CN109239703B - Real-time tracking method for moving target - Google Patents

Real-time tracking method for moving target Download PDF

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CN109239703B
CN109239703B CN201811134811.5A CN201811134811A CN109239703B CN 109239703 B CN109239703 B CN 109239703B CN 201811134811 A CN201811134811 A CN 201811134811A CN 109239703 B CN109239703 B CN 109239703B
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moving target
radar
signal
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time
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CN109239703A (en
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叶盛波
刘新
杨光耀
杨亮
阎焜
陈忠诚
张群英
方广有
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Institute of Electronics of CAS
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/415—Identification of targets based on measurements of movement associated with the target

Abstract

A real-time tracking method for a moving target is characterized by comprising the following steps: s1, collecting original echo data of the radar and preprocessing the original echo data; s2, extracting moving target information from the preprocessed data by adopting a three-frame difference method to obtain an echo signal containing the moving target information; s3, extracting the signal envelope of the echo signal by using Hilbert transform to obtain a radar oscillogram at one moment; s4, judging whether there is a moving target according to the radar oscillogram, if not, displaying the result of the radar oscillogram, if so, entering the step S5; and S5, extracting the position information of the moving object, filtering the position information, outputting the updated position information and displaying the updated position information. The invention can effectively improve the signal-to-noise ratio of the echo signal of the moving target and improve the stability of the system for tracking the target. The method is particularly suitable for real-time tracking of the human body target in the complex environment of the through-wall radar.

Description

Real-time tracking method for moving target
Technical Field
The invention belongs to the field of radar detection, and particularly relates to a real-time tracking method for a moving target.
Background
The ultra-wideband through-wall radar has excellent penetrability, ultrahigh distance resolution and positioning accuracy, and is widely applied to scenes such as fire emergency rescue, military anti-terrorism, urban street battle and the like to detect specific positions, activity ranges and the like of trapped people, criminals or characters after obstacles such as walls and the like. The original echo data of the ultra-wideband through-wall radar is very complex, and comprises echoes of moving targets of a human body, direct coupling waves of an antenna, reflection (scattering) echoes generated by static targets in walls, the ground, the ceiling and a detected environment, jitter and interference caused by instability of a radar hardware system, random thermal noise and other noise clutters in a large quantity. In an actual application scene, due to the fact that the radar scattering cross section of the human body target is small, the reflectivity is low, the radar is far away from the radar, the echo is very weak, the signal to noise ratio is low, the radar can be submerged in the noise clutter generally, and the difficulty in detecting the human body target is greatly increased. Therefore, the ultra-wideband through-wall radar signal processing has the primary task of moving target detection, namely removing non-moving target echoes in original echo data and extracting moving target echo information.
In the traditional moving target signal extraction method, a background signal is removed by adopting an adjacent cancellation method or an exponential averaging method to extract moving target information. The adjacent cancellation method is to subtract two adjacent echo data point by point to remove static or slowly moving objects and backgrounds, so as to achieve the purpose of extracting moving target information. The exponential averaging method continuously updates the background signal by adopting the current echo signal, and has better real-time performance, and the basic principle is that the current background estimated value is determined by the background estimated value at the previous and slow time and the current echo together. In practice, the adjacent cancellation method is a special case of the exponential averaging method, i.e. when the update rate is equal to 0, the exponential averaging method degenerates to the adjacent cancellation method. However, the current background estimation of the exponential averaging method is affected by the previous background estimation and is not the best estimation of the current background, so that the signal-to-noise ratio of the echo signal of the moving object is not the best.
Therefore, in the traditional through-the-wall radar moving target detection and tracking algorithm, the moving target information is extracted by removing the background signal based on the adjacent cancellation method or the exponential averaging method, and the following obvious defects exist: the echo signal of the moving target cannot obtain the maximum signal-to-noise ratio and is easily submerged in noise; the interference of distance side lobe, multipath and the like cannot be eliminated; when a moving object changes from moving to stationary, the tracked object is easily lost.
Disclosure of Invention
Aiming at the problems, in order to improve the signal-to-noise ratio of an echo signal of a moving target and avoid losing the tracked target, the invention provides a moving target real-time tracking method, which comprises the following steps:
s1, collecting original echo data of the radar and preprocessing the original echo data;
s2, extracting moving target information from the preprocessed data by adopting a three-frame difference method to obtain an echo signal containing the moving target information;
s3, extracting the signal envelope of the echo signal by using Hilbert transform to obtain a radar oscillogram at one moment;
s4, judging whether there is a moving target in the radar oscillogram, if not, displaying the result of the radar oscillogram, if so, entering the step S5;
and S5, extracting the position information of the moving target, filtering the position information, outputting the updated position information and displaying the updated position information.
In some embodiments, in step S5, constant false alarm rate detection is employed to extract the location information of a moving object.
In some embodiments, in step S5, the position information is subjected to threshold filtering, and then kalman tracking filtering is performed on the result of the threshold filtering.
In some embodiments, when performing the kalman tracking filter, the kalman filtering model is as follows:
Xt＝At，t-1Xt-1+Wt
Zt＝CtXt-1+Vt
wherein, XtIs the state vector of the subject at time t, At，t-1For a state transition matrix from time t-1 to time t, ZtIs the observation vector at time t, CtIs an observation matrix, VtTo be white Gaussian noise obeying N (0, R), WtIs white gaussian noise subject to N (0, Q).
In some embodiments, in step S5, gaussian weighted matching is also performed on the data at the next time instant at the position of the moving object.
In some embodiments, in step S1, the preprocessing includes band-pass filtering and logarithmic power gain control of the raw echo data.
In some embodiments, the logarithmic power gain control is performed using the following equation:
y′＝y*[log(m)]n
where y is a signal before gain, y' is a signal after gain, m is 1, 2, and N is a corresponding number of sampling points, N is a function power, and N represents a total number of sampling points of the echo data at the time.
In some embodiments, in step S2, the mathematical expression of the three-frame difference method is as follows:
zk＝(xk+1-xk)-(xk-xk-1)＝(xk+1+xk-1)-2xk
wherein x iskRepresenting the received echo value at the kth time instant as an Nx 1 vector, zkIs an echo signal containing information of a moving object.
In some embodiments, in step S3, the echo signal z is first processedkPerforming Hilbert transform, hereinafter replacing z with x (t)kTo represent the echo signal at this time, the hilbert transform is expressed as follows:
Figure BDA0001813101440000031
then, a signal envelope u (t) of x (t) is obtained, as shown in the following formula:
Figure BDA0001813101440000032
where A (t) is a function of amplitude,
Figure BDA0001813101440000033
is a function of phase, and
Figure BDA0001813101440000034
in some embodiments, in step S4, the following formula is used to determine whether there is a moving object:
Figure BDA0001813101440000035
and the Max () represents the maximum value, the Mean () represents the Mean value, if the calculated N is greater than a preset threshold value, the moving target is considered to exist, otherwise, the moving target is considered to not exist.
Based on the technical scheme, the invention at least obtains the following beneficial effects:
the invention adopts a three-frame difference method to extract the moving target information, and can effectively improve the signal-to-noise ratio of the moving target echo signal; if the moving target exists, the position information of the moving target is filtered and then output, and the stability of the system for tracking the target is improved. The method is particularly suitable for real-time tracking of the human body target in the complex environment of the through-wall radar.
Drawings
FIG. 1 is a flowchart illustrating steps of a real-time tracking method for a moving object according to an embodiment of the present invention;
FIG. 2 is a diagram of raw radar echo data in an embodiment of the present invention;
FIG. 3 is a waveform diagram of a time instant in raw echo data of the radar in FIG. 1;
FIG. 4 is a waveform diagram of the waveforms of FIG. 3 after preprocessing;
FIG. 5 is a comparison graph of results of extracting moving object information using three different methods;
FIG. 6 is a waveform diagram obtained by the three-frame difference method in FIG. 5;
FIG. 7 is a schematic diagram of CFAR detection;
FIG. 8 is a Gaussian waveform diagram for use in matching weights;
FIG. 9 is a waveform diagram of the waveform of FIG. 6 after Gaussian matching weighting;
FIG. 10 is a graph of the results of CFAR detection;
FIG. 11 is a graph of the results of FIG. 10 after threshold filtering;
FIG. 12 is a graph of results after Kalman tracking filtering of the results of FIG. 11;
fig. 13 is a flowchart of a method for real-time tracking of a moving object according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
Fig. 1 is a flowchart illustrating steps of a moving target real-time tracking method according to an embodiment of the present invention, and referring to fig. 1, the moving target real-time tracking method according to the embodiment of the present invention includes the following steps:
and S1, collecting raw echo data of the radar and preprocessing the raw echo data. The preprocessing of the data may include, among other things, band-pass filtering and logarithmic power gain control of the raw echo data.
And S2, extracting the moving target information from the preprocessed data by adopting a three-frame difference method to obtain an echo signal containing the moving target information.
And S3, extracting the signal envelope of the echo signal by using Hilbert transform to obtain a radar oscillogram at one moment.
And S4, judging whether the radar oscillogram has a moving target, if not, displaying the result of the radar oscillogram, and if so, entering the step S5.
And S5, extracting the position information of the moving object, filtering the position information, outputting the updated position information and displaying the updated position information.
Step S5 may specifically include: employing constant false alarm rate detection (CFAR) to extract the location information of a moving target; gaussian weighted matching is carried out on data at the next moment at the position of the moving target so as to inhibit interference such as distance side lobes, multipath and the like; performing threshold filtering on the position information of the moving target, and performing Kalman tracking filtering on the result of the threshold filtering; and updating the position information of the moving target by using a Kalman tracking filtering result, and outputting and displaying the updated position information.
The embodiment of the invention adopts a three-frame difference method to extract the moving target information, and can effectively improve the signal-to-noise ratio of the moving target echo signal; meanwhile, a matching gain control method is adopted, so that the interference of side lobe and multipath effect is greatly inhibited; threshold filtering and Kalman tracking filtering are adopted, and stability of a system tracking target is improved.
Referring to fig. 13, a set of real experimental data obtained from an ultra-wideband through-wall radar experiment is processed by the method described above.
(1) The original echo data of the ultra-wideband through-wall radar is obtained, as shown in fig. 2, in the figure, the abscissa represents time, and the ordinate represents target distance. Fig. 3 is a waveform of the original echo data at a time, in which the abscissa represents the target distance and the ordinate represents the normalized amplitude.
(2) The raw echo data is preprocessed, including band-pass filtering and logarithmic power gain control, and the waveform at this time after preprocessing is shown in fig. 4.
Wherein, the logarithmic power gain control is performed by the following formula:
y′＝y*[log(m)]n
where y is a signal before amplification, y' is a signal after amplification, m is 1, 2.
Since the echo signal of the far target is weak and the echo signal of the near target is strong, the far target is difficult to detect, and therefore the echo signal of the far target needs to be enhanced. The traditional linear or power function gain control can amplify far target echo signals and simultaneously enable the far noise level to be far higher than the target echo signals.
(3) The method adopts a three-frame difference method to extract moving target information, and the mathematical expression is as follows:
zk＝(xk+1-xk)-(xk-xk-1)＝(xk+1+xk-1)-2xk
wherein x iskRepresenting the received echo value at the kth time instant as an Nx 1 vector, zkIs an echo signal containing information of a moving object.
The traditional adjacent cancellation method and the exponential averaging method cannot maximize the signal-to-noise ratio of the echo signal of the moving target, and the three-frame difference method adopted by the invention can solve the problem.
(4) And extracting the signal envelope of the echo signal by using Hilbert transform to obtain a radar A-SCAN oscillogram at one moment. The moving object information is extracted by using an adjacent cancellation method, a three-frame interpolation method and an exponential averaging method, and the results of extracting signal envelopes and displaying the signal envelopes in a normalized manner through hilbert transform are shown in fig. 5. It can be seen from the figure that the noise is the largest with the target position shifted by exponential averaging. And the noise level of the moving target information extracted by adopting the three-frame difference method is minimum, and no position offset exists.
In this step, first, the echo signal z is filteredkPerforming Hilbert transform, hereinafter replacing z with x (t)kTo represent the echo signal at this time, as shown in the following equation:
Figure BDA0001813101440000061
then, a complex signal u (t) of x (t) is constructed as shown in the following formula:
Figure BDA0001813101440000062
where A (t) is a function of amplitude,
Figure BDA0001813101440000063
is a function of phase, and wherein
Figure BDA0001813101440000064
Fig. 6 is a waveform diagram of a radar a-SCAN at a moment after hilbert change, that is, a waveform diagram obtained by using a three-frame interpolation method in fig. 5.
(5) Judging whether a moving target exists in the image of figure 6, adopting the following formula to judge:
Figure BDA0001813101440000065
wherein Max () represents the maximum value, Mean () represents the Mean value, if the calculated N is greater than the preset threshold value, the moving object is considered to exist, and the next step is carried out; otherwise, the moving target does not exist, the position of the moving target is not updated at the moment, and the original result display is maintained.
(7) If the moving target exists, the CFAR detection is adopted to extract the position information of the moving target, and the principle is shown in fig. 7. Where D is the detected unit at the current time and Z is the background average power estimated from the signal in the reference sliding windows (the leading and trailing sliding windows in the figure) on both sides of D. T is a normalization factor, which is generally defined by a reference sliding window length R and a predetermined false alarm probability PfaT and Z together constitute the detection threshold of the D cell. The decision criteria of the comparator are as follows:
Figure BDA0001813101440000071
then, Gaussian matching weighting is carried out on data at the next moment at the position of the moving target so as to inhibit interference such as distance side lobes, multipath and the like;
the gaussian waveform obtained by the matching weighting of data is shown in fig. 8, and the result obtained by the gaussian matching weighting of fig. 6 after the hilbert transform is shown in fig. 9.
(8) Threshold filtering is carried out on the position information of the moving target extracted in the step (6); the results of CFAR detection are shown in fig. 10, and the results after threshold filtering are shown in fig. 11.
(9) And performing Kalman tracking filtering on the result of the threshold filtering. And tracking filtering by adopting a Kalman filter. The self-adaptive optimization autoregressive data processing algorithm is characterized in that a model is shown as the following formula:
Xt＝At，t-1Xt-1+Wt
Zt＝CtXt-1+Vt
Xtis the state vector of the subject at time t (the target position in this example). A. thet，t-1Is the state transition matrix from time t-1 to time t, for XtA linear transformation is performed, for a certain performance a known quantity. ZtIs an observation vector at time t (in this embodiment, the observation amount of the target position). CtIs an observation matrix. VtIs white Gaussian noise, W, obeying N (0, R)tIs white gaussian noise subject to N (0, Q).
The core of the method comprises 5 steps:
1) one-step prediction of state:
Figure BDA0001813101440000072
2) one step prediction of mean square error, PtIs the mean square error matrix at time t:
Figure BDA0001813101440000081
3) obtaining a filter gain equation:
Figure BDA0001813101440000082
4) obtaining a filtering estimation equation:
Figure BDA0001813101440000083
5) updating a filtering mean square error matrix:
Pt＝[I-HtCt]Pt，t-1
the results of performing kalman tracking filtering are shown in fig. 12.
(10) And updating the position information of the moving target by using a Kalman tracking filtering result, and outputting and displaying the updated position information.
The embodiment of the invention adopts a three-frame difference method, thereby effectively improving the signal-to-noise ratio of the echo of the moving target; meanwhile, a matching gain control method is adopted, so that the interference of side lobe and multipath effect is greatly inhibited; threshold filtering and Kalman tracking filtering are adopted, and stability of a system tracking target is improved. The method in the embodiment of the invention is suitable for real-time tracking of the human body target in the complex environment of the through-wall radar, and has a promoting effect on popularization of the through-wall radar.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (8)

1. A real-time tracking method for a moving target is characterized by comprising the following steps:
s1, collecting original echo data of the radar and preprocessing the original echo data, wherein the preprocessing comprises band-pass filtering and logarithmic power gain control of the original echo data;
s2, extracting moving target information from the preprocessed data by adopting a three-frame difference method to obtain an echo signal containing the moving target information;
s3, extracting the signal envelope of the echo signal by using Hilbert transform to obtain a radar oscillogram at one moment;
s4, judging whether there is a moving target in the radar oscillogram, if not, displaying the result of the radar oscillogram, if so, entering the step S5; and
and S5, extracting the position information of the moving target, performing Gaussian weighted matching on the data at the next moment at the position of the moving target, filtering the position information, and outputting and displaying the updated position information.
2. The method according to claim 1, wherein in step S5, constant false alarm rate detection is employed to extract the position information of a moving object.
3. The method according to claim 1, wherein in step S5, the position information is subjected to threshold filtering, and then kalman tracking filtering is performed on a result of the threshold filtering.
4. The method of claim 3, wherein the Kalman filtering model is as follows when performing the Kalman tracking filtering:
Xt＝At，t-1Xt-1+Wt
Zt＝CtXt-1+Vt
wherein, XtIs the state vector of the subject at time t, At，t-1For a state transition matrix from time t-1 to time t, ZtIs the observation vector at time t, CtIs an observation matrix, VtTo be white Gaussian noise obeying N (0, R), WtIs white gaussian noise subject to N (0, Q).
5. The method of claim 1, wherein the logarithmic power gain control is performed using the following equation:
y′＝y*[log(m)]n
where y is a signal before gain, y' is a signal after gain, m is 1, 2, and N is a corresponding number of sampling points, N is a power of a function, and N is a total number of sampling points of echo data at the time.
6. The method of claim 5, wherein in step S2, the mathematical expression of the three-frame difference method is as follows:
zk＝(xk+1-xk)-(xk-xk-1)＝(xk+1+xk-1)-2xk
wherein x iskRepresenting the received echo value at the kth time instant as an Nx 1 vector, zkIs an echo signal containing information of a moving object.
7. Method according to claim 6, characterized in that in step S3, the echo signal z is first of all measuredkPerforming Hilbert transform, hereinafter replacing z with x (t)kTo represent the echo signal at this time, the hilbert transform is expressed as follows:
Figure FDA0002727310580000021
then, a signal envelope u (t) of x (t) is obtained, as shown in the following formula:
Figure FDA0002727310580000022
where A (t) is a function of amplitude,
Figure FDA0002727310580000023
is a function of phase, and
Figure FDA0002727310580000024
8. the method according to claim 7, wherein in step S4, the following formula is used to determine whether there is a moving object:
Figure FDA0002727310580000025
and the Max () represents the maximum value, the Mean () represents the Mean value, if the calculated N is greater than a preset threshold value, the moving target is considered to exist, otherwise, the moving target is considered to not exist.
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