WO2023157157A1 - 目標追尾装置及び目標追尾方法 - Google Patents

目標追尾装置及び目標追尾方法 Download PDF

Info

Publication number
WO2023157157A1
WO2023157157A1 PCT/JP2022/006289 JP2022006289W WO2023157157A1 WO 2023157157 A1 WO2023157157 A1 WO 2023157157A1 JP 2022006289 W JP2022006289 W JP 2022006289W WO 2023157157 A1 WO2023157157 A1 WO 2023157157A1
Authority
WO
WIPO (PCT)
Prior art keywords
target
tracking
state
observation
unit
Prior art date
Application number
PCT/JP2022/006289
Other languages
English (en)
French (fr)
Japanese (ja)
Inventor
洋志 亀田
將 白石
遼平 川南
正義 系
Original Assignee
三菱電機株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 三菱電機株式会社 filed Critical 三菱電機株式会社
Priority to PCT/JP2022/006289 priority Critical patent/WO2023157157A1/ja
Priority to JP2023573317A priority patent/JP7580637B2/ja
Publication of WO2023157157A1 publication Critical patent/WO2023157157A1/ja

Links

Images

Classifications

    • G—PHYSICS
    • 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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems

Definitions

  • the present disclosure relates to a target tracking device and a target tracking method.
  • the target tracking device that predicts the state of a tracked target using a motion model of the tracked target (see Non-Patent Document 1, for example).
  • the tracked target state includes, for example, the position of the tracked target, the speed of the tracked target, and the aerodynamic coefficient of the tracked target.
  • the target tracking device includes a smoothing section and a prediction section.
  • the smoothing unit obtains observation position information indicating the observation position of the tracking target, and obtains a predicted value from the prediction unit.
  • the predicted value is a prediction result of the state of the tracking target at the position observation time, which is predicted by the prediction unit one sampling before the position observation time at which the observation position is observed.
  • the prediction unit uses the motion model of the tracking target (see equation (8)) to predict the state of the tracking target one sampling after the position observation time from the state estimated by the smoothing unit. Output the predicted value of the state to the smoothing unit.
  • the future state of the tracked target will also change as the tracked target's direction of movement changes.
  • the motion model of the tracked target used by the prediction unit of the target tracking device disclosed in Non-Patent Document 1 there is a problem that changes in the movement direction of the tracked target are not considered. For this reason, in the target tracking device, for example, when the movement direction of the tracking target changes due to a change in the attitude angle of the tracking target, the prediction accuracy of the state of the tracking target by the prediction unit may deteriorate. be.
  • the present disclosure has been made in order to solve the above problems, and aims to obtain a target tracking device and a target tracking method capable of predicting the state of a tracked target in consideration of the movement direction of the tracked target. do.
  • a target tracking device acquires observation position information indicating an observation position of a tracking target, and a position observation time predicted one sampling before the position observation time when the observation position is observed.
  • a smoothing unit that obtains the predicted value of the state of the tracked target at the time of observation, estimates the state of the tracked target at the time of position observation from the observed position information and the predicted value, and a motion model of the tracked target that changes the direction of movement of the tracked target. Predict the state of the tracking target one sampling after the time of position observation from the state estimated by the smoothing unit using the motion model including the acceleration term related to , and output the predicted value of the state of the tracking target to the smoothing unit. and a prediction unit that
  • FIG. 1 is a configuration diagram showing a target tracking device according to Embodiment 1;
  • FIG. 2 is a hardware configuration diagram showing hardware of the target tracking device according to Embodiment 1.
  • FIG. 2 is a hardware configuration diagram of a computer when the target tracking device is implemented by software, firmware, or the like;
  • FIG. 2 is a configuration diagram showing a target tracking device according to Embodiment 2;
  • FIG. 9 is a hardware configuration diagram showing hardware of a target tracking device according to Embodiment 2;
  • FIG. 1 is a configuration diagram showing a target tracking device according to Embodiment 1.
  • FIG. 2 is a hardware configuration diagram showing hardware of the target tracking device according to the first embodiment.
  • the target tracking device shown in FIG. 1 includes a tracking gate section 1, a smoothing section 2 and a prediction section 3.
  • the tracking gate unit 1 is realized by, for example, a tracking gate circuit 11 shown in FIG.
  • the tracking gate unit 1 acquires observation position information indicating the observation position of the tracking target from a target observation device (not shown), and acquires a predicted value of the state of the tracking target from the prediction unit 3 .
  • the predicted value indicates the predicted result of the state of the tracking target at the position observation time, which is predicted one sampling before the position observation time when the observation position is observed.
  • the tracking gate unit 1 calculates the difference between the observed position of the tracking target indicated by the observation position information and the predicted position of the tracking target included in the predicted value.
  • Tracking gate unit 1 outputs observed position information to smoothing unit 2 if the difference between the observed position and the predicted position is equal to or less than a threshold, and discards the observed position information if the difference is greater than the threshold.
  • the threshold may be stored in the internal memory of the tracking gate section 1, or may be given from the outside of the target tracking device shown in FIG.
  • the smoothing unit 2 is implemented by, for example, a smoothing circuit 12 shown in FIG.
  • the smoothing unit 2 acquires observation position information from the tracking gate unit 1 and acquires predicted values from the prediction unit 3 .
  • the smoothing unit 2 estimates the state of the tracking target at the time of position observation from the observed position information and the predicted value.
  • the smoothing unit 2 outputs the estimated value of the state of the tracking target to the outside, and outputs the estimated value of the state of the tracking target to the prediction unit 3 .
  • the prediction unit 3 is implemented by, for example, a prediction circuit 13 shown in FIG.
  • the prediction unit 3 includes N prediction processing units 4-1 to 4-N and a prediction value integration unit 5.
  • FIG. N is an integer of 1 or more.
  • the prediction unit 3 acquires the estimated value of the state of the tracking target from the smoothing unit 2 .
  • the prediction unit 3 uses a motion model of the tracked target that includes an acceleration term related to changes in the direction of movement of the tracked target, and uses the motion model estimated by the smoothing unit 2 as the motion model of the tracked target. Predict the state of the tracking target.
  • the prediction unit 3 outputs predicted values of the state of the tracking target to the tracking gate unit 1 and the smoothing unit 2, respectively.
  • the motion models provided in each of the prediction processing units 4-1 to 4-N are different motion models.
  • the motion model of the tracked target includes terms related to gravity and aerodynamic forces, and an acceleration term related to changes in the direction of movement of the tracked target.
  • the motion model of the tracked target only needs to include acceleration terms related to changes in the direction of movement of the tracked target, and does not need to include terms related to gravity and aerodynamic force.
  • the prediction processing unit 4-n uses the motion model to predict the state of the tracking target after one sampling from the position observation time from the state estimated by the smoothing unit 2, and predicts the predicted value of the state of the tracking target. Output to the value integration unit 5 .
  • the prediction processing section 4-n has a motion model. However, this is only an example, and the motion model may be given from outside the target tracking device.
  • the predicted value integration unit 5 acquires predicted values from each of the prediction processing units 4-1 to 4-N.
  • the predicted value integration unit 5 integrates the N predicted values and outputs the integrated predicted values to the tracking gate unit 1 and the smoothing unit 2, respectively.
  • each of the tracking gate section 1, the smoothing section 2, and the prediction section 3, which are components of the target tracking device is implemented by dedicated hardware as shown in FIG. That is, it is assumed that the target tracking device is realized by the tracking gate circuit 11, the smoothing circuit 12 and the prediction circuit 13.
  • FIG. Each of the tracking gate circuit 11, the smoothing circuit 12 and the prediction circuit 13 is, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate). Array), or a combination thereof.
  • the components of the target tracking device are not limited to those implemented by dedicated hardware, and the target tracking device may be implemented by software, firmware, or a combination of software and firmware.
  • Software or firmware is stored as a program in a computer's memory.
  • a computer means hardware that executes a program, for example, a CPU (Central Processing Unit), a central processing unit, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, a processor, or a DSP (Digital Signal Processor). do.
  • FIG. 3 is a hardware configuration diagram of a computer when the target tracking device is implemented by software, firmware, or the like.
  • the memory 31 stores a program for causing a computer to execute respective processing procedures in the tracking gate section 1, the smoothing section 2, and the prediction section 3.
  • the processor 32 of the computer executes the program stored in the memory 31 .
  • FIG. 2 shows an example in which each component of the target tracking device is implemented by dedicated hardware
  • FIG. 3 shows an example in which the target tracking device is implemented by software, firmware, or the like.
  • this is only an example, and some of the components in the target tracking device may be implemented by dedicated hardware, and the remaining components may be implemented by software, firmware, or the like.
  • the state vector x k indicating the state of the tracking target is, for example, position vectors x k , y k , and z k of the tracking target, velocity vectors x dot k , y dot It is represented by k , zdot k and aerodynamic coefficients ⁇ , ⁇ , ⁇ . k is an integer of 1 or more.
  • the state vector xk and the position vector xk are represented by the same xk , but the position vector xk is a scalar.
  • the position vectors y k and z k are also scalars.
  • Each of the position vectors xk , yk , and zk represents, for example, the three-dimensional position of the tracking target in the north-based orthogonal coordinate system.
  • the observation vector zk indicating the observation position information given to the tracking gate unit 1 from the target observation device is a vector indicating the three-dimensional observation position of the tracking target as shown in the following equation (2),
  • Each of the elements x k , y k , and z k of the observation vector z k is a scalar.
  • Equation (3) wk is a driving noise vector, and has properties shown in Equations (4) and (5) below.
  • Equation (3) f(x k ) is expressed using aerodynamic coefficients ⁇ , ⁇ , ⁇ as shown in Equation (6) below.
  • Q k is the drive noise error covariance matrix.
  • equation (5a) 0 m ⁇ n is an m-by-n matrix of zeros
  • Q k pv is a drive noise matrix parameter related to position and velocity
  • Q ⁇ , Q ⁇ , and Q ⁇ are drive coefficients related to aerodynamic coefficients.
  • diag[A,B,C] is a diagonal matrix with A,B,C as elements. In this example, there are 3 rows and 3 columns.
  • Equation (6) ⁇ is the universal gravitational constant, ⁇ is the air density, r is the distance, and V is the velocity.
  • Equation (3) can be expressed in a discrete system as shown in Equation (7) below.
  • the motion model used in the prediction processing unit 4- n (n 1, . -1 each.
  • Equation (9) u k is a constant acceleration vector included in the N motion models at sampling time t k and is represented by Equation (10) below.
  • ⁇ ′ k ⁇ 1 is a transformation matrix of constant acceleration vectors at sampling time t k ⁇ 1 and is represented by the following equation (11).
  • F k is a coordinate transformation matrix from the coordinate system defining the constant acceleration vector u k to the coordinate system defining the state vector x k .
  • I m ⁇ n is a unit matrix of m rows and n columns.
  • FIG. 4 is an explanatory diagram showing a constant acceleration vector.
  • O is the origin of the coordinate O-xyz with the target observation device as the origin
  • X is the x-axis of the coordinate O-xyz with the east direction as the positive x-axis
  • Y is the north direction as the y-axis.
  • Z is the z-axis of the coordinates O-xyz with the upward direction being the positive direction of the z-axis.
  • ⁇ 1 is a constant acceleration vector in the positive direction of the y-axis
  • ⁇ 2 is a constant acceleration vector in the negative direction of the y-axis.
  • ⁇ 3 is a constant acceleration vector in the positive x-axis direction
  • ⁇ 4 is a constant acceleration vector in the negative x-axis direction
  • ⁇ 5 is a constant acceleration vector in the positive z-axis direction
  • One axis of constant acceleration can be the north direction of a north-based orthogonal coordinate system, an estimated velocity vector, LOS (Line Of Sight), or the like.
  • LOS is a line-of-sight vector when the tracking target is viewed from the target observation device.
  • the transition of the motion model ⁇ k,a is Markovian. That is, the motion model ⁇ k,a is determined by the motion model ⁇ k ⁇ 1 ,a at the sampling time t k ⁇ 1 and does not depend on the motion model ⁇ k ⁇ 2,a etc. up to the sampling time t k ⁇ 2. and
  • the element of a row and b column is represented by p ab
  • the inter-motion-model transition probability matrix ⁇ k at the sampling time t k is represented by the following equation (15).
  • the element in row a, column b of the inter-motion model transition probability matrix ⁇ k is the inter-motion model transition probability p k,ab .
  • Equation (17) v k,a is the normal distribution approximation P[Z
  • Equation (20) The prior reliability ⁇ k,a ( ⁇ ) of the motion model ⁇ k,a at sampling time t k based on observation information Z k ⁇ 1 up to sampling time t k ⁇ 1 is given by the following equation (19):
  • Z k ⁇ 1 ] When defined by the conditional probability density function P[ ⁇ k,a
  • the estimated value u k ⁇ 1 (+) of the constant acceleration vector based on the observation information Z k up to the sampling time t k is defined by the following equation (21), and is expressed by the following equation (22): can.
  • the estimated value u k ⁇ 1 (+) of the constant acceleration vector is hereinafter referred to as an estimated acceleration vector.
  • the estimated value u k-1 (-) of the constant acceleration vector based on the observation information Z k-1 up to the sampling time t k-1 is defined by the following formula (23), and the following formula (24) can be expressed as
  • the estimated value u k ⁇ 1 ( ⁇ ) of the constant acceleration vector is hereinafter referred to as a predicted acceleration vector.
  • Equation (25) The observation model Z k for the observation vector z k shown in Equation (2) is expressed as in Equation (25) below.
  • H is an observation matrix at sampling time tk , as shown in Equation (26) below.
  • v k is an observation noise vector corresponding to the observation vector z k at sampling time t k
  • the observation noise vector is three-dimensional normally distributed white noise with an average of 0, as shown in Equation (27) below. be.
  • R k is the observed noise covariance matrix at sampling time t k and is a value that does not depend on the motion model.
  • the driving noise vector and the observation noise vector are independent of each other.
  • Equation (25) the estimated value X k (+) of the state vector x k when the observed value is obtained at the sampling time t k according to the observation model shown in Equation (25) is expressed by the following Equations (30) to (38).
  • X k,a ( ⁇ ) is the conditional average value of X k based on the observation information Z k ⁇ 1 up to the sampling time t k ⁇ 1 and the motion model ⁇ k,a .
  • the average value corresponds to a prediction vector obtained by estimating the true value at the sampling time t k based on the observation information Z k ⁇ 1 up to the sampling time t k ⁇ 1 and the motion model ⁇ k,a .
  • X k,a (+) is the conditional average value of X k based on the observation information Z k up to sampling time t k and the motion model ⁇ k,a .
  • the average value corresponds to a smoothed vector obtained by estimating the true value at the sampling time tk based on the observation information Zk up to the sampling time tk and the motion model ⁇ k ,a .
  • X k ( ⁇ ) is the conditional average value of X k based on observation information Z k ⁇ 1 up to sampling time t k ⁇ 1 .
  • the average value corresponds to a prediction vector obtained by estimating the true value at sampling time t k based on observation information Z k ⁇ 1 up to sampling time t k ⁇ 1.
  • X k (+) is a conditional mean value of X k based on observation information Z k up to sampling time t k .
  • the average value corresponds to a smoothed vector obtained by estimating the true value at sampling time tk based on observation information Zk up to sampling time tk .
  • the characters (X k, a (-), X k, a (+), X k (-), X k (+)) have " ⁇ " Since symbols cannot be attached, they are expressed as X hat k, a (-), X hat k, a (+), X hat k (-), X hat k (+).
  • P k,a ( ⁇ ) is a prediction error covariance matrix for each motion model that indicates the error covariance matrix of X k,a ( ⁇ ).
  • P k,a (+) is a smoothed error covariance matrix for each motion model indicating the error covariance matrix of X hat k,a (+).
  • P k ( ⁇ ) is the prediction error covariance matrix by the N motion models indicating the error covariance matrix of X k ( ⁇ ).
  • P k (+) is the smoothed error covariance matrix due to the N motion models representing the error covariance matrix of X k (+).
  • K k shown in Equation (34) is a gain matrix at sampling time t k .
  • each of the initial value X 0 (+) and the initial value P 0 (+) shall be determined separately. Since P k,a ( ⁇ ) is a value that does not depend on the motion model ⁇ k,a as shown in Equation (31), K k shown in Equation (34) and P k, shown in Equation (36) Each of a (+) is also a value that does not depend on the motion model ⁇ k,a .
  • FIG. 5 is a flow chart showing a target tracking method, which is a processing procedure of the target tracking device.
  • the prediction processing unit 4-n obtains from the smoothing unit 2 an estimated value of the state of the tracking target at the time of position observation.
  • the prediction processing unit 4-n uses the motion model ⁇ k,a to predict the state of the tracking target after one sampling from the point of position observation from the estimated value of the state of the tracking target (step ST1 in FIG. 5). .
  • the prediction processing unit 4-n outputs the predicted value of the state of the tracking target to the predicted value integration unit 5.
  • the tracking target state prediction processing by the prediction processing unit 4-n will be specifically described below.
  • the prediction processing unit 4-n receives from the smoothing unit 2 the smoothed vector X k,a (+) or the smoothed vector X k (+) and the smoothed error covariance matrix P k as estimated values of the state of the tracking target. (+) and get.
  • the prediction processing unit 4-n substitutes the smoothed vector X k, a (+) or the smoothed vector X k (+) into the equation (30) as x k ⁇ 1 (+) shown in the equation (30).
  • a prediction vector X k,a ( ⁇ ) is calculated as a prediction value of the state of the tracking target.
  • the prediction processing unit 4-n substitutes the smoothed error covariance matrix P k (+) into the equation (31) as P k ⁇ 1 (+) shown in the equation (31), thereby obtaining the prediction error covariance Calculate the matrix P k,a (-).
  • the prediction processing unit 4-n outputs the prediction vector X k,a (-) and the prediction error covariance matrix P k,a (-) to the prediction value integration unit 5, respectively.
  • the predicted value integrating unit 5 acquires N predicted vectors X k, a ( ⁇ ) as predicted values of the state of the tracking target from the prediction processing units 4-1 to 4-N, and obtains N predicted vectors Integrate X hat k, a (-) (step ST2 in FIG. 5).
  • Prediction vector X k (-) which is the predicted value after integration, is x k ⁇ 1 (+) shown in equation (32), smoothed vector X k, a (+) or smoothed vector X k (+ ) into equation (32).
  • the prediction value integration unit 5 outputs the prediction vector X k,a ( ⁇ ) to the smoothing unit 2 and outputs the prediction vector X k ( ⁇ ) to the tracking gate unit 1 and the smoothing unit 2, respectively.
  • the prediction value integration unit 5 acquires N prediction error covariance matrices P k,a ( ⁇ ) from the prediction processing units 4-1 to 4-N, and obtains N prediction error covariance matrices P k , a ( ⁇ ). That is, the prediction value integrating unit 5 substitutes the prediction error covariance matrix P k,a (-) into Equation (33) to calculate the prediction error covariance matrix P k (-) after integration.
  • the prediction value integration unit 5 outputs the prediction error covariance matrix P k ( ⁇ ) to the smoothing unit 2 .
  • the tracking gate unit 1 acquires an observation vector zk from a target observation device (not shown) as observation position information indicating the observation position of the tracking target. Further, the tracking gate unit 1 acquires a prediction vector X k ⁇ 1 ( ⁇ ) from the prediction value integration unit 5 as a prediction value predicted one sampling before the position observation time. The tracking gate unit 1 detects the observed position (x k , y k , z k ) of the tracking target that is an element of the observation vector z k and the predicted position of the tracking target that is an element of the prediction vector X k ⁇ 1 ( ⁇ ). Calculate the difference between If the difference between the observed position and the predicted position is equal to or less than the threshold (step ST3 in FIG.
  • the tracking gate unit 1 outputs the observation vector zk to the smoothing unit 2 (step ST4 in FIG. 5). . If the difference between the observed position and the predicted position is greater than the threshold (step ST3 in FIG. 5: NO), the tracking gate unit 1 discards the observation vector zk (step ST5 in FIG. 5).
  • the smoothing unit 2 acquires the observation vector z k from the tracking gate unit 1, and from the prediction value integration unit 5, the prediction vector X k ⁇ 1 ( -).
  • the smoothing section 2 estimates the state of the tracking target from the observation vector z k and the prediction vector X k ⁇ 1 ( ⁇ ) (step ST6 in FIG. 5).
  • the smoothing unit 2 outputs the smoothed vector X k, a (+) or the smoothed vector X k (+) and the smoothed error covariance matrix P k (+) as estimated values of the state of the tracking target to the outside. do.
  • the smoothing unit 2 also outputs the smoothed vector X k,a (+) or the smoothed vector X k (+) and the smoothed error covariance matrix P k (+) to the prediction unit 3 .
  • the tracking target state estimation processing by the smoothing unit 2 will be specifically described below.
  • the smoothing unit 2 substitutes the observation information Z k up to the sampling time t k into Equation (16) to obtain the reliability ⁇ k,a (+) of the motion model ⁇ k,a at the sampling time t k .
  • the smoothing unit 2 calculates the estimated acceleration vector u k ⁇ 1 (+) by substituting the reliability ⁇ k,a (+) into the equation (22).
  • the smoothing unit 2 acquires the prediction error covariance matrix P k,a ( ⁇ ) from the prediction value integrating unit 5 .
  • the smoothing unit 2 calculates the gain matrix K k by substituting each of the prediction error covariance matrix P k,a ( ⁇ ) and the observation noise covariance matrix R k into Equation (34).
  • the smoothing unit 2 acquires the predicted vector X k,a (-) and the predicted vector X k (-) from the predicted value integration unit 5 .
  • the smoothing unit 2 calculates the smoothed vector X k,a (+) by substituting each of the observation information Z k and the prediction vector X k,a (-) into Equation (35). Further, the smoothing unit 2 substitutes the predicted vector X k (-) into the equation (37) as x k-1 (-) shown in the equation (37), and the estimated acceleration vector u k-1 (+ ) into equation (37), the smoothed vector X k (+) is calculated.
  • the smoothing unit 2 substitutes each of the prediction error covariance matrix P k,a ( ⁇ ) and the gain matrix K k into Equation (36) to obtain the smoothed error covariance matrix P k,a (+)
  • the smoothing unit 2 calculates the smoothed error covariance matrix P k (+) by substituting each of the smoothed error covariance matrix P k,a (+) and the gain matrix K k into Equation (38). .
  • the target tracking device shown in FIG. 1 repeats the processing of steps ST1 to ST6 until the tracking of the tracking target is completed.
  • the observation position information indicating the observation position of the tracking target is acquired, and the position observation time predicted one sampling before the position observation time when the observation position is observed.
  • a smoothing unit 2 that acquires the predicted value of the state of the tracked target, estimates the state of the tracked target at the time of position observation from the observed position information and the predicted value, and a motion model of the tracked target that changes the direction of movement of the tracked target. Predict the state of the tracking target one sampling after the time of position observation from the state estimated by the smoothing unit 2 using the motion model including the acceleration term related to the smoothing unit 2
  • the target tracking device is configured so as to include a prediction unit 3 that outputs to. Therefore, the target tracking device can predict the state of the tracked target in consideration of the movement direction of the tracked target.
  • Embodiment 2 describes a target tracking device in which the smoothing unit 6 corrects the acceleration term included in the motion model from the observed position information and the predicted value predicted one sampling before the position observation time. do.
  • FIG. 6 is a configuration diagram showing a target tracking device according to Embodiment 2. As shown in FIG. In FIG. 6, the same reference numerals as in FIG. 1 denote the same or corresponding parts, so the description is omitted.
  • FIG. 7 is a hardware configuration diagram showing hardware of the target tracking device according to the second embodiment. In FIG. 7, the same reference numerals as those in FIG. 2 denote the same or corresponding parts, so description thereof will be omitted.
  • the target tracking device shown in FIG. 6 includes a tracking gate section 1, a smoothing section 6 and a prediction section 7.
  • the smoothing unit 6 is implemented by, for example, a smoothing circuit 14 shown in FIG. Similar to the smoothing unit 2 shown in FIG.
  • the predicted value indicates the predicted result of the state of the tracking target at the position observation time, which is predicted one sampling before the position observation time when the observation position is observed. Similar to the smoothing unit 2 shown in FIG. 1, the smoothing unit 6 estimates the state of the tracking target at the time of position observation from the observed position information and the predicted value.
  • a smoothing unit 6 corrects the acceleration term included in the motion model from the observed position information and the predicted value.
  • the smoothing unit 6 outputs an estimated value of the state of the tracking target to the outside.
  • the smoothing unit 6 outputs the estimated value of the state of the tracking target and the corrected acceleration term to the prediction unit 7 .
  • the prediction unit 7 is implemented by, for example, a prediction circuit 15 shown in FIG.
  • the prediction unit 7 includes N prediction processing units 4 - 1 to 4 -N and a prediction value integration unit 8 .
  • the prediction unit 7 acquires the estimated value of the tracking target state and the corrected acceleration term from the smoothing unit 6 .
  • the prediction unit 7 predicts the state of the tracking target one sampling after the position observation time from the state estimated by the smoothing unit 6 using the motion model of the tracking target including the corrected acceleration term.
  • the prediction unit 3 outputs predicted values of the state of the tracking target to the tracking gate unit 1 and the smoothing unit 6, respectively.
  • the predicted value integration unit 8 acquires predicted values from each of the prediction processing units 4-1 to 4-N.
  • the predicted value integration unit 8 integrates the N predicted values, and outputs the integrated predicted values to the tracking gate unit 1 and the smoothing unit 6, respectively.
  • each of the tracking gate unit 1, the smoothing unit 6, and the prediction unit 7, which are components of the target tracking device, is implemented by dedicated hardware as shown in FIG. That is, it is assumed that the target tracking device is realized by the tracking gate circuit 11, the smoothing circuit 14 and the prediction circuit 15.
  • FIG. Each of the tracking gate circuit 11, the smoothing circuit 14 and the prediction circuit 15 may be, for example, a single circuit, multiple circuits, programmed processors, parallel programmed processors, ASICs, FPGAs, or combinations thereof. .
  • the components of the target tracking device are not limited to those implemented by dedicated hardware, and the target tracking device may be implemented by software, firmware, or a combination of software and firmware. .
  • the target tracking device is realized by software, firmware, etc.
  • a program for causing a computer to execute respective processing procedures in the tracking gate section 1, the smoothing section 6, and the prediction section 7 is stored in the memory 31 shown in FIG. be.
  • the processor 32 shown in FIG. 3 executes the program stored in the memory 31 .
  • FIG. 7 shows an example in which each component of the target tracking device is implemented by dedicated hardware
  • FIG. 3 shows an example in which the target tracking device is implemented by software, firmware, or the like.
  • this is only an example, and some of the components in the target tracking device may be implemented by dedicated hardware, and the remaining components may be implemented by software, firmware, or the like.
  • the target tracking apparatus is the same as that shown in FIG. 1, so only the operations of the smoothing section 6 and the predicting section 7 will be described here.
  • the smoothing unit 6 acquires the observation vector z k as observation position information from the tracking gate unit 1 and acquires the prediction vector X k ( ⁇ ) from the prediction value integration unit 8 .
  • the smoothing unit 6 substitutes the prediction vector X k (-) into the equation (47) as x k (-) shown in the following equation (47), and substitutes the observed vector z k into the equation (47). to calculate the innovation vector ⁇ k .
  • the smoothing unit 6 acquires the prediction error covariance matrix P k ⁇ 1 ( ⁇ ) from the prediction value integrating unit 8 .
  • the smoothing unit 6 substitutes the prediction error covariance matrix P k-1 (-) into the equation (48) as P tilde k-1 (-) shown in the following equation (48), and converts the innovation vector ⁇ k into the equation By substituting into (48), the prediction residual covariance matrix V k is calculated.
  • P tilde k-1 (-) since it is not possible to add the symbol " ⁇ " above the character P k-1 (-) due to the electronic application, it is written as P tilde k-1 (-). ing.
  • the smoothing unit 6 substitutes the prediction error covariance matrix P k-1 (-) into the equation (49) as P tilde k -1 (-) shown in the following equation (49), so that the predicted position Calculate the error covariance matrix Mk .
  • the smoothing unit 6 calculates a prediction residual covariance matrix N k by subtracting the prediction position error covariance matrix M k from the prediction residual covariance matrix V k as shown in the following equation (50). .
  • the smoothing unit 6 is a pair of M (x, y, z), which is the diagonal component of the prediction position error covariance matrix M k , and the prediction residual covariance matrix N k Correction coefficients ⁇ k (x, y , z).
  • the smoothing unit 6 uses the correction coefficient ⁇ k (x, y, z) of the constant acceleration vector u k ⁇ 1 ( ⁇ ) to convert the constant acceleration vector u k ⁇ 1 ( -) is corrected.
  • u k '(-) is the corrected constant acceleration vector.
  • the prediction unit 7 predicts the state of the tracking target from the state estimated by the smoothing unit 6 using the motion model of the tracking target including the corrected constant acceleration vector u k '(-).
  • the predicted value integration unit 8 of the prediction unit 7 acquires N predicted vectors X k,a (-) as predicted values of the state of the tracking target from each of the prediction processing units 4-1 to 4-N. , N prediction vectors X k,a ( ⁇ ).
  • the predicted value integration unit 8 substitutes the corrected constant acceleration vector u k '(-) into the following equation (54), and converts the smoothed vector X By substituting hat k, a (+) or smooth vector X hat k (+) into equation (54), prediction vector X hat k (-), which is the predicted value after integration, is calculated. Further, the predicted value integration unit 8 substitutes the corrected constant acceleration vector u k '(-) into the following equation (55), and the prediction error output from each of the prediction processing units 4-1 to 4-N By substituting the covariance matrix P k,a (-) into Equation (55), the post-integration prediction error covariance matrix P k (-) is calculated.
  • the smoothing unit 6 corrects the acceleration term included in the motion model from the observed position information and the predicted value of the state of the tracking target at the time of position observation, as shown in FIG.
  • the target tracking device shown was constructed. Therefore, the target tracking device shown in FIG. 6 can predict the state of the tracked target in consideration of the movement direction of the tracked target in the same way as the target tracking device shown in FIG. can also improve the estimation accuracy of the smoothing unit 2 .
  • the present disclosure is suitable for target tracking devices and target tracking methods.

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)
PCT/JP2022/006289 2022-02-17 2022-02-17 目標追尾装置及び目標追尾方法 WO2023157157A1 (ja)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/JP2022/006289 WO2023157157A1 (ja) 2022-02-17 2022-02-17 目標追尾装置及び目標追尾方法
JP2023573317A JP7580637B2 (ja) 2022-02-17 2022-02-17 目標追尾装置及び目標追尾方法

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2022/006289 WO2023157157A1 (ja) 2022-02-17 2022-02-17 目標追尾装置及び目標追尾方法

Publications (1)

Publication Number Publication Date
WO2023157157A1 true WO2023157157A1 (ja) 2023-08-24

Family

ID=87577847

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2022/006289 WO2023157157A1 (ja) 2022-02-17 2022-02-17 目標追尾装置及び目標追尾方法

Country Status (2)

Country Link
JP (1) JP7580637B2 (enrdf_load_stackoverflow)
WO (1) WO2023157157A1 (enrdf_load_stackoverflow)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117951653A (zh) * 2024-01-31 2024-04-30 兰州理工大学 基于Student’s t过程回归的平滑跟踪方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS61169786A (ja) * 1985-01-22 1986-07-31 Mitsubishi Electric Corp 追尾フィルタ
JPS6473275A (en) * 1987-09-14 1989-03-17 Nec Corp Tracking apparatus for radar
US5313212A (en) * 1992-10-19 1994-05-17 Hughes Aircraft Company Track filter bias estimation
JPH09189761A (ja) * 1996-01-08 1997-07-22 Mitsubishi Electric Corp 目標追尾方法およびその方法を用いたレーダ装置
JP2005274300A (ja) * 2004-03-24 2005-10-06 Mitsubishi Electric Corp 目標追尾装置

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH06347542A (ja) * 1993-06-08 1994-12-22 Nec Corp 目標追尾装置
JP3148120B2 (ja) * 1996-05-01 2001-03-19 三菱電機株式会社 追尾装置
JP3388180B2 (ja) * 1998-04-24 2003-03-17 三菱電機株式会社 目標追尾装置および目標追尾方法
JP3659575B2 (ja) * 2001-05-01 2005-06-15 三菱電機株式会社 目標追尾装置
JP3970585B2 (ja) * 2001-11-26 2007-09-05 三菱電機株式会社 目標追尾装置及び方法
JP5230132B2 (ja) * 2007-07-06 2013-07-10 三菱電機株式会社 目標追尾装置及び目標追尾方法
JP5709651B2 (ja) * 2011-06-03 2015-04-30 三菱電機株式会社 追尾装置
JP6041547B2 (ja) * 2012-06-08 2016-12-07 三菱電機株式会社 追尾装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPS61169786A (ja) * 1985-01-22 1986-07-31 Mitsubishi Electric Corp 追尾フィルタ
JPS6473275A (en) * 1987-09-14 1989-03-17 Nec Corp Tracking apparatus for radar
US5313212A (en) * 1992-10-19 1994-05-17 Hughes Aircraft Company Track filter bias estimation
JPH09189761A (ja) * 1996-01-08 1997-07-22 Mitsubishi Electric Corp 目標追尾方法およびその方法を用いたレーダ装置
JP2005274300A (ja) * 2004-03-24 2005-10-06 Mitsubishi Electric Corp 目標追尾装置

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
WATSON G. A., BLAIR, W. D.: "Interacting Acceleration Compensation Algorithm For Tracking Maneuvering Targets", IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, vol. 31, no. 3, 1 July 1995 (1995-07-01), pages 1152 - 1159, XP093085577, DOI: 10.1109/7.395225 *
ZENG JIANGHUI; GAO YONGMING; DING DAN: "Positioning and Tracking Performance Analysis of Hypersonic Vehicle based on Aerodynamic Model", 2019 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND COMPUTER APPLICATIONS (ICAICA), IEEE, 29 March 2019 (2019-03-29), pages 312 - 318, XP033633332, DOI: 10.1109/ICAICA.2019.8873439 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117951653A (zh) * 2024-01-31 2024-04-30 兰州理工大学 基于Student’s t过程回归的平滑跟踪方法

Also Published As

Publication number Publication date
JPWO2023157157A1 (enrdf_load_stackoverflow) 2023-08-24
JP7580637B2 (ja) 2024-11-11

Similar Documents

Publication Publication Date Title
CN114013449B (zh) 针对自动驾驶车辆的数据处理方法、装置和自动驾驶车辆
CN106352876B (zh) 一种基于h∞和ckf混合滤波的机载分布式pos传递对准方法
CN107110650B (zh) 在能观测性方面受约束的导航状态的估计方法
US8577539B1 (en) Coded aperture aided navigation and geolocation systems
WO2019114807A1 (zh) 多传感器目标信息融合
CN112285676A (zh) 激光雷达与imu外参标定方法及装置
CN109059907B (zh) 轨迹数据处理方法、装置、计算机设备和存储介质
JP7173471B2 (ja) ３次元位置推定装置及びプログラム
CN111623773B (zh) 一种基于鱼眼视觉和惯性测量的目标定位方法及装置
JP2011064484A (ja) センサバイアス推定装置
CN109631894A (zh) 一种基于滑动窗口的单目视觉惯性紧耦合方法
JP2014185996A (ja) 計測装置
CN108458709B (zh) 基于视觉辅助测量的机载分布式pos数据融合方法和装置
CN111238469A (zh) 一种基于惯性/数据链的无人机编队相对导航方法
JP4145451B2 (ja) ハイブリッド航法およびその装置
WO2023157157A1 (ja) 目標追尾装置及び目標追尾方法
CN117739972A (zh) 一种无全球卫星定位系统的无人机进近阶段定位方法
JP2019082328A (ja) 位置推定装置
CN114323007A (zh) 一种载体运动状态估计方法及装置
JP6041547B2 (ja) 追尾装置
CN114076946B (zh) 一种运动估计方法及装置
JP5487890B2 (ja) 傾斜角検出装置、傾斜角検出方法、および傾斜角検出プログラム
US20220375105A1 (en) Tracking in a moving platform
JP6861663B2 (ja) センサ装置、誤差検出方法および誤差検出プログラム
JP3302870B2 (ja) センサのバイアス誤差推定装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22927059

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2023573317

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22927059

Country of ref document: EP

Kind code of ref document: A1