CN109239703A - Moving object real-time tracking method - Google Patents
Moving object real-time tracking method Download PDFInfo
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- CN109239703A CN109239703A CN201811134811.5A CN201811134811A CN109239703A CN 109239703 A CN109239703 A CN 109239703A CN 201811134811 A CN201811134811 A CN 201811134811A CN 109239703 A CN109239703 A CN 109239703A
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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
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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
- 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
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- Radar, Positioning & Navigation (AREA)
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Abstract
A kind of moving object real-time tracking method, which comprises the steps of: S1 collects the raw radar data of radar, and pre-processes to it；S2 obtains the echo-signal containing moving target information using Three image difference from by extracting moving target information in pretreated data；S3 is extracted the signal envelope of echo-signal using Hilbert transform, obtains the radar waveform figure at a moment；S4 judges whether there is moving target according to radar waveform figure, if it does not exist then with the radar waveform figure the results show that then entering step S5 if it exists；S5, extracts the location information of moving target, and after being filtered to location information, exports updated location information and shown.The present invention can effectively improve the signal-to-noise ratio of Moving Target Return signal, and improve the stability of system tracking target.Present invention is particularly suitable for the human body target real-time trackings under through-wall radar complex environment.
Description
Technical field
The invention belongs to radar detection fields, and in particular to a kind of moving object real-time tracking method.
Background technique
Ultra-broadband wall-through radar has excellent penetrability, the distance resolution of superelevation and positioning accuracy, therefore extensive
It is rescued for fire-fighting emergent, in the military scenes such as anti-terrorism and military operations in urban terrain, to detect the stranded masses, the criminal after the barriers such as wall
Guilty molecule or hostage's specific location and scope of activities etc..The raw radar data of ultra-broadband wall-through radar is extremely complex, not only wraps
Echo containing humanbody moving object, also includes the straight coupling wave of antenna, the static mesh in wall, ground, ceiling and test environment
Anti- (scattered) caused by mark is emitted back towards wave, and shake and interference and random thermal noise etc. caused by radar hardware system is unstable are a large amount of
Noise clutter.In practical application scene, due to the Radar Cross Section of human body target is smaller, reflectivity is lower, apart from thunder
Up to farther out, causing its echo very faint, signal-to-noise ratio is low, it will usually be submerged in above-mentioned noise clutter, so as to human body mesh
The difficulty detected is marked to greatly increase.Therefore the top priority of ultra-broadband wall-through Radar Signal Processing is moving object detection,
The non-athletic target echo in raw radar data is removed, Moving Target Return information is extracted.
Traditional moving target method for extracting signal is mostly using based on adjacent opposition method or exponetial smoothing method removal back
Scape signal, to extract moving target information.Adjacent opposition method refers to subtracts each other twice adjacent echoes data point by point, quiet to remove
Only or slow object and background are moved, achievees the purpose that extract moving-target information.Exponetial smoothing method, which constantly uses, works as pre-echo
Signal update background signal, while there is preferable real-time, the basic principle is that current background estimated value by it is preceding slow when the moment
Background estimating value is codetermined with pre-echo is worked as.Actually adjacent opposition method is a special case of exponetial smoothing method, that is, works as update
When rate is equal to 0, it is adjacent opposition method that exponetial smoothing method, which is degenerated,.However the estimation of the current background of exponetial smoothing method is by preceding background
The influence of estimation is not the best estimate of current background, so that the signal-to-noise ratio of Moving Target Return signal is not best.
Therefore, in traditional through-wall radar moving object detection and track algorithm, using based on adjacent opposition method or
Exponetial smoothing method removes background signal and extracts moving target information, and there are obvious shortcomings below: the echo-signal of moving target
Maximum signal-to-noise ratio cannot be obtained, it is easy to be submerged in noise；The interference such as distance side lobe and multipath can not be eliminated；Work as movement
When target becomes static from moving, it is easily lost the target of tracking.
Summary of the invention
In view of the above-mentioned problems, avoid losing the target tracked to improve the signal-to-noise ratio of the echo-signal of moving target, this
Invention proposes a kind of moving object real-time tracking method, includes the following steps:
S1 collects the raw radar data of radar, and pre-processes to it；
S2 is obtained using Three image difference from by extracting moving target information in pretreated data containing movement
The echo-signal of target information；
S3 extracts the signal envelope of the echo-signal using Hilbert transform, obtains the radar waveform at a moment
Figure；
S4 judges with the presence or absence of moving target in the radar waveform figure, if it does not exist then with the knot of the radar waveform figure
Fruit shows, then enters step S5 if it exists；
S5 extracts the location information of moving target, and after being filtered to the location information, exports updated
The location information is simultaneously shown.
In some embodiments, in step s 5, the position letter of moving target is extracted using constant false alarm rate detection
Breath.
In some embodiments, in step s 5, threshold filter first is carried out to the location information, then to threshold filter
As a result Kalman tracking filter is carried out.
In some embodiments, when carrying out the Kalman tracking filter, Kalman filter model is shown below:
Xt=AT, t-1Xt-1+Wt
Zt=CtXt-1+Vt
Wherein, XtFor the state vector of t moment research object, AT, t-1Square is shifted for the state from t-1 moment to t moment
Battle array, ZtFor the observation vector of t moment, CtFor an observing matrix, VtFor the white Gaussian noise for obeying N (0, R), WtTo obey N
The white Gaussian noise of (0, Q).
In some embodiments, in step s 5, the data at next moment are carried out also at the position of moving target
Gauss weighted registration.
In some embodiments, in step sl, the pretreatment includes carrying out band logical filter to the raw radar data
Wave and logarithm power gain control.
In some embodiments, logarithm power gain control is carried out using following formula:
Y '=y* [log (m)]n
Wherein, the signal before y is gain, y ' are the signal after gain, and m=1,2 ..., N are corresponding sampled point
Number, n are function power, and N represents total sampling number of the echo data at the moment.
In some embodiments, in step s 2, the mathematic(al) representation of the Three image difference is as follows:
zk=(xk+1-xk)-(xk-xk-1)=(xk+1+xk-1)-2xk
Wherein, xkThe reception Echo Rating at k-th of moment is represented, is the vector of N × 1, zkFor the echo containing moving target information
Signal.
In some embodiments, in step s3, first to echo-signal zkHilbert transform is carried out, uses x (t) below
Instead of zkIndicate the echo-signal at this moment, then Hilbert transform is shown below:
Then the signal envelope u (t) for obtaining x (t), is shown below:
A (t) is magnitude function in above formula,For phase function, and
In some embodiments, in step s 4, moving target is judged whether there is using following formula:
Wherein, middle Max () indicates maximizing, and Mean () representative is averaged, if the N being calculated is greater than preset threshold
Value, then it is assumed that there are moving targets, otherwise it is assumed that moving target is not present.
Based on the above-mentioned technical proposal it is found that the present invention at least achieve it is following the utility model has the advantages that
The present invention extracts moving target information using Three image difference, can effectively improve the letter of Moving Target Return signal
It makes an uproar ratio；Moving target will then export again after the filtering of its location information if it exists, improve the stability of system tracking target.Especially
Suitable for the human body target real-time tracking under through-wall radar complex environment.
Detailed description of the invention
Fig. 1 is the step flow chart of the moving object real-time tracking method of the embodiment of the present invention；
Fig. 2 is the radar raw radar data figure in specific embodiments of the present invention；
Fig. 3 be Fig. 1 in radar raw radar data in a moment waveform diagram；
Fig. 4 is that the waveform in Fig. 3 passes through pretreated waveform diagram；
Fig. 5 is the comparative result figure that moving target information is extracted using three kinds of distinct methods；
Fig. 6 is the waveform diagram obtained in Fig. 5 using Three image difference；
Fig. 7 is the schematic diagram of CFAR detection；
Fig. 8 is the Gaussian waveform figure that matching weighting uses；
Fig. 9 is that the waveform diagram after Gauss matches weighting is carried out to the waveform in Fig. 6；
Figure 10 is the result figure of CFAR detection；
Figure 11 is the result figure carried out after threshold filter to the result of Figure 10；
Figure 12 is the result figure carried out after Kalman tracking filter to the result of Figure 11；
Figure 13 is the flow chart of the method for the moving object real-time tracking of a specific embodiment of the invention.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, technical solution of the present invention will be carried out below
Clearly and completely describe.Obviously, described embodiment is a part of the embodiments of the present invention, instead of all the embodiments.
Based on described the embodiment of the present invention, those of ordinary skill in the art are obtained under the premise of being not necessarily to creative work
Every other embodiment, shall fall within the protection scope of the present invention.
Unless otherwise defined, the technical term or scientific term that the present invention uses should be tool in fields of the present invention
The ordinary meaning for thering is the personage of general technical ability to be understood.
Fig. 1 is the step flow chart of the moving object real-time tracking method of the embodiment of the present invention, and referring to Fig.1, the present invention is real
The moving object real-time tracking method applied in example includes the following steps:
S1 collects the raw radar data of radar, and pre-processes to it.Wherein, the pretreatment of data may include
Bandpass filtering and logarithm power gain control are carried out to raw radar data.
S2 is obtained using Three image difference from by extracting moving target information in pretreated data containing movement
The echo-signal of target information.
S3 is extracted the signal envelope of echo-signal using Hilbert transform, obtains the radar waveform figure at a moment.
S4 judges with the presence or absence of moving target in radar waveform figure, then aobvious with the result of the radar waveform figure if it does not exist
Show, then enters step S5 if it exists.
S5, extracts the location information of moving target, and after being filtered to location information, exports updated position
Information is simultaneously shown.
Step S5 is specific can include: the location information of moving target is extracted using constant false alarm rate detection (CFAR)；
Gauss weighted registration is carried out to the data at next moment at the position of moving target, to inhibit distance side lobe and multipath etc. dry
It disturbs；Threshold filter is carried out to the location information of moving target, then Kalman tracking filter is carried out to the result of threshold filter；It utilizes
The result of Kalman tracking filter updates the location information of moving target, exports updated location information and shows.
The embodiment of the present invention extracts moving target information using Three image difference, can effectively improve Moving Target Return
The signal-to-noise ratio of signal；Simultaneously using matching gain control methods, the interference of secondary lobe and multipath effect is greatly inhibited；It additionally uses
Threshold filter and Kalman tracking filter improve the stability of system tracking target.
A specific embodiment of the invention is introduced with reference to the accompanying drawing, referring to Fig.1 3, ultra-broadband wall-through radar is tested
Obtained in one group of true experimental data, handled by above-mentioned method.
(1) raw radar data of ultra-broadband wall-through radar is obtained, as shown in Fig. 2, abscissa represents the time in figure, is indulged
Coordinate represents target range.Fig. 3 is the waveform at a moment in raw radar data, and in figure, abscissa represents target range, is indulged
Coordinate is normalization amplitude.
(2) raw radar data is pre-processed, including bandpass filtering and logarithm power gain control, it is pretreated to be somebody's turn to do
The waveform at moment is as shown in Figure 4.
Wherein, logarithm power gain control is carried out using following formula:
Y '=y* [log (m)]n
Wherein, the signal before y is amplification, y ' are the signal after amplification, and m=1,2 ..., N are corresponding sampled point
Number, n are function power, and N is total sampling number of the echo data at the moment.
Since the echo-signal of distant object is weaker, nearby the echo-signal of target is strong, so that distant object is difficult to be detected
It measures, it is therefore desirable to which remote target echo signal is enhanced.Remote target is being amplified in traditional linear or power function gain control
While echo-signal, so that the noise level of distant place is far more than target echo signal, logarithm power function of the present invention
Gain controls the noise level that can rationally control distant place, so that target echo signal is not flooded by noise.
(3) moving target information is extracted using Three image difference, mathematic(al) representation is as follows:
zk=(xk+1-xk)-(xk-xk-1)=(xk+1+xk-1)-2xk
Wherein, xkThe reception Echo Rating at k-th of moment is represented, is the vector of N × 1, zkFor the echo containing moving target information
Signal.
Traditional adjacent opposition method and exponetial smoothing method can not make the echo-signal signal-to-noise ratio of moving target maximize, and
The three-frame difference rule that the present invention uses can solve the problems, such as this.
(4) signal envelope that echo-signal is extracted using Hilbert transform, obtains the radar A-SCAN wave at a moment
Shape figure.Wherein, adjacent opposition method, three frame interpolation methods and exponetial smoothing method is respectively adopted and carries out moving target information extraction, and passes through
It crosses Hilbert transform extraction signal envelope and the result normalized after display is as shown in Figure 5.It uses and refers to as can be seen from Figure
The noise of the number methods of average is maximum, at the same target position have it is offset.And use the extracted moving target information of Three image difference
Noise level is minimum, and does not have positional shift.
In this step, first to echo-signal zkHilbert transform is carried out, replaces z with x (t) belowkTo indicate at this time
The echo-signal at quarter, is shown below:
Then the complex signal u (t) for constructing x (t), is shown below:
A (t) is magnitude function in above formula,For phase function, and wherein
Fig. 6 is to be inserted in the radar A-SCAN waveform diagram namely Fig. 5 at a moment after Hilbert changes using three frames
The waveform diagram that value method obtains.
(5) judge to be judged with the presence or absence of moving target using following formula in Fig. 6:
Wherein, middle Max () indicates maximizing, and Mean () representative is averaged, if the N being calculated is greater than preset threshold
Value, then it is assumed that there are moving targets, into next step；Otherwise it is assumed that moving target is not present, moving target is not updated at this time
Position, maintenance are original as the result is shown.
(7) moving target if it exists then uses the location information of CFAR Detection and Extraction moving target, principle such as Fig. 7 institute
Show.D is the detected unit at current time in figure, Z be by the two sides D with reference to sliding window (in figure for forward position sliding window and after along sliding window) in
The background mean power that estimates of signal.T is the normalized factor, generally by reference sliding window length R, preset false-alarm probability Pfa
Etc. factors determine, T and Z together constitute the detection threshold value of D unit.The decision rule of comparator is as follows:
Then, Gauss is carried out to the data at next moment at moving target position and matches weighting, to inhibit distance other
The interference such as valve and multipath；
The Gaussian waveform of matching weighting is carried out to data as shown in figure 8, carrying out Gauss to Fig. 6 after Hilbert transform
It is as shown in Figure 9 with weighted results.
(8) threshold filter is carried out to the moving target position information extracted in step (6)；Result such as Figure 10 of CFAR detection
Shown, the result after threshold filter is as shown in figure 11.
(9) Kalman tracking filter is carried out to the result of threshold filter.Tracking filter is carried out using Kalman filter.Its
Adaptive optimization autoregression data processing algorithm, model are shown below:
Xt=AT, t-1Xt-1+Wt
Zt=CtXt-1+Vt
XtIt is the state vector (being target position in the present embodiment) of t moment research object.AT, t-1It is from t-1 moment to t
The state-transition matrix at moment, to XtLinear transformation is carried out, the performance determining for one is known quantity.ZtIt is the observation of t moment
Vector (being the observed quantity of target position in the present embodiment).CtIt is an observing matrix.VtIt is the Gauss white noise for obeying N (0, R)
Sound, WtIt is the white Gaussian noise for obeying N (0, Q).
Its core includes 5 steps:
1) one-step prediction of state:
2) one-step prediction of mean square error, PtIt is the Square Error matrix of t moment:
3) filtering gain equation is obtained:
4) filtering estimation equation is obtained:
5) filtering Square Error matrix is updated:
Pt=[I-HtCt]PT, t-1
The result for carrying out Kalman tracking filter is as shown in figure 12.
(10) location information that moving target is updated using the result of Kalman tracking filter exports updated position letter
It ceases and shows.
The embodiment of the present invention uses Three image difference, effectively improves the signal-to-noise ratio of transient echo；Simultaneously using matching
Gain control methods greatly inhibit the interference of secondary lobe and multipath effect；Threshold filter and Kalman tracking filter are additionally used,
Improve the stability of system tracking target.Method in the embodiment of the present invention is suitable for the people under through-wall radar complex environment
Body object real-time tracking has impetus to the popularization of through-wall radar.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, according to the technical solution and its hair of this hair
Bright design is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (10)
1. a kind of moving object real-time tracking method, which comprises the steps of:
S1 collects the raw radar data of radar, and pre-processes to it；
S2 is obtained using Three image difference from by extracting moving target information in pretreated data containing moving target
The echo-signal of information；
S3 extracts the signal envelope of the echo-signal using Hilbert transform, obtains the radar waveform figure at a moment；
S4 judges with the presence or absence of moving target in the radar waveform figure, then aobvious with the result of the radar waveform figure if it does not exist
Show, then enters step S5 if it exists；And
S5 extracts the location information of moving target, and after being filtered to the location information, exports updated described
Location information is simultaneously shown.
2. the method according to claim 1, wherein in step s 5, fortune is extracted using constant false alarm rate detection
The location information of moving-target.
3. the method according to claim 1, wherein in step s 5, first carrying out threshold value to the location information
Filtering, then Kalman tracking filter is carried out to the result of threshold filter.
4. according to the method described in claim 3, it is characterized in that, when carrying out the Kalman tracking filter, Kalman filtering
Model is shown below:
Xt=AT, t-1Xt-1+Wt
Zt=CtXt-1+Vt
Wherein, XtFor the state vector of t moment research object, AT, t-1For the state-transition matrix from t-1 moment to t moment, ZtFor
The observation vector of t moment, CtFor an observing matrix, VtFor the white Gaussian noise for obeying N (0, R), WtFor the height for obeying N (0, Q)
This white noise.
5. the method according to claim 1, wherein in step s 5, also at the position of moving target under
The data at one moment carry out Gauss weighted registration.
6. the method according to claim 1, wherein in step sl, the pretreatment includes to described original
Echo data carries out bandpass filtering and logarithm power gain control.
7. according to the method described in claim 6, it is characterized in that, carrying out logarithm power gain control using following formula:
Y '=y* [log (m)]n
Wherein, the signal before y is gain, y ' are the signal after gain, and m=1,2 ..., N are corresponding sampling number, n
For function power, N is total sampling number of the echo data at the moment.
8. the method according to the description of claim 7 is characterized in that in step s 2, the mathematical expression of the Three image difference
Formula is as follows:
zk=(xk+1-xk)-(xk-xk-1)=(xk+1+xk-1)-2xk
Wherein, xkThe reception Echo Rating at k-th of moment is represented, is the vector of N × 1, zkFor the echo letter containing moving target information
Number.
9. according to the method described in claim 8, it is characterized in that, in step s3, first to echo-signal zkCarry out Martin Hilb
Spy's transformation, replaces z with x (t) belowkIndicate the echo-signal at this moment, then Hilbert transform is shown below:
Then the signal envelope u (t) for obtaining x (t), is shown below:
A (t) is magnitude function in above formula,For phase function, and
10. according to the method described in claim 9, it is characterized in that, in step s 4, judging whether there is movement using following formula
Target:
Wherein, middle Max () indicates maximizing, and Mean () representative is averaged, if the N being calculated is greater than preset threshold value,
Think that there are moving targets, otherwise it is assumed that moving target is not present.
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