JP2002090450A - Target tracking apparatus - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain a target tracking apparatus which enables the using thereof without modifying a tracking filter designed for the processing of observation values of a mechanically driven type radar by reducing coordination conversion processing for the shortening of tracking processing time. SOLUTION: This apparatus is provided with an observation value calculator 2 for calculating a three-dimensional observation value comprising the distance to a target and bidirectional cosine, a coordinate conversion device 3 for converting the observation value to a north reference orthogonal coordinate system, devices 9 and 10 which performs a calculation of a predicted vector and for a forecast error covariance matrix at the subsequent sampling time, delay means 14 and 15 which perform a single sampling delay for the predicted vector and the forecast error covariance matrix, a Karman gain matrix calculation means 4 and a smooth vector calculator 5 for calculating a smooth vector based on the predicted vector, a Karman gain matrix and the observation value and a smooth error covariance matrix calculator 6 which performs a calculation for a smooth error covariance matrix from the Karman gain matrix and the forecast error covariance matrix.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to a target tracking device, and more particularly, to a target tracking device based on target information observed in a phased array radar.

[0002]

2. Description of the Related Art FIGS.
FIG. 1 is a flowchart showing a processing procedure of a target tracking device using an observation value of a conventional phased array radar, and a block diagram showing a configuration, which is shown in Japanese Unexamined Patent Application Publication No. H11-209,004.

In FIG. 3, step S101 is an observation value calculation process, step S102 is a coordinate conversion process of the observation value into radar coordinates, step S103 is a Kalman gain matrix calculation process, step S104 is an observation approximation value calculation process, and step S105 is an observation value calculation process. Smooth vector calculation processing, step S1
06 is a smooth error covariance matrix calculation process, step S107
Is a coordinate conversion process of the smooth vector to the north reference rectangular coordinates, step S108 is a coordinate conversion process of the smooth error covariance matrix to the north reference rectangular coordinates, step S109 is a prediction vector calculation process, and step S110 is a prediction error covariance matrix calculation. Processing, step S111 is a coordinate conversion process of the prediction vector into the radar coordinates, step S112 is a coordinate conversion process of the prediction error covariance matrix into the radar coordinates, and step S113 is 1
This is a sampling delay process.

In FIG. 4, 101 is a radar device, 102 is an observation value calculation device, 103 is a first coordinate transformation device, 104 is a Kalman gain matrix calculation device, 105 is a smooth vector calculation device, and 106 is a smooth error A variance matrix calculation device, 107 is a second coordinate conversion device, 108 is a third coordinate conversion device, 109 is a predicted vector calculation device, 110
Is a prediction error covariance matrix calculation device, 111 is a fourth coordinate conversion device, 112 is a fifth coordinate conversion device, 113 is an observation approximate value calculation device, 114 is a first delay device, and 115 is a second delay device.
Of the delay device.

[0005] The operation of the thus configured target tracking device will be described with reference to Figs. Radar device 101
Is received by the observation value calculation device 102. The observation value calculation device 102 calculates four-dimensional observation values of the target distance, the azimuth information, the high angle information, and the distance change rate (step S101). The above observation value is input to the first coordinate conversion device 103. The first coordinate conversion device 103 converts the observation value into a radar coordinate system, and outputs it to the smoothed vector calculation device 105 (step S102). The Kalman gain calculation device 104 calculates a Kalman gain matrix based on the prediction error covariance matrix calculated at the previous sampling time and the observation accuracy information set in advance,
The output is output to the smoothing vector calculation device 105 and the smoothing error covariance matrix calculation device 106 (step S103). The smoothed vector calculation device 105 calculates a smoothed vector and outputs it to the second coordinate transformation device 107 (step S10).
4). The smoothing error covariance matrix calculating device 106 calculates a smoothing error covariance matrix and outputs it to the third coordinate transformation device 108 (step S105). Second coordinate conversion device 107
In step S10, the smoothed vector is converted to the north reference rectangular coordinate system and output to the predicted vector calculation device 109 (step S10).
6). The third coordinate conversion device 108 converts the smoothed error covariance matrix into the north reference orthogonal coordinate system, and outputs it to the prediction error covariance matrix calculation device 110 (step S107). The prediction error covariance matrix calculation device 110 calculates a prediction error covariance matrix and outputs it to the fifth coordinate transformation device 112 (steps S108 and S109). The fourth coordinate conversion device 111 converts the prediction vector into a radar coordinate system and outputs the result to the first delay device 114 (step S110). The first delay device 114 performs one sampling delay of the prediction vector, and outputs the result to the observation approximate value calculation device 113 (step S112). The observation approximate value calculation device 113 calculates the observation approximate value from the prediction vector, and
5 (step S113). The fifth coordinate conversion device 112 converts the prediction error covariance matrix into a radar coordinate system and outputs the result to the second delay device 115 (step S11).
1). The second delay device 115 performs one sampling delay of the prediction error covariance matrix and outputs the result to the smoothed error covariance matrix calculation device 106 and the Kalman gain calculation device 104 (step S112).

[0006]

In a conventional target tracking device using a phased array radar, the data is converted into a radar coordinate system at the time of smoothing processing (step S2), and is converted into a north reference orthogonal coordinate system at the time of prediction processing (step S6). , 7), a complicated coordinate transformation is required many times, and there is a problem that a tracking device designed to process observation values of a mechanical drive type radar cannot be used as it is.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and is intended to reduce the time required for tracking processing by reducing coordinate conversion processing and to process observation values of a machine-driven radar. It is an object of the present invention to obtain a target tracking device that can use a designed tracking filter without making any changes.

[0008]

SUMMARY OF THE INVENTION The present invention comprises radar means having a phased array antenna, a signal received by the radar means being input, and a distance to a target and two direction cosine based on the signal. Target observation means for calculating a three-dimensional observation value, coordinate conversion means for converting the observation value to a north reference rectangular coordinate system, prediction vector calculation means for calculating a prediction vector at the next sampling time, and prediction error at the next sampling time A prediction error covariance matrix calculating means for calculating a covariance matrix, a first delay means for delaying one sampling of a prediction vector output from the prediction vector calculating means, and a signal output from the prediction error covariance matrix calculating means. Second delay means for delaying one sampling of the prediction error covariance matrix, and a pre-sampler output from the second delay means Kalman gain matrix calculation means for calculating a Kalman gain matrix based on the prediction error covariance matrix at the time, Kalman gain calculated by the Kalman gain matrix calculation means at the previous sampling time outputted from the first delay means The gain matrix and the smoothed vector calculating means for calculating the smoothed vector based on the coordinate-transformed observation value output from the coordinate converting means and outputting the calculated smoothed vector to the predicted vector calculating means, and the Kalman gain matrix calculating means. A smoothing error covariance matrix calculation device that calculates a smoothing error covariance matrix from the Kalman gain matrix and the prediction error covariance matrix at the previous sampling time output from the second delay unit, and outputs the smoothing error covariance matrix to the prediction error covariance matrix calculation unit This is a target tracking device provided with:

Further, electronic scanning is performed in the elevation angle direction while mechanically rotating the antenna surface of the radar means in the azimuth direction.

Further, electronic scanning is performed in the horizontal direction while mechanically rotating the antenna surface of the radar means in the elevation direction.

A target is observed on the antenna axis of the radar means.

[0012]

DESCRIPTION OF THE PREFERRED EMBODIMENTS Embodiment 1 Hereinafter, a first embodiment of a target tracking device and a target tracking method according to the present invention will be described.

First, the definition of a coordinate system and target position observation information obtained by a phased array radar will be described. The origin is the radar position, the east is the positive x-axis, the north is the positive y-axis, the vertical coordinate perpendicular to the horizontal plane (xy plane) is the positive z-axis, and the north reference rectangular coordinates, and from the radar to the target. The distance R is called a polar coordinate, the elevation angle from the horizontal plane to the target is E, and the azimuth from the north to the target in the horizontal plane is By. A half line on the side irradiated with radio waves perpendicular to the antenna surface with the center of the antenna as a starting point is called an antenna axis. Next, the origin is the center of the antenna, the xA axis is positive on the right side of the antenna axis when viewed from the origin parallel to the horizontal plane in the antenna plane, and the yA axis is positive on the side on which the radio wave is irradiated on the antenna axis. The orthogonal coordinates perpendicular to the antenna axis and taking the positive direction of the zA axis upward are referred to as antenna plane orthogonal coordinates.

The parameters observed in the phased array radar are the distance to the target and two direction cosine obtained from a target position vector which is a vector connecting the antenna center and the target. Here, the parameters observed in the phased array radar are the distance R and the xA components l and z of the unit vector (l, m, n) on the target position vector.
A component is assumed to be n.

Here, a processing algorithm of a Kalman filter generally used as a tracking filter in a normal mechanical drive type radar will be described in detail.

The sampling time is set to t _k (k = 1, 2,
…). In the Kalman filter theory, first, a target motion model is expressed as in equation (1).

(Equation 1)

In the equation (1), x _k is an r-dimensional state vector representing the true value of the target motion data at the sampling time t _k , and Φ (t _k−1 , t _k ) is the sampling time from the sampling time t _k−1 it is a transition matrix of r * r, which represents the transition of the state vector x _k to the time t _k. Further, w _k is an s-dimensional driving noise vector at the sampling time t _k , and has an average of 0,
And a white noise sequence in accordance with s variate normal distribution covariance matrix Q _k. The driving noise excites the disturbance of the state vector when the actual target motion does not match the set motion model. Γ ₁ (t _k−1 , t _k ) is a drive noise vector conversion matrix, which is an r * s matrix.

For example, when tracking the position and speed of a target in the xyz north reference rectangular coordinate system, the equations (2) to (4) can be set as the motion model of equation (1). Here, the drive noise w _k is a three-dimensional vector representing a disturbance input corresponding to acceleration in each of the x, y, and z axes. In addition,
In _{* n} represents an n * n unit matrix.

[0019]

(Equation 2)

[0020]

(Equation 3)

[0021]

(Equation 4)

Next, an observation model of the observation device is expressed as follows.

[0023]

(Equation 5)

In equation (5), z_kIs for sampling
Time t_k, The u-dimensional observation vector at, where H is the u * n
It is an observation matrix. Also, v_kIs the sampling time t_kIn
Vector z_kU-dimensional observation noise vector corresponding to
Torr, mean 0, covariance matrix R_kFollows the u-variate normal distribution of
Let it be a white noise sequence. Note that the driving noise vector
w _kAnd the observation noise vector v_kAre independent of each other.

For example, based on the detection result of the observation device, x
When the target position in the yz north reference rectangular coordinate system is obtained as an observation value, the expression (6) is used as the observation model of expression (5)
And Equation (7).

[0026]

(Equation 6)

[0027]

(Equation 7)

Assuming that the standard deviations of the observation noise of x, y and z are σ _x (k), σ _y (k) and σ _z (k), R _k is given by the following equation.

[0029]

(Equation 8)

According to the mechanically driven radar, the observation information of the target position can be obtained in the polar coordinate system of the distance, the elevation angle and the azimuth angle. Therefore, the standard deviation of the observation noise v _{k, p} of the distance, the elevation angle and the azimuth angle is obtained. Are respectively σ _R (k), σ _E (k), and σ _By (k), and the observation error covariance matrix R _{k, p} is as follows.

[0031]

(Equation 9)

This is a transformation matrix 北₂ to the north reference rectangular coordinate system.
Can be converted to the observation error covariance matrix of the north reference rectangular coordinate system.

[0033]

(Equation 10)

[0034]

[Equation 11]

Here, (R, E, By) is a true value of the target position in the polar coordinate system.

The accumulation of the observation value vectors up to the sampling time t _k is represented as follows.

[0037]

(Equation 12)

According to the theory of the Kalman filter, when the observed value is obtained at the sampling time t _k according to the above model, the estimated value x _k of the state vector x _k is given by the following equation (13).
１７ (17). Here, it is assumed that Q _k is a target parameter and R _k is a sensor parameter. Note that T represents transposition of a matrix.

[0039]

(Equation 13)

[0040]

[Equation 14]

[0041]

(Equation 15)

[0042]

(Equation 16)

[0043]

[Equation 17]

Note that the estimated value x _k hat of the state vector x _k when no observation value is obtained at the sampling time t _k is calculated by the equations (18) to (21). This process is generally called extrapolation.

[0045]

(Equation 18)

[0046]

[Equation 19]

However,

(Equation 20)

[0048]

(Equation 21)

Here, x _k hat, x _{k tilde} , P _k hat, and P _{k tilde} are defined as in equations (22) to (25), respectively. Here, E [•] is a symbol representing the averaging operation. x _k hat is conditional mean value of x _k based on the observation information Z _k to the sampling time t _k, estimating the true value of the state vector of the sampling time t _k on the basis of the observation information Z _k to the sampling time t _k corresponds to the smoothed vector, x _k tilde is conditional mean value of x _k based on the observation information Z _k-1 to the sampling time t _k-1, observation information Z _k-1 to the sampling time t _k-1 corresponds to the prediction vector estimating the true value of the state vector at sampling time t _k based on. P _k hat and P _{k tilde} are a smoothed error variance matrix and a prediction error covariance matrix representing an error covariance matrix of x _k hat and x _k tilder, respectively. K _k in equation (16) is called a Kalman gain matrix, and u * n
It is a matrix. Note that the smoothed vector x _k hat calculated by the above equations (13) to (21) is an optimum estimated value under the observation information Z _{k in} the sense that the average of the estimation error is zero and the variance is minimized. Become.

[0050]

(Equation 22)

[0051]

(Equation 23)

[0052]

(Equation 24)

[0053]

(Equation 25)

FIG. 1 explains the operation procedure of the first embodiment of the target tracking device of the present invention, and FIG. 2 shows the configuration of the first embodiment of the target tracking device of the present invention.

In FIG. 2, 1 is a radar device, 2 is an observation value calculation device for calculating a three-dimensional observation value consisting of a distance to a target and two direction cosine, and 3 is a north reference rectangular coordinate system. A coordinate transformation device 4 for transforming is a Kalman gain matrix calculation device for calculating a Kalman gain matrix based on a prediction error covariance matrix calculated at the previous sampling time, and a prediction device 5 is also calculated at the previous sampling time. Vector, the Kalman gain matrix, and
A smoothing vector calculating device for calculating a smoothing vector based on the observed value; 6, a smoothing error covariance matrix calculating device for calculating a smoothing error covariance matrix from the Kalman gain matrix and the prediction error covariance matrix; Prediction vector calculation device for calculating a prediction vector at time, 10
Is a prediction error covariance matrix calculation device that similarly calculates a prediction error covariance matrix, 14 is a first delay device that performs one sampling delay of the prediction vector, and 15 is a one sampling delay of the prediction error covariance matrix. The second delay device.

Next, the operation will be described with reference to FIG. First, at a sampling time, a three-dimensional observation value R, l, n including a distance to a target and two direction cosine is input (step S1). This target position observation value (R, l, n)
Is converted to the position (x, y, z) in the north reference rectangular coordinate system (step S2). Here, the expression (x _A , y _A , z _A ) of the target position (R, l, n) in the antenna plane orthogonal coordinate system is as shown in Expression (26).

[0057]

(Equation 26)

The position (x _A ,
y _A , z _A ) to the position (x, y, z) in the north reference rectangular coordinate system
A matrix for conversion into へ is defined as Γ _{2, N} (Equation (27)).

[0059]

[Equation 27]

Here, the antenna surface is in the azimuth direction By _A ,
Suppose that it is facing the elevation direction EA. _Assuming that the matrices for correction in each direction are R _ByA and R _EA , respectively, Γ _{2, N} is given by equation (2)
It can be expressed as 8).

[0061]

[Equation 28]

[0062]

(Equation 29)

[0063]

[Equation 30]

That is, in step S2, the target observation value (R, l, n) is converted into the observation value z _k in the north reference rectangular coordinate system using equation (31).

[0065]

(Equation 31)

Next, in step S3, a Kalman gain matrix is obtained using equation (16). At this time, obtains a covariance matrix R _k of the observation error of the phased array _radar, the _PA as follows covariance matrix R _k of the observation error in the North reference orthogonal coordinate system.

[0067]

(Equation 32)

Here, the standard deviation of the observation noise v _{k, PA} of the distance R and the direction cosines l, n is _represented by σ _R (k), σ _l (k), σ
_{Assuming n} (k), the observation error row variance matrix R _{k, PA} is given by the following equation.

[0069]

[Equation 33]

[0070] is a gamma _{2, A} is the observed noise v _k of the distance and direction cosines by phased array _radar, observation noise v _k of the antenna plane orthogonal coordinate system _PA, transformation matrix to _A.

[0071]

(Equation 34)

Next, in step S4, the smoothed vector x _{k in the} target state is calculated by equation (14).

Next, in step S5, a smooth error covariance matrix P _k hat is calculated by equation (17).

Next, in step S6, a predicted vector x _{k + 1} tilder of the target state is calculated by equation (13).

Next, in step S7, a prediction error covariance matrix P _{k + 1} tilder is calculated by equation (15).

Next, in step S8, the prediction vector and the prediction error covariance matrix are delayed by one sampling. This series of steps is repeated until tracking ends.

As described above, according to the first embodiment, since the coordinate conversion device 3 converts the target observation value and the observation error into the north reference rectangular coordinate system, the conventional mechanical drive type radar is used. Therefore, the Kalman filter (tracking filter) designed in the north reference rectangular coordinate system can be used without any change.

Further, according to the first embodiment, since the number of coordinate conversion devices is reduced to one, an effect that the tracking processing can be executed at a high speed is achieved.

Embodiment 2 Hereinafter, a second embodiment of the target tracking device and the target tracking method according to the present invention will be described. The configuration of the device according to the second embodiment is the same as that of the first embodiment except for the radar device 1.

The radar apparatus 1 equipped with a phased array antenna scans the antenna surface mechanically in the azimuth direction while performing beam scanning in the elevation direction. That is, the target is captured directly above or below the antenna axis.

The processing procedure of the second embodiment is the same as that of the first embodiment except for the operation of step S3.

[0082] In the second embodiment, when calculating the Kalman gain matrix at step S3, the observation noise v _k, the observation error covariance matrix R _k of _PA, observation noise v _k of polar coordinate system the _{PA, p}
Into an observation error covariance matrix R _{k, p} .

Observation noise v _{k, PA of} phased array radar
Let 行列_{2, P} be a matrix that converts into polar observation noise v _{k, p} .

[0084]

(Equation 35)

[0085] Here, R _NP is observed noise v _k, transformation matrix for transforming the _p of the observation noise v _k in North reference orthogonal coordinate system in a polar coordinate system.

[0086]

[Equation 36]

Observation noise v _{k, PA of} phased array radar
Is the observation error covariance matrix R _{k, PA} of the observation noise v
_k, the observation error covariance matrix R _k of _p, can be converted in the following manner _p.

[0088]

(37)

For simplicity, the following equation is assumed assuming that the antenna plane is in a plane perpendicular to the horizontal plane.

[0090]

(38)

At this time,

[0092]

[Equation 39]

Therefore, the observation noise of the polar coordinate system is

[0094]

(Equation 40)

That is, in the target tracking device according to the second embodiment, the elevation angle observation error is a value obtained by dividing v _n (k) by cosE, and v _l (k) is the elevation angle E observation error.

As described above, according to the second embodiment, while the radar apparatus performs beam scanning in the elevation direction,
Since the antenna surface is moved mechanically in the azimuth direction, it is possible to search the entire surroundings. At this time, the observation error row variance matrix of the phased array radar is converted into a polar coordinate observation error covariance matrix, and a tracking filter designed assuming a conventional machine-driven radar can be used as it is.

Further, since the conversion of the observation noise is only correction by the elevation angle E, the calculation of the Kalman gain matrix is performed at high speed, and the tracking processing time is reduced. As a result, the surplus time can be allocated to search and tracking, and search and tracking performance can be improved.

Embodiment 3 Hereinafter, a third embodiment of the target tracking device and the target tracking method of the present invention will be described. The configuration of the device according to the third embodiment is the same as that of the first embodiment except for the radar device 1.

The radar apparatus 1 equipped with the phased array antenna, while mechanically rotating the antenna surface in the elevation direction,
Beam scanning is performed in the horizontal direction. That is, the target is captured right beside the antenna axis.

The processing procedure of the third embodiment is the same as that of the first embodiment except for the operation of step S3.

[0102] In the third embodiment, when calculating the Kalman gain matrix at step S3, the observation using the equation (37) noise v _k, the observation error covariance matrix R _k of _PA, observation noise in the polar coordinate system with _PA v _{k, p} is converted to an observation error covariance matrix R _{k, p} .

For simplicity, the following equation is assumed assuming that the antenna plane is in a plane perpendicular to the horizontal plane.

[0103]

[Equation 41]

At this time,

[0105]

(Equation 42)

Therefore, the observation noise of the polar coordinate system is

[0107]

[Equation 43]

That is, in the target tracking apparatus according to the third embodiment, the observation error of the elevation angle is v _n (k), and the observation error of the azimuth angle is a value obtained by dividing v _l (k) by cos (By−By _A ). Becomes

As described above, according to the third embodiment, while the radar apparatus performs beam scanning in the azimuth direction,
Since the antenna surface is moved mechanically in the elevation direction, a specific azimuth direction can be intensively searched. At this time, the observation error row variance matrix of the phased array radar is converted into a polar coordinate observation error covariance matrix, and a tracking filter designed assuming a conventional machine-driven radar can be used as it is. By arranging several such antennas in a round shape facing outward, it is possible to search the entire area at high speed.

The conversion of the observation noise is based on the azimuth (By-B
Since only the correction based on y _A ) is performed, the calculation of the Kalman gain matrix is performed at high speed, and the tracking processing time is reduced. As a result, the surplus time can be allocated to search and tracking, and search and tracking performance can be improved.

Embodiment 4 Hereinafter, a fourth embodiment of the target tracking device and the target tracking method of the present invention will be described. The configuration of the device according to the fourth embodiment is the same as that of the first embodiment except for the radar device 1.

It is assumed that the radar apparatus 1 including the phased array antenna is configured to observe a target on the antenna axis.

The processing procedure of the fourth embodiment is the same as that of the first embodiment except for the operation of step S3.

When a target is observed on the antenna axis,

[0115]

[Equation 44]

Therefore, the observation noise of the polar coordinate system is

[0117]

[Equation 45]

That is, in the target tracking device according to the fourth embodiment, the observation error of the elevation angle is v _n (k) and v _l (k) is the observation error of the elevation angle E.

That is, in the fourth embodiment, step S3
When calculating the Kalman gain matrix in
v_{k, PA}The observation error covariance matrix R_{k, PA}Is the observation coordinate of the polar coordinate system.
Sound v _{k, p}The observation error covariance matrix R_{k, p}May be considered.

As described above, according to the fourth embodiment, the observation error row variance matrix of the phased array radar can be used as it is as the polar coordinate observation error covariance matrix, and the conventional mechanically driven radar can be used. The tracking filter designed assuming can be used as it is.

Further, since the observation noise of the phased array radar may be regarded as the observation noise of the polar coordinates, the conversion of the observation noise does not need to be performed. Therefore, the calculation of the Kalman gain matrix is performed at high speed, and the tracking processing time is reduced. This allows
The surplus time can be allocated to search and tracking to improve search and tracking performance.

[0122]

According to the present invention, three-dimensional observation comprising a radar means having a phased array antenna, a signal received by the radar means, and a distance to a target and two direction cosine based on the signal. Target observation means for calculating a value, coordinate conversion means for converting the observed value to the north reference rectangular coordinate system, prediction vector calculation means for calculating a prediction vector at the next sampling time, and a prediction error covariance matrix at the next sampling time. Means for calculating a prediction error covariance matrix to be calculated, first delay means for delaying one sampling of a prediction vector output from the prediction vector calculation means, and the prediction error covariance output from the prediction error covariance matrix calculation means Second delay means for delaying one sampling of the matrix, and A Kalman gain matrix calculation means for calculating a Kalman gain matrix based on the measurement error covariance matrix,
A smooth vector based on the predicted vector at the previous sampling time output from the first delay means, the Kalman gain matrix calculated by the Kalman gain matrix calculation means, and the coordinate-transformed observation value output from the coordinate conversion means And a smoothing vector calculating means for outputting to the prediction vector calculating means, a smoothing from the Kalman gain matrix calculated by the Kalman gain matrix calculating means and the prediction error covariance matrix at the previous sampling time outputted from the second delay means. The target tracking device includes a smoothing error covariance matrix calculation device that calculates an error covariance matrix and outputs the error covariance matrix to the prediction error covariance matrix calculation means. Can be shortened and designed to handle machine-driven radar observations It can be used without changing the tail filter.

Also, since the electronic scanning is performed in the elevation direction while mechanically rotating the antenna surface of the radar means in the azimuth direction, it is possible to search over the entire periphery.

Further, since the electronic scanning is performed in the horizontal direction while mechanically rotating the antenna surface of the radar means in the elevation direction, a specific azimuth direction can be intensively searched.

Since the target is observed on the antenna axis of the radar means, the observation error covariance matrix of the radar device 1 can be used as it is as the polar coordinate observation error covariance matrix. A tracking filter designed for a driven radar can be used without any changes.

[Brief description of the drawings]

FIG. 1 is a flowchart showing a processing flow of a target tracking device of the present invention.

FIG. 2 is a block diagram showing a configuration of a target tracking device of the present invention.

FIG. 3 is a flowchart showing a processing flow of a conventional target tracking device.

FIG. 4 is a block diagram showing a configuration of a conventional target tracking device.

[Explanation of symbols]

1,101 radar device, 2,102 observation value calculation device, 3 coordinate conversion device, 4,104 Kalman gain matrix calculation device, 5,105 smooth vector calculation device, 6,
106 smooth error covariance matrix calculation device, 9,109 prediction vector calculation device, 10,110 prediction error covariance matrix calculation device, 14,114 first delay device, 15,1
15 a second delay device, 103 a first coordinate transformation device,
107 second coordinate conversion device, 108 third coordinate conversion device, 111 fourth coordinate conversion device, 112 fifth coordinate conversion device.

 ────────────────────────────────────────────────── ─── Continuing on the front page (72) Inventor Yoshio Kosuge 2-3-2 Marunouchi, Chiyoda-ku, Tokyo F-term in Mitsubishi Electric Corporation (reference) 5J070 AC02 AC06 AC11 AC13 AD10 AH19 AJ02 AK13 AK40 BB04 BB06 BB16

Claims (4)

[Claims] 1. A radar means having a phased array antenna, a signal received by the radar means being input, and a three-dimensional observation value consisting of a distance to a target and two direction cosine based on the signal. An observation value calculation means for calculating, a coordinate conversion means for converting the observation value to the north reference rectangular coordinate system, a prediction vector calculation means for calculating a prediction vector at the next sampling time based on the observation result at the previous sampling time, Prediction error covariance matrix calculation means for calculating a prediction error covariance matrix at the next sampling time based on the observation result at the previous sampling time; and one sampling delay of the prediction vector output from the prediction vector calculation means. A first delay unit for performing the calculation, and the prediction error output from the prediction error covariance matrix calculation unit. Second delay means for delaying one sampling of the covariance matrix, and Kalman gain matrix calculation means for calculating the Kalman gain matrix based on the prediction error covariance matrix at the previous sampling time output from the second delay means A prediction vector at the previous sampling time output from the first delay means, the Kalman gain matrix calculated by the Kalman gain matrix calculation means, and
Calculating a smoothed vector based on the coordinate-transformed observation value output from the coordinate converting means and outputting the smoothed vector to the predicted vector calculating means; and the Kalman calculated by the Kalman gain matrix calculating means. Calculating a smooth error covariance matrix from the gain matrix and the prediction error covariance matrix at the previous sampling time output from the second delay means, and outputting the smooth error covariance matrix to the prediction error covariance matrix calculation means A target tracking device comprising: 2. The target tracking apparatus according to claim 1, wherein electronic scanning is performed in an elevation angle direction while mechanically rotating an antenna surface of said radar means in an azimuth direction. 3. The target tracking apparatus according to claim 1, wherein electronic scanning is performed in a horizontal direction while mechanically rotating an antenna surface of said radar means in an elevation direction. 4. The target tracking device according to claim 1, wherein a target is observed on an antenna axis of said radar means.