JP5665569B2 - Target tracking device and target tracking method - Google Patents

Description

  The present invention relates to a target tracking device and a target tracking method for tracking a target such as an aircraft.

When the resolution of the sensor is higher than the size of the target and the target is observed as an image for each resolution cell or as multiple points, clustering of multiple observation values observed from the same target is performed. Patent Document 1 below discloses a target tracking device that performs target tracking using the center of gravity position of a cluster that is a clustering result.
However, in this target tracking device, processing for determining whether the cluster is a moving object or a stationary object is not performed.

Japanese Patent No. 4116898 (paragraph number [0007], FIG. 1)

  Since the conventional target tracking device is configured as described above, for example, when tracking a moving airplane as a target in an airport or an airfield, for example, a stationary object (for example, parking) In-flight aircraft, fixed clutter that reflects fixedly from the same location, etc.) cannot be distinguished from the target, and there is a problem that a stationary object may be tracked by mistake with the target.

  The present invention has been made to solve the above-described problems, and an object of the present invention is to provide a target tracking device and a target tracking method capable of preventing target tracking errors and improving tracking accuracy.

A target tracking device according to the present invention includes a resolution cell detection unit that performs sensing that obtains a plurality of detection positions from the same target and detects a plurality of resolution cells in which the target exists, and a resolution cell detection unit. Performs clustering on a plurality of detected resolution cells, identifies a plot in which one or more resolution cells are collected, and identifies the centroid position of the plot and the number of resolution cells constituting the plot Using the identification means, the centroid position of the plot identified by the plot identification means, the number of cells, and the time at which sensing was performed by the resolution cell detection means, the classification processing for the plot is performed, and the classification result of the plot is output Classification processing means, plot classification results output from the classification processing means, and track speed of the target tracking track See, the determining using determining means for determining whether to use the center of gravity of the plots that were identified update process of the tracking track by plotting specific means, the tracking track the position of the center of gravity of the plot by using determination means updating If it is determined that the tracking track is used, the tracking track is updated using the center of gravity position of the plot, and if it is determined that it is not used, the tracking track is updated without using the center of gravity position of the plot. Track update means and tracking track display means for displaying the tracking track updated by the tracking track update means, and the classification processing means, as one processing of the classification processing for the plot, the distance from the target tracking track to the plot is If it is greater than or equal to a predetermined threshold, it is determined that the plot is another target, and if the distance from the tracking track of the target to the plot is less than the predetermined threshold, the plot Determining by the target / debris determination process that the pieces of target in the stationary is obtained so as to implement.

According to the present invention, the sensing that obtains a plurality of detection positions from the same target is performed, and the resolution cell detecting means for detecting the plurality of resolution cells where the target exists, and the resolution cell detecting means Plot specifying means for performing clustering on a plurality of resolution cells, specifying a plot in which one or more resolution cells are collected, and specifying the center of gravity position of the plot and the number of resolution cells constituting the plot; Using the centroid position of the plot specified by the plot specifying means and the number of cells and the time when sensing was performed by the resolution cell detecting means, the classification process is performed on the plot, and the classification result is output. Refer to the classification result of the plot output from the means and the classification processing means and the track speed of the target tracking track. And determining using determining means for determining whether to use the center of gravity of the plots that were identified update process of the tracking track by plotting specific means, the effect of using the center of gravity of the plot update process of the tracking track by using determination means When the determination is made, the tracking track is updated using the center of gravity position of the plot, and when it is determined that the tracking track is not used, the tracking track update means for updating the tracking track without using the center of gravity position of the plot; and Tracking track display means for displaying the tracking track updated by the tracking track update means, and the classification processing means, as one processing of the classification processing for the plot, the distance from the target tracking track to the plot is not less than a predetermined threshold If it is determined that the plot is another target, and the distance from the tracking track of the target to the plot is less than a predetermined threshold, the Configuration was because of the determining by the target / debris determination process that the pieces to implement, to prevent erroneous tracking of a target, there is an effect that can increase the tracking accuracy.

It is a block diagram which shows the target tracking apparatus by Embodiment 1 of this invention. It is a flowchart which shows the processing content of the target tracking apparatus by Embodiment 1 of this invention. It is explanatory drawing which shows the example of a detection of the resolution cell by the sensor. It is a flowchart which shows the classification process by the plot classification process part 2. FIG. It is explanatory drawing which shows the example of a size determination process by the plot classification process part. It is explanatory drawing which shows the movement presence determination process (peak determination process) by the plot classification process part. It is explanatory drawing which shows the target / clutter determination process (time determination process) by the plot classification process part. It is explanatory drawing which shows the another target / debris determination process (distance determination process) by the plot classification process part 2. FIG. It is explanatory drawing which shows the classification result of a plot. It is a Venn diagram showing the classification example of a plot. It is explanatory drawing which shows the use determination process by the plot use determination process part. It is explanatory drawing which shows the example where the gravity center position of a classification | category plot is used for the update of a tracking structure. It is a block diagram which shows the target tracking apparatus by Embodiment 2 of this invention. It is explanatory drawing which shows the another target / fragment determination process (distance determination process) in a 2nd another target / fragment determination processing system. It is a block diagram which shows the target tracking apparatus by Embodiment 3 of this invention. It is explanatory drawing which shows the example in which the threshold value compared with distance is set to the value corresponding to a target shape. It is a block diagram which shows the target tracking apparatus by Embodiment 4 of this invention.

Embodiment 1 FIG.
1 is a block diagram showing a target tracking apparatus according to Embodiment 1 of the present invention.
In FIG. 1, a sensor 1 is composed of, for example, a radio wave sensor such as a radar, an infrared sensor, etc., and performs sensing that obtains a plurality of detection positions from the same target, and a plurality of the targets that exist A process for detecting a resolution cell is performed.
In addition, the sensor 1 performs clustering on a plurality of resolution cells, identifies a plot in which one or more resolution cells are collected, and identifies the center of gravity position of the plot and the number of resolution cells constituting the plot. Perform the process.
The sensor 1 constitutes resolution cell detection means and plot specification means.

  The plot classification processing unit 2 is composed of, for example, a semiconductor integrated circuit on which a CPU is mounted or a one-chip microcomputer, and the center of gravity position of the plot specified by the sensor 1 and the resolution cell constituting the plot. Using the number of cells and the time when sensing was performed by the sensor 1, the classification process for the plot is performed, and the process for outputting the classification result of the plot is performed. The plot classification processing unit 2 constitutes a classification processing unit.

  The plot use determination processing unit 3 is composed of, for example, a semiconductor integrated circuit on which a CPU is mounted or a one-chip microcomputer, and the plot classification result output from the plot classification processing unit 2 and the track of the target tracking track With reference to the speed, a process of determining whether or not to use the gravity center position of the plot specified by the sensor 1 for the tracking wake update process is performed. The plot use determination processing unit 3 constitutes a use determination unit.

  The tracking processing unit 4 is composed of, for example, a semiconductor integrated circuit on which a CPU is mounted or a one-chip microcomputer, and the plot use determination processing unit 3 uses the position of the center of gravity of the plot for the tracking wake update process. When the determination is made, the tracking track is updated using the center of gravity position of the plot specified by the sensor 1, and when the determination is made not to be used, the tracking track is updated without using the center of gravity position of the plot. Perform the process. The tracking processing unit 4 constitutes tracking track update means.

  The display processing unit 5 is configured by, for example, a GPU (Graphics Processing Unit), and performs processing for displaying the tracking track updated by the tracking processing unit 4 on the display. The display processing unit 5 constitutes tracking track display means.

In FIG. 1, the sensor 1, the plot classification processing unit 2, the plot use determination processing unit 3, the tracking processing unit 4, and the display processing unit 5 that are components of the target tracking device are each configured by dedicated hardware. However, all or part of the target tracking device may be configured by a computer. In this case, a program in which all or part of the processing contents of the sensor 1, the plot classification processing unit 2, the plot use determination processing unit 3, the tracking processing unit 4 and the display processing unit 5 is described is stored in the memory of the computer. The CPU of the computer may execute a program stored in the memory.
FIG. 2 is a flowchart showing the processing contents of the target tracking device according to Embodiment 1 of the present invention.

Next, the operation will be described.
First, the sensor 1 performs sensing in which a plurality of detection positions are obtained from the same target, and detects a plurality of resolution cells (detection cells) where the target exists (step ST1).
FIG. 3 is an explanatory diagram showing an example of resolution cell detection by the sensor 1.
In the example of FIG. 3, sensing is performed to obtain detection positions of two targets T1 and T2 (target T1 is a small target and target T2 is a large target). This detection position is determined by the resolution of the sensor 1. is there.

For example, when the sensor 1 detects a plurality of resolution cells in which the targets T1 and T2 exist, one or more resolution cells are collected by performing clustering on the plurality of resolution cells based on a known clustering technique. The specified plot (clustering result of a plurality of resolution cells) is specified (step ST2).
In the example of FIG. 3, five plots (number 1 plot consisting of one resolution cell, number 2 plot consisting of 3 resolution cells, number 3 plot consisting of 1 resolution cell, 11 plots, No. 4 plot consisting of resolution cells and No. 5 plot consisting of 5 resolution cells) are specified.

When the sensor 1 identifies a plot that is a clustering result of a plurality of resolution cells, the sensor 1 identifies the barycentric position of the plot (step ST3).
In the example of FIG. 3, the position of ◯ surrounding the number for identifying the plot is the center of gravity position of the plot.
When specifying the center of gravity position of the plot, the center of gravity position of the plot may be calculated after weighting each resolution cell in consideration of the target shape and the like.
In addition, the sensor 1 specifies the number of resolution cells constituting each plot (step ST4).
In the example of FIG. 3, the number 1 plot has the number of cells “1”, the number 2 plot has the number of cells “3”, the number 3 plot has the number of cells “1”, and the number 4 plot has the number of cells. In the plot of “11” and number 5, the number of cells is “5”.

The plot has components of the sensing time by the sensor 1, the position observation value, and the cell number observation value.
The position observation value is the centroid position of the plot, and the centroid position is represented by position information and an observation error covariance matrix.
The cell number observation value is the number of cells of the resolution cells constituting the plot, and the cell number observation value is an index representing the size of the entire target or the partial size of the target.

The plot classification processing unit 2 uses the centroid position of the plot specified by the sensor 1, the number of resolution cells constituting the plot, and the time when sensing was performed by the sensor 1, for the plot. A classification process is performed, and the classification result of the plot is output (step ST5).
Hereinafter, specific contents of the classification process by the plot classification processing unit 2 will be described.
FIG. 4 is a flowchart showing the classification process by the plot classification processing unit 2.

First, the plot classification processing unit 2 performs a size determination process for determining the size of a plot (step ST21 in FIG. 4).
In the size determination process, when the number of resolution cells constituting the plot specified by the sensor 1 is λ, the plot is classified into “large signal” and “small signal” depending on the size of the cell number λ. It is.
That is, if the number of resolution cells λ constituting the plot specified by the sensor 1 is equal to or greater than a preset threshold value _λth , the plot classification processing unit 2 indicates that the plot is “large signal”. It is determined that it exists (step ST22), and the plot is classified as a large signal (step ST23).
λ ≧ λ _th → large signal On the other hand, if the cell number λ of resolution cells constituting the plot specified by the sensor 1 is less than the preset threshold value λ _th , the plot is “small signal”. It is determined that it exists (step ST22), and the plot is classified as a small signal (step ST24).
λ <λ _th → Small signal

Here, an example is shown in which the plot classification processing unit 2 classifies plots into two types, “large signal” and “small signal”, using a preset threshold value _λth. You may make it classify | categorize a plot into three, "large signal", "medium signal", and "small signal" using (lambda) _th .
Further, the plot may be classified into N pieces using N−1 threshold values λ _th .

FIG. 5 is an explanatory diagram showing an example of size determination processing by the plot classification processing unit 2.
FIG. 5 shows a histogram regarding the target observation status and the observed value of the number of cells.
In the example of FIG. 5, the plots of numbers 2, 4, and 5 are classified as large signals because the number of cells λ is greater than or equal to the threshold _λth .
On the other hand, the plots of numbers 1 and 3 are classified as small signals because the number of cells λ is less than the threshold _λth .

Next, the plot classification processing unit 2 performs a movement presence / absence determination process (peak determination process) for determining the presence / absence of a plot movement (step ST25 or ST26).
FIG. 6 is an explanatory diagram showing movement presence / absence determination processing (peak determination processing) by the plot classification processing unit 2.
When performing the peak determination process, the plot classification processing unit 2 firstly, as shown on the left side of FIG. 6, the xy voting areas (predetermined areas at predetermined intervals, the x-axis direction and the y-axis direction). Is provided). Hereinafter, each cell dividing the region is referred to as a voting cell.

Each time the sensor 1 identifies a plot, the plot classification processing unit 2 identifies which voting cell in the xy voting region corresponds to the position observation value of the plot.
When specifying the voting cell corresponding to the position observation value of the plot, the plot classification processing unit 2 increments the plot cumulative number of the voting cell by “1” (in the initial state, the plot cumulative number of each voting cell is "0").
Thus, by adding the cumulative number of voting cell plots over a plurality of scans, if the plot exists in the same voting cell in the xy voting area, the two-dimensional histogram of the xy voting area is included in the xy voting area. A peak appears. The peak of this two-dimensional histogram corresponds to the cumulative number of plots added to the time series.
The one-dimensional histogram in which the two-dimensional histogram shown on the left side of FIG. 6 is used as the cumulative number of plots for each voting cell is the diagram on the right side of FIG.

When the plot classification processing unit 2 specifies the peak of the two-dimensional histogram by adding the cumulative number of plots of the voting cells over a plurality of scans, the plot cumulative number that is the peak of the two-dimensional histogram is L The plot is classified into “stationary signal” or “moving signal” depending on the magnitude of the plot accumulation number L.
That is, the plot classification processing unit 2 plots cumulative number L is equal to or threshold _{L th} or more set in advance, it determines that the plot is "stationary signal" (step ST27 or,, ST28).
L ≧ L _th → stationary signal On the other hand, if the plot cumulative number L is less than the preset threshold L _th , it is determined that the plot is a “movement signal” (step ST27 or ST28).
L <L _th → Movement signal

When the plot classification processing unit 2 determines that the plot is a “movement signal”, if the plot is classified as a “large signal” in the previous size determination process, the plot is set as a “movement target”. Classify (step ST29).
If the plot is classified as “small signal” in the previous size determination process, the plot is classified as “moving small target” (step ST30).

Here, the plot classification processing unit 2, by comparing the plots cumulative number L and the threshold L _th, showed those classified as "stationary signal" or "movement signal" to plot, in advance, all the voting region A value obtained by averaging the peaks of the two-dimensional histogram of the voting cell is calculated, and a value obtained by subtracting a value obtained by averaging the peaks from the peak of each voting cell is compared with a predetermined threshold value. Or “movement signal”.
Note that various averaging methods, such as a weighted average value of peaks, can be considered as the average value of peaks.

When the plot classification processing unit 2 determines that the plot is a “stationary signal”, the plot classification processing unit 2 performs a target / clutter determination process (time determination process) that determines whether the plot is a target or a clutter (Step S1). ST31 or ST32).
FIG. 7 is an explanatory diagram showing a target / clutter determination process (time determination process) by the plot classification processing unit 2.
The target / clutter determination process (time determination process) is a process for determining whether the plot is “clutter” or “not clutter”. “Not clutter” includes not only the case where the relevant plot is “target” but also the possibility that the target may be other than clutter.

FIG. 7A shows an example of a case where “clutter” is determined, and FIG. 7B shows an example of a case where “not clutter” is determined.
The determination result “not clutter” may include “Pending”, which is a determination result indicating postponement of the determination.
The horizontal axis of FIG. 7 represents the plot input status at each time, and the bar on each time indicates that the plot has been input (the plot exists).
A dotted line frame represents a time window, and FIG. 7 shows a time window at the current time and a time window at the previous time. The time window is shifted at each time, and the time determination is made by the following (1) and (2).

Time determination (1)
When the plot exists in the time window more than M times out of N times in time T
→ Judge as “Clutter” Time Judgment (2)
If the plot does not exist in the time window more than M times out of N times in time T
→ Judged as “not clutter”

In the example of FIG. 7A, since the plot is input at times 1, 2, 3, and 4, the bar stands at each time.
For example, if N = 4 and M = 3, and the time T is set larger than the input interval of N times (for example, N = 4) of the plot, the plot exists in the time window at least three times out of four times. In the example of FIG. 7A, the plot at the current time (time 4) is determined as “clutter”.
In the example of FIG. 7B, since the plot is input at time 1 and 4, and the plot is not input at time 2 and 3, the bar stands at time 1 and 4.
For example, if N = 4 and M = 3 and the time T is set larger than the input interval of N times (for example, N = 4) of the plot, the plot does not exist in the time window more than 3 times out of 4 times. Therefore, in the example of FIG. 7B, the plot at the current time (time 4) is determined to be “not clutter”.

As the meaning of time determination, for example, when time T is set to 1 hour, in the case of time determination (1), if the plot continues within 1 hour, it is determined to be “clutter”. To do.
In the case of time determination (2), if the plot does not continue within one hour, it is determined as “not clutter”, that is, in this case, the target plot is a target other than “clutter” or There are other signal possibilities.

When the plot classification processing unit 2 performs the target / clutter determination processing (time determination processing) as described above and determines that the plot is “clutter” (step ST33 or ST34), In the size determination process, if the plot is classified as “large signal”, the plot is classified as “stationary large clutter” (step ST35).
On the other hand, if the plot is classified as “small signal” in the previous size determination process, the plot is classified as “stationary small clutter” (step ST36).
In addition, when the plot classification processing unit 2 performs the target / clutter determination processing (time determination processing) and determines that the plot is “not clutter” (step ST33), the plot classification processing unit 2 performs the size determination processing in the previous size determination processing. If it is classified as “Large Signal”, the plot is classified as “Still Large Target” (step ST37).

The plot classification processing unit 2 performs target / clutter determination processing (time determination processing), determines that the plot is “not clutter” (step ST34), and in the previous size determination processing, the plot is “small”. If it is classified as “Signal”, the plot may be a target other than “Clutter” or other signal, so determine whether the plot is another target or a fragment of a stationary target To perform another target / debris determination process (distance determination process) (step ST38).
FIG. 8 is an explanatory diagram showing another target / debris determination process (distance determination process) by the plot classification processing unit 2.
The left side of FIG. 8 shows the positional relationship between the tracking track and the plot, and the right side shows the distance between the tracking track and the plot in a histogram.

The distance determination process is performed when the distance between the track prediction value at the current time predicted by the tracking processing unit 4 to be described later and the center of gravity position (the current position of the plot) of the plot specified by the sensor 1 is D. This is a process of classifying the plot into “another target” and “unknown (a fragment of a stationary large target, etc.)” depending on the size of.
That is, the plot classification processing unit 2, if the distance D is the threshold D _th or more set in advance, determines that the plot is "another target" (step ST39), a still small target (tracking the plots The target is classified into a target different from the target of the wake (step ST40).
D ≧ D _th → other target On the other hand, if the distance D is less than the preset threshold D _th , the plot may be a part of the target of the tracking track (for example, a fragment from a stationary large target) Since there is a possibility of a split plot from a large target that is stationary), it is determined that the plot is “unknown” (step ST39), and the plot is classified as “unknown” (step ST39). Step ST41).
D <D _th → unknown

FIG. 9 is an explanatory diagram showing the result of plot classification, and FIG. 10 is a Venn diagram showing an example of plot classification.
When the plot classification processing unit 2 executes the plot classification process, the plot classification processing unit 2 assigns the classification result to the plot, and accumulates the plot with the classification result (hereinafter referred to as “classification plot”) in a classification plot database (not shown). .
The plot classification processing unit 2 has a function of displaying the classification plots stored in the classification plot database on the screen under the instruction of the operator. The operator can select a man-machine interface (such as a keyboard, a mouse, or a touch panel). When the input device) is operated to input a change command for the category plot, the change of the category plot is accepted according to the change command. As a result, when there is an error in the plot classification result, the operator can appropriately correct the plot classification result.
Further, the plot classification processing unit 2 outputs the classification plot to the plot use determination processing unit 3.

When the plot use determination processing unit 3 receives the classification plot from the plot classification processing unit 2 and receives the tracking track of the target generated by the tracking processing unit 4, the plot use determination processing unit 3 refers to the tracking speed of the classification plot and the tracking track of the target. Then, it is determined whether or not the barycentric position of the plot specified by the sensor 1 is used for the tracking wake update process (step ST6 in FIG. 2).
Hereinafter, the determination process by the plot use determination processing unit 3 will be specifically described.

The plot use determination processing unit 3 refers to the track speed of the target tracking track when determining whether to use the gravity center position of the plot specified by the sensor 1 for the tracking track update processing. The track speed of the tracking track of the target generated by 4 has a large error depending on the plot input or the abnormal plot input.
For this reason, if the track speed of the target tracking track generated by the tracking processing unit 4 is referred to as it is, the use determination accuracy may deteriorate. Therefore, before executing the use determination process, the following equation (1) is obtained. As shown, the track speed of the tracking track is smoothed, and the smoothed track speed that is the track speed after smoothing is calculated.
Vps (k) = Vps (k−1) + α [Vpz (k) −Vps (k−1)] (1)
In equation (1),
Vpz (k): current track speed Vps (k-1): previous smoothed track speed Vps (k): current smoothed track speed α: smoothing coefficient (0 <alpha <1)

There are two types of track speeds for tracking tracks: smooth track values and predicted track values. Whether the predicted track speed (wake predicted speed) or the smooth track value (wake smooth speed) is used. , Shall be determined in advance.
Here, the track speed of the tracking track is smoothed by equation (1), but the smoothed track speed can also be calculated by other smoothing methods such as moving average.

When the plot use determination processing unit 3 smoothes the track speed of the tracking track and calculates the smoothed track speed Vps (k) that is the track speed after smoothing, the plot use determination processing unit 3 plots the smoothed track speed Vps (k) and plot classification processing. With reference to the classification plot output from the unit 2, it is determined whether or not the barycentric position of the plot specified by the sensor 1 is used for the tracking wake update process.
That is, if the classification plot output from the plot classification processing unit 2 is “stationary large target”, the plot use determination processing unit 3 determines that the smoothed wake speed Vps (k) is less than the predetermined threshold V _th. Then, it is determined that the gravity center position of the category plot is used for the tracking wake update process (see FIG. 11).
Vps (k) <V _th → Use the centroid position of the classification plot On the other hand, if the smoothed wake speed Vps (k) is equal to or higher than the predetermined threshold V _th , the tracking centroid position of the classification plot is updated. It is determined that it will not be used.
Vps (k) ≥ V _th → Do not use the centroid position of the classification plot

Further, the plot use determination processing unit 3 determines that the smoothed wake speed Vps (k) is a predetermined threshold when the classification plot output from the plot classification processing unit 2 is “moving large target” or “moving small target”. If it is _Vth or more, it is determined that the barycentric position of the category plot is used for the tracking wake update process.
Vps (k) ≧ V _th → uses the center of gravity position of the category plot On the other hand, if the smoothed track speed Vps (k) is less than the predetermined threshold V _th , the track center position of the category plot is updated. It is determined that it will not be used.
Vps (k) <V _th → Do not use the centroid position of the classification plot

When the category plot output from the plot category processing unit 2 is “stationary large clutter”, “stationary small clutter”, “stationary small target (separate target)” or “unknown”, the plot use determination processing unit 3 The center-of-gravity position of the category plot is determined not to be used for tracking track update processing.
However, if the smoothed wake speed Vps (k) is less than the predetermined threshold value _Vth, it is determined that the centroid position of these classification plots is used for the tracking wake update process for the time being. The centroid position of these classification plots may not be used in the tracking wake update process when the wake accuracy of the wake is degraded.

When the plot use determination processing unit 3 determines whether or not to use the centroid position of the classification plot for the tracking wake update process, the plot use determination processing unit 3 adds a flag indicating the determination result to the classification plot and outputs it to the tracking processing unit 4. To do.
Use the centroid position of the category plot for update processing → Flag = 1
Do not use the centroid position of the classification plot for update processing → Flag = 0

When the tracking processing unit 4 receives the classification plot with a flag from the plot use determination processing unit 3, if the flag added to the classification plot is “1”, the tracking track using the centroid position of the classification plot. Is updated (step ST7).
That is, the tracking processing unit 4 classifies the plot specified by the sensor 1 as “moving large target” or “moving small target”, and if the track speed of the tracking track is equal to or higher than the threshold value V _th , the plot is currently moving. Since there is a high possibility of the target in the middle, the tracking track is updated using the center of gravity position of the plot.
Further, the tracking processing unit 4 classifies the plot specified by the sensor 1 as a “stationary large target”, and if the track speed of the tracking track is less than the threshold _Vth , the plot is the target that is currently stationary or creeping. Therefore, the tracking track is updated using the position of the center of gravity of the plot.

  FIG. 12 is an explanatory diagram showing an example in which the barycentric position of the classification plot is used for updating the tracking configuration. In the example of FIG. 12, the track prediction value at the current time obtained by the tracking processing unit 4 in the previous prediction process. The centroid position between the centroid position (in the figure, □) and the centroid position in the categorized plot (in the figure, ◯ point) is the track smoothing value at the current time (point △ in the figure), and the track smoothing at the current time The predicted track value at the next time is predicted from the value.

If the flag added to the category plot is “0”, the tracking processing unit 4 updates the tracking track without using the position of the center of gravity of the category plot (step ST8).
That is, the tracking processing unit 4 is specified by the sensor 1 when the plot specified by the sensor 1 is classified into “large moving target” or “moving small target” and the tracking speed of the tracking track is less than the threshold value _Vth. If the track speed of the tracking track is equal to or higher than the threshold _Vth , or the plot specified by the sensor 1 is “large static clutter”, “small static clutter”, “static” If the plot is classified as “small target (different target)” or “unknown”, it is unlikely that the plot is the target being tracked, so the tracking track is updated without using the barycentric position of the plot.
When updating the tracking track without using the centroid position of the category plot, the track prediction value at the current time (points in the figure) obtained in the previous prediction process is the smoothed value of the track at the current time (in the figure). , Δ points), and the predicted track value at the next time is predicted from the track smooth value at the current time.
Since the process itself for predicting the predicted track at the next time from the track smooth value at the current time is a known technique, detailed description thereof is omitted.

When the tracking processing unit 4 updates the tracking track, the tracking processing unit 4 outputs the tracking track to the plot classification processing unit 2, the plot use determination processing unit 3, and the display processing unit 5.
The tracking wake includes a wake prediction value and a wake smooth value, and the wake prediction value and the wake prediction value have a state vector composed of a position and a velocity and an error covariance matrix.
Upon receiving the updated tracking track from the tracking processing unit 4, the display processing unit 5 displays the tracking track on the display (step ST9).

  As is apparent from the above, according to the first embodiment, a process of detecting a plurality of resolution cells in which a target is present by performing sensing in which a plurality of detection positions are obtained from the same target, A sensor that performs clustering on one resolution cell, identifies a plot in which one or more resolution cells are collected, and performs processing for identifying the barycentric position of the plot and the number of resolution cells constituting the plot 1, using the centroid position and the number of cells of the plot specified by the sensor 1 and the time when sensing was performed by the sensor 1, the classification process for the plot is performed, and the plot classification process for outputting the classification result of the plot Referring to the classification result of the plot output from the unit 2 and the plot classification processing unit 2 and the track speed of the target tracking track, the sensor 1 A plot use determination processing unit 3 that determines whether or not to use the determined center of gravity position of the plot for tracking track update processing. The tracking processing unit 4 uses the plot use determination processing unit 3 to determine the center of gravity position of the plot. When it is determined that the track is used for updating the tracking track, the tracking track is updated using the center of gravity position of the plot, and when it is determined that the tracking track is not used, the center of gravity position of the plot is not used. Since the tracking track is updated, the target tracking can be prevented and the tracking accuracy can be improved.

That is, in the first embodiment, in the pre-processing of the tracking process, the plot size status, the plot detection status, the target time existence status assumed to be the plot, and the high resolution when the target is observed by the radar are achieved. Refer to the classification of plots that do not work with individual processing alone, such as the detection status from the same target that is caused by the situation, increase the classification accuracy of the plot, and whether the plot originates from the target or clutter Or whether it is a stationary target, a moving target, a large moving target, a small moving target, a large stationary target, a small stationary target, or a size For detailed plot categorization information such as whether the clutter is large or small in size, or the categorization of the plot is unknown It can be provided to the over data.
Then, using the plot with the detailed classification information, the tracking situation such as the target moving at a low speed and the target moving at a high speed is fed back to the preprocessing of the tracking process. By using the tracking information, etc., the plots are classified, and unnecessary plots (observed values) are rejected for low-speed moving targets and high-speed moving targets, and the required ones are used to improve tracking accuracy. Can do.

Embodiment 2. FIG.
13 is a block diagram showing a target tracking apparatus according to Embodiment 2 of the present invention. In the figure, the same reference numerals as those in FIG.
The distance determination control processing unit 6 performs processing for controlling the processing method of another target / debris determination processing (distance determination processing) in the plot classification processing unit 7.
The plot classification processing unit 7 performs the same classification process as the plot classification processing unit 2 in FIG. 1, but changes the processing method of the separate target / debris determination process under the control of the distance determination control processing unit 6.
That is, the plot classification processing unit 7, like the plot classification processing unit 2 of FIG. 1, if the distance D from the target of the tracking track until plots threshold D _th or more, and its plot is another goal determination If the distance D from the tracking track of the target to the plot is less than the threshold D _th , a process for determining that the plot is a fragment of the stationary target (hereinafter referred to as “first separate target / debris determination processing method”). However, when a processing mode change instruction is received from the distance determination control processing unit 6, among the one or more plots specified by the sensor 1, from the plot having the largest size to the other plots. If the distance D1 is equal to or greater than the threshold D1 _th , it is determined that another plot is another target. If the distance D1 from the largest size plot to the other plot is less than the threshold D1 _th , the other plot is determined. Processing for determining that the lot is a fragment of a stationary target (hereinafter referred to as “second different target / fragment determination processing method”) is performed.
The distance determination control processing unit 6 and the plot classification processing unit 7 constitute a classification processing unit.

Next, the operation will be described.
When the plot classification processing unit 7 receives an instruction from the distance determination control processing unit 6 to change the processing method of the different target / fragment determination processing (distance determination processing) to the first different target / fragment determination processing method. In the same manner as the plot classification processing unit 2 in FIG. 1, it is determined whether the plot is another target or whether the plot is a stationary target fragment.
When the plot classification processing unit 7 receives an instruction from the distance determination control processing unit 6 to change the processing method of the separate target / fragment determination processing (distance determination processing) to the second separate target / fragment determination processing method, Among the one or more plots specified by 1, the plot having the largest size is specified.
FIG. 14 is an explanatory diagram showing another target / fragment determination process (distance determination process) in the second different target / fragment determination processing method.
The left side of FIG. 14 shows the positional relationship between each plot, and the right side shows the distance from the largest plot to another plot in a histogram.

When the plot classification processing unit 7 specifies a plot having the largest size (number 3 in the example of FIG. 14), the plot classification processing unit 7 obtains a distance D1 from the plot having the largest size to another plot, and the distance D1 is the threshold value D1. If it is greater than or equal to _th , it is determined that the other plots are “different targets”, and the other plots are classified as stationary small targets (targets different from the tracking track targets).
D1 ≧ D1 _th → other target On the other hand, if the distance D1 from the largest size plot to the other plot is less than the threshold D1 _th , the other plot may be a part of the target of the tracking wake (for example, stationary) The other plots are determined to be “unknown” and the other plots are identified as “unknown”. Classified into "".
D1 <D1 _th → unknown
In the example of FIG. 14, the plot number 1 is classified as “stationary small target”, and the plot numbers 2 and 4 are classified as “unknown”.

As apparent from the above, according to the second embodiment, the distance D1 from the largest plot to the other plot among the one or more plots specified by the sensor 1 is equal to or greater than the threshold _D1th. For example, if the other plot is determined to be another target, and the distance D1 from the largest size plot to the other plot is less than the threshold D1 _th , the other plot is a fragment of the target at rest. Since the tracking wake error is large and the first separate target / fragment determination processing method cannot accurately perform the separate target / fragment determination process, the separate target / fragment determination process is accurately performed. There is an effect that can be performed.

Embodiment 3 FIG.
FIG. 15 is a block diagram showing a target tracking apparatus according to Embodiment 3 of the present invention. In the figure, the same reference numerals as those in FIG.
The plot classification processing unit 8 is similar to the plot classification processing unit 7 of FIG. 13 under the control of the distance determination control processing unit 6, the first separate target / fragment determination processing method or the second separate target / fragment determination process. The method is implemented, but the threshold value to be compared with the distance is different in that it is set to a value corresponding to the target shape.
The distance determination control processing unit 6 sets a threshold value to be compared with the distance.

In the second embodiment, in carrying out the first target / debris determination processing scheme, as the threshold value D _th, it is set to a radius of a circle centered on the track prediction value of the current time in the tracking track (FIG. 8 When performing the second target / debris determination processing method, the radius of a circle centered on the center of gravity of the plot with the largest size is set as the threshold D1 _th (see FIG. 14). ).
In the third embodiment, the threshold values D _th and D1 _th are set to values corresponding to the actual target shape (for example, if the target is an aircraft, the shape of the aircraft) (see FIG. 16).

Considering the equidistant circle from the tracking track, depending on the size of the threshold, the plot of number 5 is regarded as a reflection point from the same target as the plot of numbers 2, 3, and 4, but on the left side of FIG. As shown, the thresholds D _th and D1 _th are set according to the shape of the actual target, so that the plot of No. 5 is distinguished from the target related to the plots of No. 2, 3, and 4 by different targets. It can be properly determined that there is.

  As apparent from the above, according to the third embodiment, since the threshold value to be compared with the distance is set to a value corresponding to the target shape, the determination accuracy of the separate target / debris determination process There is an effect that can be improved.

Embodiment 4 FIG.
FIG. 17 is a block diagram showing a target tracking apparatus according to Embodiment 4 of the present invention. In the figure, the same reference numerals as those in FIG.
FIG. 17 shows an example in which the plot use determination control processing unit 9 is applied to the target tracking device of the first embodiment, but it may be applied to the target tracking device of the second and third embodiments.
The plot use determination control processing unit 9 does not end even when the categorization processing in the plot classification processing unit 2 has passed a predetermined time, or when a stop request for plot classification processing is input from the operator, the plot classification processing unit 2 and The processing of the plot use determination processing unit 3 is stopped, and control is performed so that the plot obtained from the sensor 1 is given to the tracking processing unit 4 via the plot classification processing unit 2 and the plot use determination processing unit 3.

In the first to third embodiments, the plot classification processing unit 2 performs the classification process on the plot specified by the sensor 1 and outputs the plot classification result, and the plot use determination processing unit 3 outputs the plot classification result. The center of gravity of the plot specified by the sensor 1 is used to determine whether or not to use for the tracking wake update process. For example, the classification of plots is affected by the difference in parameter settings. There are cases where the processing cannot be performed or the calculation time of the plot classification processing takes more than a predetermined threshold.
In such a case, there is a possibility that the tracking track cannot be continuously output.

Therefore, in the fourth embodiment, when the classification process in the plot classification processing unit 2 does not end even after a predetermined time has passed, or when a stop request for the plot classification process is input from the operator, the plot use determination control process The unit 9 stops the processing of the plot classification processing unit 2 and the plot usage determination processing unit 3, and the plot obtained from the sensor 1 is sent to the tracking processing unit via the plot classification processing unit 2 and the plot usage determination processing unit 3. 4 may be provided to the tracking processing unit 4 directly from the sensor 1 without going through the plot classification processing unit 2 and the plot use determination processing unit 3.
In this case, the tracking processing unit 4 can update the tracking track of the target using the center of gravity position of the plot obtained from the sensor 1 without waiting for the usage determination result output from the plot usage determination processing unit 3. .

  As is apparent from the above, according to the fourth embodiment, when the classification process in the plot classification processing unit 2 does not end even after a predetermined time has elapsed, or a stop request for the plot classification process is input from the operator. In this case, the plot usage determination control processing unit 9 stops the processing of the plot classification processing unit 2 and the plot usage determination processing unit 3, and the plot obtained from the sensor 1 becomes the plot classification processing unit 2 and the plot usage determination processing unit 3. Since, for example, the plot classification process cannot be performed due to the influence of the parameter setting difference, or the calculation time of the plot classification process is determined in advance. Even under such circumstances, the effect of being able to continue the tracking track in real time is achieved.

  In the present invention, within the scope of the invention, any combination of the embodiments, or any modification of any component in each embodiment, or omission of any component in each embodiment is possible. .

  DESCRIPTION OF SYMBOLS 1 Sensor (resolution cell detection means, plot specification means), 2 Plot classification processing section (classification processing means, change acceptance means), 3 Plot use determination processing section (use determination means), 4 Tracking processing section (tracking track update means) 5 Display processing section (tracking track display means), 6 Distance determination control processing section (category processing means, change acceptance means), 7, 8 Plot classification processing section (classification processing means), 9 Plot use determination control processing section (tracking) Wake update means).

Claims (10)

Resolution cell detection means for detecting a plurality of resolution cells in which the target exists by performing sensing in which a plurality of detection positions are obtained from the same target;
Clustering is performed on a plurality of resolution cells detected by the resolution cell detection means, a plot in which one or more resolution cells are collected is specified, and the barycentric position of the plot and the resolution cell constituting the plot are identified. A plot identifying means for identifying the number of cells;
A category that performs classification processing on the plot and outputs a classification result of the plot, using the center of gravity position and the number of cells specified by the plot specifying unit and the time when sensing is performed by the resolution cell detection unit. Processing means;
Whether or not to use the centroid position of the plot specified by the plot specifying means for the tracking track update process with reference to the plot classification result and the track speed of the target tracking track output from the classification processing means Use determination means for determining
When the use determining means determines that the center of gravity position of the plot is used for the update process of the tracking wake, the tracking wake is updated using the center of gravity position of the plot, and it is determined not to use. The tracking track update means for updating the tracking track without using the position of the center of gravity of the plot, and the tracking track display means for displaying the tracking track updated by the tracking track update means ,
If the distance from the tracking track of the target to the plot is equal to or greater than a predetermined threshold, the classification processing means determines that the plot is a different target, and starts from the tracking track of the target. If the distance to the plot is less than a predetermined threshold, another target / debris determination process for determining that the plot is a stationary target fragment is performed . Resolution cell detection means for detecting a plurality of resolution cells in which the target exists by performing sensing in which a plurality of detection positions are obtained from the same target;
Clustering is performed on a plurality of resolution cells detected by the resolution cell detection means, a plot in which one or more resolution cells are collected is specified, and the barycentric position of the plot and the resolution cell constituting the plot are identified. A plot identifying means for identifying the number of cells;
A category that performs classification processing on the plot and outputs a classification result of the plot, using the center of gravity position and the number of cells specified by the plot specifying unit and the time when sensing is performed by the resolution cell detection unit. Processing means;
Whether or not to use the centroid position of the plot specified by the plot specifying means for the tracking track update process with reference to the plot classification result and the track speed of the target tracking track output from the classification processing means Use determination means for determining
When the use determining means determines that the center of gravity position of the plot is used for the update process of the tracking wake, the tracking wake is updated using the center of gravity position of the plot, and it is determined not to use. The tracking track update means for updating the tracking track without using the position of the center of gravity of the plot, and the tracking track display means for displaying the tracking track updated by the tracking track update means ,
The categorization processing means, as one process of categorization processing for a plot, is that the distance from the largest size plot to another plot among the one or more plots specified by the plot specification means is greater than or equal to a predetermined threshold. For example, the other plot is determined to be another target, and if the distance from the largest plot to the other plot is less than a predetermined threshold, the other plot is determined to be a stationary target fragment. A target tracking device that performs another target / debris determination process . The classification processing means performs a classification process on the plot, and classifies the plot into a moving target, a stationary target, a stationary clutter, another target, or a stationary target fragment. The target tracking device according to claim 1 or 2, wherein Said classification processing means, in addition to another target / debris determination process as classification processing for plotting, size determining processing to determine the size of the plot, the mobile existence determination processing and the plot determines the presence or absence of movement of the plot is a target the or at a the one in the target / clutter determination processing determines clutter, target tracking apparatus according to claim 3, wherein the performing at least one or more determination process. The target tracking device according to any one of claims 1 to 4, wherein a threshold value corresponding to a target shape is set as the predetermined threshold value. Plot the tracking track updating means, if the classification process in the classification process section does not end even after the lapse of a predetermined time, which stops the process of the classification processing means and the use determination means, specified by the plot specifying means The target tracking device according to any one of claims 1 to 5 , wherein the tracking track of the target is updated using the position of the center of gravity of the target. The use determination means, if track velocity of the tracking track the target is higher than a predetermined speed, if the plot by the classification processing unit is classified to the target in movement, the update process of the tracking track the position of the center of gravity of the plot a judgment to the effect to be used in the case track velocity of the tracking track the target is lower than the predetermined speed, if plotted by the classification processing unit is classified into a target at rest, tracking the position of the center of gravity of the plot track The target tracking device according to claim 3 , wherein the target tracking device determines whether to use the update processing. Target tracking apparatus according to any one of claims 1 to 7, wherein, characterized in that provided change receiving means for receiving a change of classification results of the plot by the classification processing unit. The resolution cell detection means performs sensing for obtaining a plurality of detection positions from the same target, detects a plurality of resolution cells in which the target exists, and a plot specifying means includes the above-described plot specifying means, Clustering is performed on a plurality of resolution cells detected in the resolution cell detection processing step, a plot in which one or more resolution cells are collected is specified, and the barycentric position of the plot and the resolution cell constituting the plot are identified. The plot specifying processing step for specifying the number of cells, and the classification processing means, using the centroid position of the plot specified in the plot specifying processing step and the number of cells and the time at which sensing was performed in the resolution cell detecting processing step, Performs a classification process on the plot and outputs a classification result for the plot. And the use determination means refers to the plot classification result output in the classification processing step and the track speed of the target tracking track, and determines the position of the center of gravity of the plot specified in the plot identification processing step. The use determination processing step for determining whether or not to use for the update process and the tracking wake update means determine that the center of gravity position of the plot is used for the tracking wake update process in the use determination processing step. In this case, the tracking track is updated using the center of gravity position of the plot, and when it is determined that the tracking track is not used, the tracking track update processing step of updating the tracking track without using the center of gravity position of the plot; and The tracking track display means includes a tracking track display processing step for displaying the tracking track updated in the tracking track update processing step .
In the above classification processing step, as one processing of the classification processing for the plot, if the distance from the target tracking track to the plot is equal to or greater than a predetermined threshold, it is determined that the plot is a different target, and the target tracking track is If the distance to the plot is less than a predetermined threshold, another target / debris determination process for determining that the plot is a stationary target fragment is performed . The resolution cell detection means performs sensing for obtaining a plurality of detection positions from the same target, detects a plurality of resolution cells in which the target exists, and a plot specifying means includes the above-described plot specifying means, Clustering is performed on a plurality of resolution cells detected in the resolution cell detection processing step, a plot in which one or more resolution cells are collected is specified, and the barycentric position of the plot and the resolution cell constituting the plot are identified. The plot specifying processing step for specifying the number of cells, and the classification processing means, using the centroid position of the plot specified in the plot specifying processing step and the number of cells and the time at which sensing was performed in the resolution cell detecting processing step, Performs a classification process on the plot and outputs a classification result for the plot. And the use determination means refers to the plot classification result output in the classification processing step and the track speed of the target tracking track, and determines the position of the center of gravity of the plot specified in the plot identification processing step. The use determination processing step for determining whether or not to use for the update process and the tracking wake update means determine that the center of gravity position of the plot is used for the tracking wake update process in the use determination processing step. In this case, the tracking track is updated using the center of gravity position of the plot, and when it is determined that the tracking track is not used, the tracking track update processing step of updating the tracking track without using the center of gravity position of the plot; and The tracking track display means includes a tracking track display processing step for displaying the tracking track updated in the tracking track update processing step .
In the classification processing step, as one processing of the classification processing for the plot, the distance from the largest size plot to another plot among the one or more plots specified by the plot specification processing step is a predetermined threshold or more. If so, it is determined that the other plot is another target, and if the distance from the largest plot to the other plot is less than a predetermined threshold, the other plot is a fragment of the target at rest. A target tracking method characterized by performing another target / debris determination process for determination .

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