is independent from each other. Wherein, the variance of the process noise is Q, and the variance of the measurement noise is R. f is the sum system state variable X in the state equationkA time k dependent nonlinear function; h is the sum state variable X in the observation equationkA non-linear function related to time k.
Applying an EKF algorithm to the model:
Figure BDA0002242915080000091
Pk,k-1＝APk-1AT+Q
Kk＝Pk,k-1HT[HPk,k-1HT+R]-1
Figure BDA0002242915080000092
Pk＝[I-KkH]Pk,k-1
wherein the content of the first and second substances,
Figure BDA0002242915080000093
represents the result of the filtering of the previous step,
Figure BDA0002242915080000094
one-step prediction of the representative state, Pk,k-1Representing the one-step prediction error variance, KkRepresenting the filter gain, PkRepresenting the current step filter error variance, Pk-1Representing the filtering error variance of the k-1 step; i represents an identity matrix of corresponding dimension, wherein A and H represent f and H respectively
Figure BDA0002242915080000095
The jacobian matrix is a matrix in which the first partial derivatives of a function commonly used in mathematics are arranged in a certain way.
Step 2: a target speed measurement stage: the airborne platform estimates the speed information of the target by using the axial speed (speed component in the direction of the target-airborne) of the target received by the airborne platform and the target tracking result in the target positioning stage, and obtains the target tracking result in the target speed measuring stage.
Firstly, the target tracking result of the target positioning stage is utilized
Figure BDA0002242915080000096
Calculating the relative azimuth angle of the target-carrier and the relative pitch angle of the target-carrier,the specific calculation method is as follows:
Figure BDA0002242915080000097
Figure BDA0002242915080000098
wherein
Figure BDA0002242915080000099
And
Figure BDA00022429150800000910
are respectively as
Figure BDA00022429150800000911
1, 2, and 3 elements of theta1And
Figure BDA0002242915080000101
respectively a relative azimuth angle and a relative pitch angle obtained by calculation. Using the above-mentioned theta1And
Figure BDA0002242915080000102
meanwhile, the relative axial speed of the target-carrier at the moment k is used as filtering input, EKF calculation is carried out by using a target speed measurement model, and a target tracking result of a target speed measurement stage at the moment k is obtained
Figure BDA0002242915080000103
And step 3: and integrating the target tracking result of the target positioning stage and the target tracking result of the target speed measuring stage to obtain and output a final target tracking result.
Defining the final output target tracking result as
Figure BDA0002242915080000104
Wherein
Figure BDA0002242915080000105
Figure BDA0002242915080000106
Are respectively as
Figure BDA0002242915080000107
The 1 st, 2 nd and 3 rd elements,
Figure BDA0002242915080000108
Figure BDA0002242915080000109
are respectively as
Figure BDA00022429150800001010
The 1 st, 2 nd and 3 rd elements in (b). In addition, in order to further improve the target tracking accuracy, the following replacement is further made:
Figure BDA00022429150800001011
and (3) outputting the filtering result obtained in the step (3) as a final target tracking result, wherein the final output result is shown in fig. 3 to 4, fig. 3 is a schematic diagram of a three-axis position tracking error, and fig. 4 is a schematic diagram of a three-axis velocity tracking error. It can be seen from the simulation graph that, by using the target tracking algorithm designed by the invention, the position error and the speed error of the three axial directions are effectively limited within a certain allowable range, the tracking accuracy is sufficient, and the time divergence is avoided, that is, the accurate positioning and tracking of the target can be effectively realized by using the algorithm.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (5)

1. A target tracking method based on speed measurement information is characterized by comprising the following steps:
step 1: and a target positioning stage: the method comprises the steps that an airborne platform estimates position information of a target by utilizing a target-airborne relative angle and target-airborne relative distance information received by the airborne platform, and a target tracking result of a target positioning stage is obtained;
step 2: a target speed measurement stage: the airborne platform estimates the speed information of the target by using the relative axial speed of the target received by the airborne platform and the target tracking result in the target positioning stage to obtain the target tracking result in the target speed measuring stage;
and step 3: and integrating the target tracking result of the target positioning stage and the target tracking result of the target speed measuring stage to obtain and output a final target tracking result.
2. The target tracking method based on velocity measurement information according to claim 1, wherein the specific method for estimating the position of the target in step 1 is as follows:
using a target positioning model, taking the relative distance of the target-carrier, the relative azimuth angle of the target-carrier and the relative pitch angle of the target-carrier at the moment k as filtering input, and performing filtering estimation by using an extended kalman filtering algorithm to obtain a target tracking result at the target positioning stage at the moment k
Figure FDA0002974086180000011
3. The target tracking method based on speed measurement information according to claim 1 or 2, wherein the specific method of the target tracking result in the target speed measurement stage in step 2 is as follows:
target tracking result in target positioning stage at moment k
Figure FDA0002974086180000012
Calculating the relative azimuth angle and the relative pitch angle of the target-carrierThe calculation method is as follows:
Figure FDA0002974086180000013
Figure FDA0002974086180000014
wherein the content of the first and second substances,
Figure FDA0002974086180000015
and
Figure FDA0002974086180000016
are respectively as
Figure FDA0002974086180000017
1, 2, and 3 elements of theta1And
Figure FDA0002974086180000018
respectively obtaining a relative azimuth angle and a relative pitch angle through calculation; using the above-mentioned theta1And
Figure FDA0002974086180000019
meanwhile, the relative axial speed of the target-carrier at the moment k is used as filtering input, EKF calculation is carried out by using a target speed measurement model, and a target tracking result of a target speed measurement stage at the moment k is obtained
Figure FDA00029740861800000110
4. The target tracking method based on velocity measurement information according to claim 2, wherein the target positioning model is:
x1(k+1)＝Φ1x1(k)+w1(k)
z1(k)＝H1(x1(k))+r1(k)
the corresponding state vector and state transition matrix are:
Figure FDA0002974086180000021
Figure FDA0002974086180000022
the corresponding observation vectors are:
Figure FDA0002974086180000023
namely the relative distance between the target and the carrier, the relative azimuth angle between the target and the carrier and the relative pitch angle between the target and the carrier; the corresponding observation matrix is:
Figure FDA0002974086180000024
w1(k) and r1(k) Is zero-mean white gaussian noise of the corresponding dimension.
5. The target tracking method based on velocity measurement information according to claim 3, wherein the target velocity measurement model is:
x2(k+1)＝Φ2x2(k)+w2(k)
z2(k)＝H2(x2(k))+r2(k)
the corresponding state vector and state transition matrix are:
Figure FDA0002974086180000025
Figure FDA0002974086180000026
the corresponding observation vectors are: z2(k)＝[dv]TI.e. the relative axial speed of the target-vehicle; the corresponding observation matrix is:
Figure FDA0002974086180000031
wherein, theta1And
Figure FDA0002974086180000032
the calculated relative azimuth angle and the calculated relative pitch angle are both
Figure FDA0002974086180000033
And calculating to obtain the following results:
Figure FDA0002974086180000034
Figure FDA0002974086180000035
wherein
Figure FDA0002974086180000036
And
Figure FDA0002974086180000037
are respectively as
Figure FDA0002974086180000038
2 and 3 elements of (a), xo,yo,zoSequentially represents three shaft positions of the carrier under X, Y and Z axes.
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