CN113188507B - Infrared imaging anti-tank missile target distance estimation method - Google Patents

Infrared imaging anti-tank missile target distance estimation method Download PDF

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CN113188507B
CN113188507B CN202110396702.6A CN202110396702A CN113188507B CN 113188507 B CN113188507 B CN 113188507B CN 202110396702 A CN202110396702 A CN 202110396702A CN 113188507 B CN113188507 B CN 113188507B
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孔哲
蔺睿
赵红刚
尚妮妮
胡思翱
张智超
贺江涛
龙婧
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Xian Aerospace Propulsion Institute
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Abstract

The invention provides an infrared imaging anti-tank missile target distance estimation method which can reliably realize rapid and high-precision estimation of a target distance. The method introduces a new observation quantity, namely missile height, on the basis of the traditional estimation algorithm, and takes a relation equation of the missile height and a state quantity as one of new observation equations so as to enhance the observability of the system. On the basis, the traditional extended Kalman filtering method is utilized to realize accurate and reliable estimation of the bullet distance.

Description

Infrared imaging anti-tank missile target distance estimation method
Technical Field
The invention relates to a relative position estimation method, in particular to an infrared imaging anti-tank missile target distance estimation method.
Background
The measurement of the distance between the targets of the infrared imaging anti-tank missile is an important link of a closed loop of an anti-tank missile weapon system and is an input condition of a missile control system. The missile control system takes the above as a basis, realizes automatic parameter adjustment aiming at targets with different distances, and ensures that battle technical indexes such as hit precision, hit falling angle and the like in the full range meet requirements.
The traditional distance measuring method is mainly measured by a laser distance measuring instrument, but with the common use of a laser warning device on an armored car, the method is not suitable for the future battlefield environment. And the precision of other optical measurement means is difficult to meet the requirement of a weapon system.
At present, the estimation of the relative distance of the bullet mainly utilizes or borrows the Kalman filtering thought to construct a state equation and an observation equation, and then the estimation of the distance of the bullet is realized through a proper filtering algorithm. Wherein the state equation is generally a bullet relative motion equation, and the observed quantity is generally a visual angle or a visual angular velocity output by the seeker. In a document published by Harbin Industrial university, a rolling time sequence filtering method is provided by taking a relative equation of motion of a bullet as an observation equation and taking a line-of-sight angle and a relative speed of the bullet as observed quantities, and filtering precision is improved by introducing a cost function and a weighting factor. In the literature of Beijing electromechanical engineering research institute, a robust Kalman filtering method is provided based on an estimation algorithm of a strong tracking filter by taking a line-of-sight angle as an observed quantity, so that the robustness to an initial state and system noise is improved.
Although the above method has proved certain feasibility through mathematical simulation, the following problems exist:
(1) The engineering implementation difficulty is large: the algorithm is relatively complex, the calculation amount is large, and the software and hardware are difficult to realize.
(2) Reliability is difficult to guarantee: the above methods are not generally verified in engineering, and the reliability and stability of the algorithm are to be further checked.
(3) The weighting factors are not easy to determine: in order to improve the filtering performance, the improved filtering algorithm introduces a weighting factor, the selection of the weighting factor needs a large amount of prior information, and if the selection is not proper, the final filtering effect is directly influenced.
Disclosure of Invention
In view of the above, the invention provides an infrared imaging anti-tank missile target distance estimation method, which can reliably realize the rapid and high-precision estimation of the target distance.
The method for estimating the missile-target distance of the infrared imaging antitank missile specifically comprises the following steps:
the method comprises the following steps: establishing a state equation and an observation equation:
the state equation is a missile-target relative motion equation, the speed and the missile-target relative position of the missile are used as state quantities, and the acceleration of the missile is used as a control quantity;
the observed quantity in the observation equation comprises a missile eye sight angle in a pitching plane, a missile eye sight angle in a yawing plane and a missile height;
step two: discretizing the state equation and the observation equation established in the step one;
step three: and (4) establishing an extended Kalman filtering recursion equation set according to the state equation and the observation equation after discretization in the step two, and calculating a bullet distance estimation value according to the established extended Kalman filtering recursion equation set.
In the first step, a state equation is established under a ground rectangular coordinate system xyz, wherein in the ground rectangular coordinate system xyz, x is the direction pointing to a target in a horizontal plane, and the pointing target is positive; y is the direction vertical to the ground and is positive upwards;
let the velocity matrix of the missile be [ v ] under the rectangular coordinate system of the ground x v y v z ] T Acceleration matrix is [ a ] xm a ym a zm ] T The relative position matrix of the eyes is [ r ] x r y r z ] T (ii) a The state equation is as follows:
Figure BDA0003018839760000021
in the first step, the established observation equation is as follows:
Figure BDA0003018839760000022
wherein: q. q.s ε Is the angle of line of sight of the bullet in the pitch plane, q β Is the angle of the line of sight of the bullet in the yaw plane,
Figure BDA0003018839760000023
as the true value r of missile height y Actual measurement value after adding noise, e 1 、e 2 、e 3 To measure noise.
In the second step, the state equation and the observation equation after discretization are respectively as follows:
X k+1 ＝Φ k+1 X k +B k U k +Γ k+1 V k
Z k+1 ＝H(X k+1 )+e k+1
wherein: x = [ r ] x r y r z v x v y v z ] T ，U＝[a xm a ym a zm ] T ，V＝[v 1 v 2 v 3 v 4 v 5 v 6 ] T ；Z＝[q ε q β r y ] T ，e＝[e 1 e 2 e 3 ] T (ii) a The subscripts k, k +1 denote different sampling instants; phi is a transfer matrix, B is a control matrix, and gamma is a process noise matrix; the method specifically comprises the following steps:
Figure BDA0003018839760000031
Figure BDA0003018839760000032
Figure BDA0003018839760000033
wherein: t is the calculation period, I 3 Is a third order identity matrix, 0 3 Is a zero matrix of third order.
In the third step, the established extended kalman filtering recursion equation set is as follows:
Figure BDA0003018839760000034
Figure BDA0003018839760000035
Figure BDA0003018839760000036
Figure BDA0003018839760000037
Figure BDA0003018839760000038
wherein:
Figure BDA0003018839760000039
an estimated value representing the state quantity at the time k +1 from the previous k total observed values; phi k+1,k The state at the time k is transferred to the state at the time k + 1;
Figure BDA00030188397600000310
an estimated value representing the state quantity at time k; b is k Representing the k time control matrix, U k Represents a control amount at time k; p k+1/k An estimated value representing the error covariance of the k +1 time from the previous k total observations; p k/k Represents the error covariance at time k; gamma-shaped k+1 Representing a k +1 time process noise matrix; q k Representing the noise variance at time k; k k+1 Representing the kalman gain at time k + 1; r k+1 Represents the measurement noise variance at time k + 1; and (4) I unit matrix.
Has the advantages that:
(1) The method of the invention introduces new observed quantity and observation equation based on the use characteristics of the antitank missile, improves the observability of the system, and the improvement of the observability has great significance on the rapid convergence of the estimation of the missile-target distance.
(2) Due to the improvement of observability, the estimation of the missile-eye distance can be realized by means of the traditional extended Kalman filtering algorithm, and the algorithm is simple and high in reliability.
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FIG. 1 is an algorithm schematic diagram of an infrared imaging antitank missile target distance estimation technology of the invention;
fig. 2 is a ground simulation graph.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The embodiment provides an anti-tank missile target distance estimation method based on infrared imaging, introduces a new observation quantity, namely missile height, on the basis of a traditional estimation algorithm, and takes a relation equation of the missile height and a state quantity as one of new observation equations so as to enhance the observability of the system. On the basis, the traditional extended Kalman filtering method is utilized to realize accurate and reliable estimation of the bullet distance.
The method for estimating the missile-target distance of the infrared imaging anti-tank missile mainly comprises the following steps: establishing a state equation and an observation equation, discretizing the state equation and the observation equation, establishing an extended Kalman filtering recursion equation set and setting an initial value;
the specific implementation steps are as follows:
the method comprises the following steps: establishing a relative equation of motion of the bullet under a rectangular coordinate system as a state equation and an observation equation:
firstly, establishing a rectangular coordinate system xyz, wherein x is the direction pointing to a target in a horizontal plane, and the pointing target is positive; y is the direction vertical to the ground and is positive upwards; z is determined according to the right-hand rule perpendicular to the xy-plane direction. And taking the equation of relative motion of the missile eyes under the rectangular coordinate system as a state equation and an observation equation.
The velocity matrix of the missile is [ v ] under the rectangular coordinate system x v y v z ] T Acceleration matrix is [ a ] xm a ym a zm ] T The relative position matrix of the eyes is [ r ] x r y r z ] T (ii) a The equation of state is as follows:
Figure BDA0003018839760000041
wherein: [ r ] of x r y r z v x v y v z ] T Is a state quantity, denoted as X; [ a ] A xm a ym a zm ] T Is a control quantity, and is marked as U; [ v ] of 1 v 2 v 3 v 4 v 5 v 6 ] T Is noise, denoted as V;
the following observation equation is established:
Figure BDA0003018839760000051
wherein: q. q.s ε Is the angle of line of sight of the bullet in the pitch plane, q β Is the angle of the line of sight of the bullet in the yaw plane,
Figure BDA0003018839760000052
as the true value r of missile height y Actual measurement value (r) after adding noise y I.e. new observations introduced-missile height), e 1 、e 2 、e 3 To measure noise.
Let Z = [ q ] ε q β r y ] T ，e＝[e 1 e 2 e 3 ] T Then the observation equation can be written as Z = H (X) + e, where H (X) observes the matrix.
Step two: discretizing the state equation and the observation equation established in the step one to obtain:
X k+1 ＝Φ k+1 X k +B k U k +Γ k+1 V k
Z k+1 ＝H(X k+1 )+e k+1
wherein: subscripts k, k +1 in the discretization equation represent different sampling moments; whereby X k+1 Represents the state quantity at the moment k + 1; phi is a transfer matrix, B is a control matrix, and gamma is a process noise matrix; specifically, can be written as:
Figure BDA0003018839760000053
Figure BDA0003018839760000054
Figure BDA0003018839760000055
wherein: t is the calculation period, I 3 Is a third order identity matrix, 0 3 Is a zero matrix of third order.
Step three: establishing an extended Kalman filtering recursion equation set according to the state equation and the observation equation after discretization in the step two:
Figure BDA0003018839760000056
Figure BDA0003018839760000057
Figure BDA0003018839760000058
Figure BDA0003018839760000059
Figure BDA0003018839760000061
wherein:
Figure BDA0003018839760000062
an estimated value representing the system state at the time k +1 from the previous k total observed values; phi k+1,k For the transition matrix, the state at the moment k is transferred to the state at the moment k + 1;
Figure BDA0003018839760000063
an estimated value representing the state quantity at time k; b is k Representing the k time control matrix, U k A system control quantity representing the k moment; p is an error covariance matrix, then P k+1/k An estimated value representing the error covariance of the k +1 time from the previous k total observations; p k/k Represents the error covariance at time k; gamma-shaped k+1 Representing a k +1 time process noise matrix; q isSystem noise variance matrix, then Q k Representing the noise variance at time k; k is the Kalman gain matrix, then K k+1 Representing the kalman gain at time k + 1; r is the measurement noise variance matrix, then R k+1 Represents the measurement noise variance at time k + 1; and (4) I unit matrix.
The iterative process of estimating the bullet distance according to the extended Kalman filtering recursion equation set comprises the following steps:
knowing the initial value of the estimate
Figure BDA0003018839760000064
P k/k Respectively carrying out forward state estimation on the k +1 moment by the previous k observation values according to a formula (1) and a formula (2) in the extended Kalman filtering recursion equation system
Figure BDA0003018839760000065
P k+1/k ；
Then, according to the above formula (4), the kalman gain K at the time K +1 is calculated k+1 ；
Then by observing the variable Z according to the above equation (3) k+1 Updating an estimate
Figure BDA0003018839760000066
Filtered estimates at time k +1
Figure BDA0003018839760000067
Will be provided with
Figure BDA0003018839760000068
As the time of k +1
Figure BDA0003018839760000069
The output value of (d);
due to the fact that
Figure BDA00030188397600000610
Then the estimated value of the bullet distance after k +1 time filtering can be calculated
Figure BDA00030188397600000611
Finally, updating the error covariance P at the moment k +1 according to the formula (5) k+1/k+1 (ii) a Will be calculated
Figure BDA00030188397600000612
P k+1/k+1 And iteration is carried out as an initial value at the next moment.
The ground simulation result proves the effectiveness of the method. The method selects a flat flying projectile path form in the test process, the initial target distance is 6000m, the initial value error is 1000m, and the algorithm can estimate the relative distance of the projectile to the precision range of 20m within about 2s after verification, as shown in figure 2.
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. An infrared imaging antitank missile target distance estimation method is characterized in that,
the method comprises the following steps: establishing a state equation and an observation equation:
the state equation is a missile-target relative motion equation, the speed and the missile-target relative position of the missile are used as state quantities, and the acceleration of the missile is used as a control quantity;
the observed quantity in the observation equation comprises a missile eye sight angle in a pitching plane, a missile eye sight angle in a yawing plane and a missile height;
step two: discretizing the state equation and the observation equation established in the step one;
step three: and (4) establishing an extended Kalman filtering recursion equation set according to the state equation and the observation equation after discretization in the step two, and calculating a bullet distance estimation value according to the established extended Kalman filtering recursion equation set.
2. The method of claim 1, wherein the method comprises the steps of: in the first step, a state equation is established under a ground rectangular coordinate system xyz, wherein in the ground rectangular coordinate system xyz, x is the direction pointing to a target in a horizontal plane, and the pointing target is positive; y is the direction vertical to the ground and is positive upwards;
let the velocity matrix of the missile be [ v ] under the rectangular coordinate system of the ground x v y v z ] T Acceleration matrix is [ a ] xm a ym a zm ] T The relative position matrix of the eyes is [ r ] x r y r z ] T ，[v 1 v 2 v 3 v 4 v 5 v 6 ] T Is a noise matrix;
the state equation is as follows:
Figure FDA0003857749750000011
3. the method of estimating the missile target distance of an infrared imaging antitank missile as claimed in claim 2, wherein: in the first step, the established observation equation is as follows:
Figure FDA0003857749750000012
wherein: q. q.s ε Is the angle of line of sight of the bullet in the pitch plane, q β Is the angle of the line of sight of the bullet in the yaw plane,
Figure FDA0003857749750000013
as the true value r of missile height y Actual measurement value after adding noise, e 1 、e 2 、e 3 To measure noise.
4. The method of estimating the missile target distance of an infrared imaging antitank missile as claimed in claim 3, wherein: in the second step, the state equation and the observation equation after discretization are respectively as follows:
X k+1 ＝Φ k+1 X k +B k U k +Γ k+1 V k
Z k+1 ＝H(X k+1 )+e k+1
wherein: x = [ r ] x r y r z v x v y v z ] T ，U＝[a xm a ym a zm ] T ，V＝[v 1 v 2 v 3 v 4 v 5 v 6 ] T ；Z＝[q ε q β r y ] T ，e＝[e 1 e 2 e 3 ] T (ii) a The subscripts k, k +1 denote different sampling instants; phi is a transfer matrix, B is a control matrix, gamma is a process noise matrix, H (X) is an observation matrix, and H (X) is k+1 ) An observation matrix of the system at the moment k + 1; the method specifically comprises the following steps:
Figure FDA0003857749750000021
Figure FDA0003857749750000022
Figure FDA0003857749750000023
wherein: t is the calculation period, I 3 Is a third order identity matrix, 0 3 Is a zero matrix of third order.
5. The method of estimating the missile target distance of an infrared imaging anti-tank missile as claimed in claim 4, wherein: in the third step, the established extended kalman filtering recursion equation set is as follows:
Figure FDA0003857749750000024
Figure FDA0003857749750000025
Figure FDA0003857749750000026
Figure FDA0003857749750000027
Figure FDA0003857749750000028
wherein:
Figure FDA0003857749750000029
an estimated value representing the state quantity at the time k +1 from all previous k observations; phi k+1,k The state at the time k is transferred to the state at the time k + 1;
Figure FDA00038577497500000210
an estimated value representing the state quantity at time k; b is k Representing the k time control matrix, U k A control amount indicating the time k; p k+1/k An estimated value representing the error covariance of the k +1 time from the previous k total observations; p k/k Represents the error covariance at time k; gamma-shaped k+1 Representing a k +1 time process noise matrix; q k Representing the noise variance at time k; k k+1 Representing the kalman gain at time k + 1; r k+1 Represents the measurement noise variance at time k + 1; and (4) I unit matrix.
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