CN102008291B - Single-channel UWB-based radar type life detection instrument for multi-target detection - Google Patents
Single-channel UWB-based radar type life detection instrument for multi-target detection Download PDFInfo
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Abstract
The invention discloses a single-channel ultra-wide bandwidth (UWB)-based radar type life detection instrument for multi-target detection. The life detection instrument comprises a UWB biologic radar front end and a calculating unit, wherein the UWB biologic radar front end comprises a transmitting antenna, a receiving antenna, a pulse oscillator, an electromagnetic pulse generator and a sampling integrator; the pulse oscillator generates a pulse signal; the signal triggers the electromagnetic pulse generator to generate a narrow pulse and radiates the narrow pulse out through the transmitting antenna; a reflected signal is transmitted to the sampling integrator through the receiving antenna; a pulse signal generated by the pulse oscillator simultaneously generates a distance gate through a delay circuit and a distance gate generator to select a received signal; the signal passes through a sampling integration circuit; a weak signal is detected after accumulation, amplified and filtered by an amplifier and a filter, sampled by a high-speed analogue/digital (A/D) acquisition card and transmitted to the calculating unit; and the acquired signal is analyzed by the calculating unit to extract life information and each target distance of multiple human targets.
Description
Technical field
The present invention relates to belong to noncontact life parameters technical field of detection, particularly a kind of radar life-detector that can be used for the single channel of multiple target detection based on UWB.
Background technology
Radar life-detector is a kind of fusion Radar Technology and the penetrable nonmetal medium of biomedical engineering technology (brick wall, ruins etc.) noncontact, surveys a kind of emerging special radar of human life's body (breathing, heart beating, body are moving etc.) at a distance.The radar life-detector technology then is to be an emerging technology of the detection of a target with the life entity, is the very important cutting edge technology field that International Technology circle is generally acknowledged.Because this technology does not have any constraint to measured object; Need not the connection of contact electrode, pick off, cable etc.; And can be at a distance from certain distance, penetrate certain medium (like clothes, gauze, brick wall, ruins etc.) human body discerned detection; So can be widely used in fields such as disaster buried person person search and rescue, the monitoring of struggle against terror mid-board and battle reconnaissance, particularly have irreplaceable advantage in fields such as emergency management and rescue, anti-terrorisms.
Target recognition ability and distance, angular resolution are two emphasis of current radar life-detector area research, also are the key issues that this paper need break through.At present, comparatively sophisticated radar life-detector system based on the continuous wave radar system can only provide the unmanned result of people, and the distance that can't provide target and angle information etc., and penetration capacity also remains further raising.In view of the advantage that ultra wide range radar is had, we have adopted advanced in the world at present ultra wide range technology, and it is combined with noncontact life detection technology, and research is visited people's Radar Technology based on the noncontact of ultra wide range.
Existing radar type life detection technology is identified as the master with the detection to single goal, and multiobject detection and location are also only limited to moving target.Up to the present, still unresolved a plurality of static human body identification of targets in this field and orientation problem.Many quiet target acquisition identification positioning technology are the new research direction and the difficult points in international life detection field, and this technology is the key technology of radar life-detector, and it is restricting the extensive use of radar life-detector.The solution of many quiet target acquisition identification positioning difficult problems can greatly improve the detection efficient in the noncontact life detection, satisfies in the real work the localized demand of multiple target quick detection.
Summary of the invention
Technical problem to be solved by this invention is the deficiency to prior art, and a kind of single pass radar life-detector based on UWB that realizes multiple target detection is provided, and solves the detection and the orientation problem of a plurality of static human body targets.
A kind of radar life-detector that can be used for the single channel of multiple target detection based on UWB; Comprise UWB bioradar front end and computing unit, said UWB bioradar front end comprises transmitting antenna, reception antenna, pulse oscillator, electromagnetic pulse generator, Sampling Integral device; Pulse oscillator produces pulse signal, and this signal triggering electromagnetic pulse generator produces burst pulse, and radiate through transmitting antenna; Reflected signal is delivered to the Sampling Integral device through reception antenna; The pulse signal that is produced by pulse oscillator produces range gate through delay circuit and range gate generator simultaneously; Select to received signal, signal is detected through accumulation back small-signal through the Sampling Integral circuit; And via amplifier and wave filter amplify, filtering; After the sampling of high-speed a/d capture card, send into computing unit again, the signal that collects is carried out analyzing and processing, finally extract a plurality of human body target life-informations and each target range by computing unit.
Described single channel UWB radar life-detector, said wave filter adopt that gain is 1, passband is a 0.08-5000Hz hardware filtering circuit.
Described single channel UWB radar life-detector; Said computing unit comprises signal integration module, signal decomposition reconstructed module, digital filtering module and numerical differentiation module, spatial-frequency analysis module; Said signal integration module is carried out integration to signal on distance; Said signal decomposition reconstructed module signal is broken up decompose, reconstruct; Synthetic target echo signal and distance signal; Said digital filtering and numerical differentiation module are carried out digital filtering and numerical differentiation to target echo signal, and said spatial-frequency analysis module is used for carrying out spatial-frequency analysis according to target echo signal after digital filtering and the numerical differentiation and distance signal, obtains the target one-dimensional distance.
It is the hamming window Finite Impulse Response filter of 0.5Hz that described single channel UWB radar life-detector, said digital filtering module adopt 160 rank, cut-off frequency.
Described single channel UWB radar life-detector, said numerical differentiation module adopt 60 exponent number word differentiators.
Described single channel UWB radar life-detector also comprises the crest discrimination module, and whether be used for putting the threshold decision crest location according to spatial-frequency analysis result and systemic presupposition has human body target to exist.
Innovation part of the present invention is:
(1) proposed first realization enhancing, human body identification and the one-dimensional distance of the faint vital signs of static human body target have been distinguished, carried out the new method of multiple target two-dimensional localization again, for a plurality of static human body target localizations of radar life-detector are opened up new approach.
(2) adopting to change the Time-Frequency Analysis Method of shape--empty frequency analysis (space, frequency) is that master's one-dimensional distance is distinguished algorithm the echo-signal of the ultra wide range radar life-detector of single channel system acquisition is split, recombinates and relevant processing, and being expected provides new method for the one-dimensional distance differentiation of a plurality of quiet targets in the life detection.
Description of drawings
Fig. 1 is the ultra wide range radar life-detector of single channel system principle diagram;
Fig. 2 is the ultra wide range radar life-detector of single channel computing unit structural representation;
Fig. 3 is provided with sketch map for ultra wide range radar parameter;
Fig. 4 is target echo signal and distance signal;
Fig. 5 forms block diagram for the hardware filtering circuit;
Fig. 6 is that the signal waveform before and after the differential algorithm compares (30 seconds data);
Fig. 7 carries out discrimination result for the crest method of discrimination to the two objective data;
Fig. 8 is the aimless time-frequency figure of free space (driftlessness);
Fig. 9 is the time-frequency figure (target physical location 6.5m) of free space single goal;
Figure 10 is the time-frequency figure (target physical location 2.5m and 7.5m) of free space two objective;
Figure 11 is the time-frequency figure (target physical location 6.0m) that wears 30cm brick wall single goal;
Figure 12 is the time-frequency figure (target physical location 3.0m and 6.0m) that wears 30cm brick wall two objective.
The specific embodiment
Below in conjunction with accompanying drawing and specific embodiment, the present invention is elaborated.
Present embodiment provides a kind of single channel ultra wide range radar life-detector, and Fig. 1 is the ultra wide range radar life-detector of single channel system principle diagram.At first pulse oscillator produces pulse signal, and this signal triggering electromagnetic pulse generator produces burst pulse, and radiate through transmitting antenna.Reflected signal is delivered to the Sampling Integral device through reception antenna, and the signal that is produced by pulse oscillator produces range gate through delay circuit, selects to received signal; Signal is through the Sampling Integral circuit; Be detected through small-signal after the accumulation of thousands of pulses, and carry out amplification filtering, after the high-speed collection card sampling, send into computing unit again; By computing unit the signal that collects is carried out analyzing and processing and identification, calculate target range at last.
As shown in Figure 1, be radar front end in the frame of broken lines, the mid frequency and the bandwidth of system are all 500MHz, and the wave beam angle of coverage is 60 °.Computing unit command range door generator obtains the echo-signal of different distance section in the search coverage.
The parameter of computer-controllable system is: initial distance, investigative range, sample frequency and antenna gain.As shown in Figure 3, after antenna penetrated brick wall, search coverage was one fan-shaped; Through initial distance and investigative range are set; The scanning probe of the sector region of dash area in can realizing scheming, if echo-signal shows target information after analyzing, just can judge in this sector region has target.Through the initial distance of continuous adjustment, can realize the tomoscan in certain zone.And adjustment investigative range (reception of antenna is counted constant) then can be adjusted the sensitivity of detection system, changes the target range resolving power of system, realizes the coarse scan in certain zone and carefully sweeps.
For example, initial distance is set to 6m (40 nanosecond), and investigative range is set to 3m (20 nanosecond); The echo-signal of native system is the sequence that 2048 points are formed, and the effective search coverage of so current radar is antenna dead ahead 6m～9m, and angle is 60 ° a sector region; Echo-signal only reflects the information that vertically goes up 3m, and the scope of 3m on average is divided into 2048 parts, and promptly each sampling obtains 2048 data; We are referred to as 2048 points, and the distance of n point representative is:
In the formula (1): n is an ordinal number.
According to nyquist sampling theorem, sample frequency must be greater than the twice of signal highest frequency, and we set the A/D sample frequency is 64Hz.
Fig. 2 is the ultra wide range radar life-detector of single channel of the present invention computing unit structural representation; Said computing unit comprises signal integration module, signal decomposition reconstructed module, digital filtering module and numerical differentiation module, spatial-frequency analysis module; Said signal integration module is carried out integration to signal on distance; Said signal decomposition reconstructed module signal is broken up decompose, reconstruct; Synthetic target echo signal and distance signal; Said digital filtering and numerical differentiation module are carried out digital filtering and numerical differentiation to target echo signal, and said spatial-frequency analysis module is used for carrying out spatial-frequency analysis according to target echo signal after digital filtering and the numerical differentiation and distance signal, obtains the target one-dimensional distance.
Present embodiment provides the ultra wide range radar life-detection instrument of single channel parameter index:
The antenna of system, receive-transmit system relevant parameter index are following:
(1) antenna is: medium coupling shield type;
(2) number of antenna: 1 transmitting antenna, 1 reception antenna;
(3) dual-mode antenna mid frequency: 500MHz;
(4) bandwidth: 500MHz;
(5) the window time: 4～5000ns is adjustable;
Quiet target small-signal strengthens:
Realize the static human body identification of targets, at first should the faint life signal of static human body be strengthened.In the present embodiment,, adopt faint processing of biomedical signals method, improve signal to noise ratio, realize basic identification human body target to handle the enhancing with useful signal through signal after the high-speed sampling to the characteristics of UWB radar echo signal.
4 dot product point-scores carry out integration to signal between adopting at 8 on distance; Again signal is broken up decompose, reconstruct, synthetic target echo signal and distance signal; Target echo signal is carried out digital filtering and numerical differentiation, to realize the enhancing of weak useful signal.
3.1 the integration of signal
The high-speed collection card sample rate that adopts in the present embodiment is 64Hz, and then the data volume after the AD sampling is big, is unfavorable for real-time operation; The data volume minimizing can cause echo-signal to lack enough range informations again too much.So choose between 84 integration methods the back signal of sampling carried out subsection integral guaranteeing to have under the situation of enough range resolutions present embodiment.
4 integration methods are made even per 8 additions of data all exactly between 8, per twice integration interval, 4 points (0～7,4～11; 8～15; Analogize the back), make sampling back signal data amount become 1/4th of original signal through the last integration of distance, under the situation of not losing signal characteristic, reduced the sequence length of signal; Reduce operand, accelerated arithmetic speed.
3.2 signal is decomposed and reconstituted
With signal behind the integration by time and space two territories decompose, reconstruct, the synthetic distance signal y (d) that contains the target echo signal x (t) of temporal information and contain spatial information, wherein t is a time variable, d is apart from variable.What target echo signal reflected is the time dependent situation of signal amplitude on the respective distances point, and the abscissa of target echo signal is the time; Distance signal then is the sequence that the amplitude of the each point on the synchronization different distance is formed, and the abscissa of distance signal is a distance.Fig. 4 is road target echo signal (1600 points, 25 seconds) and distance signal (60ns, 9m) oscillogram of selecting at random.
Target echo signal has improved signal to noise ratio, more helps the extraction of vital sign signals, and distance signal has guaranteed suitable range resolution again when reducing operand greatly.
3.3 the selection of wave filter
3.3.1 hardware filtering device
In the present embodiment with before the hardware filtering circuit access high-speed AD acquisition card; Filter bandwidht is adjustable, and in the early stage preliminary experiment, successively having tested bandwidth is several kinds of wave filter of 0.08-10Hz, 0.08-100Hz, 0.08-1000Hz, 0.08-2000Hz, 0.08-3000Hz, 0.08-4000Hz, 0.08-5000Hz; Pass through effect comparison; Finally selected the passband of 0.08-5000Hz as the hardware filtering circuit, gain is divided into two grades: gain is 1 o'clock, and amplification is 1 times; Gain is 2 o'clock, and amplification is 2 times.
Adopt the single channel UWB system, single channel UWB system that adds hardware filter circuit (gain is 1) and the single channel UWB system random acquisition data that add hardware filter circuit (gain is 2) each 16 groups (driftlessness, single goal data) that do not add the hardware filter circuit respectively, add up to totally 48 groups of data.The algorithm that adopts computing unit to comprise respectively to these 48 groups of data is handled and is differentiated, the statistical recognition accuracy, and statistical result is as shown in table 1 below.
Recognition correct rate situation (48 groups of data) during table 1 increase and decrease hardware filtering device
Using gain is that the recognition correct rate of 1 hardware filtering circuit is the highest, is 62%.
Through relatively finding, adopt the Effect on Detecting of the UWB system that gain is 1, passband is 0.08-5000Hz hardware filtering circuit best.
3.3.2 digital filter
Because phase information is extremely important to quiet target detection in the faint vital sign parameter signals; And quiet target recognition and one dimension are distinguished technology having relatively high expectations to algorithm stability and follow-up Digital Signal Processing; So adopt finite impulse response (FIR) (FIR) wave filter to remove High-frequency Interference in the present embodiment, extract useful signals such as breathing.The system function of FIR wave filter is:
Difference equation is:
The selection of filter order has been directly connected to its amplitude-frequency characteristic, and exponent number is high more, and amplitude-frequency characteristic is good more, and filter effect is good more.Also bring some negative effects but unrestrictedly increase exponent number, as increased system's operand, prolonged the time delay of filtering output etc.Comprehensive above two aspects consider that under the situation that system's operational capability allows, we select for use 160 rank FIR wave filter to test.
On Filter Design, adopt the window function method,, finally adopted the hamming window through contrasting the amplitude-frequency characteristic of several kinds of window function low pass filters.
Normal condition servant's breathing rate is per minute 15～20 times, considers that its frequency of abnormal condition generally can not surpass 0.4Hz yet.So the digital filter that we adopt, its cut-off frequency is this index of main reference 0.4Hz also, and the low pass filter that promptly is not less than 0.4Hz with cut-off frequency carries out filtering to target echo signal, with the performance of each wave filter relatively.We test the lowpass digital filter that cut-off frequency is respectively 0.4Hz, 0.5Hz, 0.6Hz, 0.7Hz, 0.8Hz in this article.
Picked at random investigative range (window when being radar) be 20 nanoseconds (3m) and 60 nanoseconds (9m) data each 48 groups; Amount to 96 groups; These signals are signal (data that contain driftlessness, single goal) after the sampling of single channel UWB system, and the algorithm that these 96 groups of The data computing units comprise is differentiated.In the contrast experiment, only change filter cutoff frequency, other each software and hardware parameter constants, statistics is differentiated result's accuracy, and it is as shown in table 2 that it differentiates accuracy.
Table 2 changes filter cutoff frequency to differentiating the influence of accuracy
According to above experimental result, through comprehensive comparison, the hamming window Finite Impulse Response filter of finally choosing 160 rank, cut-off frequency side 0.5Hz comes target echo signal filtering High-frequency Interference, keeps vital sign signals such as breathing.
3.4 the selection of differentiator
Because the existence of DC component and baseline drift phenomenon often comprises the very big extremely low frequency composition of energy in the target echo signal, make signal substantial deviation baseline faint life signal identification to be produced very big influence.Present embodiment proposes to adopt the method for numerical differentiation to come filtering DC component in time and extremely low frequency to disturb, and makes useful signal center on zero base line and fluctuates up and down, to reach the purpose that strengthens vital sign signals such as breathing.The computational process of differentiator is shown in formula (4):
In the formula: y is the output signal, and x is an input signal, and m is an exponent number, and n is the sequence number of point.
Picked at random investigative range (window during radar) be 20 nanoseconds (3m) and 60 nanoseconds (9m) data each 48 groups; Amount to 96 groups; These signals are signal (data that contain driftlessness, single goal) after the sampling of single channel UWB system; Adopt the digital differentiator on 20 rank, 40 rank, 60 rank, 80 rank, 100 rank, 120 rank, 140 rank, 160 rank, 180 rank to carry out discerning and distance calculation after differential is handled respectively to these data, it is as shown in table 3 that it differentiates accuracy:
Table 3 changes the differentiator exponent number to differentiating the influence of accuracy
| Accuracy (%) | 20 |
40 |
60 |
80 |
100 |
120 |
140 |
160 rank | 180 rank |
| 20 nanoseconds | 62.50 | 56.25 | 77.08 | 70.83 | 70.83 | 70.83 | 70.83 | 68.75 | 68.75 |
| 60 nanoseconds | 43.75 | 31.25 | 43.75 | 31.25 | 37.50 | 41.67 | 41.67 | 43.75 | 43.75 |
Can find out that the differentiation accuracy of the signal after 60 exponent number word differentiators are handled is the highest, total accuracy of its 96 groups of data is 60.78%.Through comparing, present embodiment has selected for use 60 exponent number word differentiators to remove DC component and extremely low frequency disturb, the vital signs of enhancing signal.
Can find out that from the comparison of Fig. 6 after target echo signal was carried out 60 rank differential in time, signal had been got back near the baseline and tightly and fluctuateed up and down around baseline, DC component and extremely low frequency composition have obtained inhibition, and useful signal has obtained enhancing.
The spatial-frequency analysis method is carried out one-dimensional distance and is distinguished:
After accomplishing quiet target small-signal enhancing, will on distance, distinguish human body target.Because distance signal is the ultra-low frequency signal of reflection target range information; So contain spatial information and characteristics such as non-stationary to distance signal; Present embodiment has made up the joint distribution function of space, frequency; Adopt spatial frequency conjoint analysis method (changing the time frequency analysis of shape) the signal analysis of adjusting the distance, describe energy density and the intensity of signal on different distance, frequency, thereby provide the range information of each human body target.
What time frequency analysis was represented is the situation of change of signal spectrum on time shaft; After becoming time variable apart from variable; What the time frequency analysis result represented is exactly frequency spectrum situation of change spatially, thus utilize these characteristics of time frequency analysis to come the target on the different distance is carried out spectrum analysis, and then obtain human body differentiation result and target one-dimensional distance information; So just formed space, application form that this time frequency analysis of frequency conjoint analysis is new, its essence remains time frequency analysis.
In the present embodiment; Time variable in the time frequency analysis is become space (distance) variable; Make up space, frequency associating function; Make it can utilize space, frequency information to describe the energy density of input signal simultaneously, make this method possess " location " function of space, frequency, thereby the method for good non-stationary signal Frequency Estimation in a certain distance range is provided for us.
Time-frequency conversion comprises single linear conversion such as short time discrete Fourier transform, and bilinear transformation such as Wei Na-Wei Er distributes, wavelet transformation etc.Spatial frequency transforms in the present embodiment is that the time variable in the time frequency analysis is replaced to space (distance) variable and comes, and its range resolution be to confirm in advance, need not change through the variation window width; And object of experiment is a static human body; Its breath signal is comparatively stable in long-time; Belong to steadily local and the big non-stationary signal of length; Be fit to analyze for this type signal, carry out spatial-frequency analysis, and result is analyzed and compared so chosen the short time discrete Fourier transform of single linear conversion in the present embodiment with short time discrete Fourier transform.
In the present embodiment with the time window be that distance signal evenly is divided into 26 sections on distance behind the differential of 60ns (corresponding 9m investigative range, initial distance are 1ns), corresponding range resolution is about 0.36m.Because antenna is the transceiver antenna, the dual-mode antenna close together receives the influence of antenna direct wave; Distance signal partly has a segment signal amplitude bigger near reception antenna, and influence is judged, so this distance signal is clipped for preceding 4 sections; Do not consider; Then the amplitude of 20 points on each section of the 5th to the 26th section (the 26th section is 11 points) is done an addition in the section, obtain and as the value of this section, thereby form the new distance signal that has only 22 numerical value to form; What these 22 numerical value were corresponding is to finish since 0.36 * 4=1.44m to 9m, the target echo signal of the point that equally distributed each distance is last.
After the segmentation, according to the needed positioning result refresh rate of actual detection, every all the new distance signals in this time period, 64 * 10=640 new distance signal altogether of taking out at a distance from 10 seconds; Each distance signal is split into point (22 point), and in chronological sequence order is with the sequence reorganization with each point again, and formation contains the fresh target echo-signal of temporal information; Each new target echo signal by joining end to end apart from antenna order from the close-by examples to those far off, is constituted the input signal of spatial-frequency analysis.
Synthetic input signal is made spatial-frequency analysis; Promptly make short time discrete Fourier transform; Wherein window width is corresponding to the length of fresh target echo-signal; Be decided to be 64 * 10=640, the each sliding distance of window is corresponding to the range resolution of distance signal, and counting according to conversion, to select Fourier transform to count to the demand of frequency resolution be 1024 points with differentiating the result for the principle that is not less than window width.Confirm after the above parameter input signal to be carried out short time discrete Fourier transform, and draw figure as a result.The short time discrete Fourier transform formula is suc as formula shown in (6):
STFT(t，w)＝∫S(τ)γ(τ-t)e
-jwτdτ ......(6)
Wherein S (τ) is an input signal, and γ (t) is a window function.
In the selection of window function length,, usually require the window function time width of selection short as far as possible in order to improve the temporal resolution of short time discrete Fourier transform.On the other hand, short time discrete Fourier transform will be expected high frequency resolution, then require the window function time width of selection long as far as possible, so the raising of the raising of temporal resolution and frequency resolution contradicts.In the reality, the width of the window function γ (t) of selection should adapt with the steady length of the local of signal.In this experiment; The eupnea frequency of detected object human body is per minute 15-20 time; Be that 3-4 accomplishes the respiration motion second; For the influence and the assurance frequency resolution that reduce human body respiration accidentalia, individual variation, the time width of the window function that we choose is 10 seconds, and the window width that corresponds in the spatial-frequency analysis is 640.
The setting of crest method of discrimination and threshold value:
The result of space, frequency analysis is one 3 dimension (space, frequency, energy) corresponding relation, and two coordinate axess are respectively distance and frequency, and energy intensity is to come corresponding by the depth of color.Through suitable mode and suitable human life characteristic decision threshold is set, can realize that promptly single channel distinguishes and distance calculation the distance of a plurality of quiet targets.If on a certain distance, signal energy is big, the spectrum peak is concentrated, the signal energy on the neighbor distance, and meet decision threshold, then thinking has the static human body target (one-dimensional distance is confirmed) on the respective in this reception antenna investigative range; If have the signal of macro-energy to occur on a plurality of distances, and meet threshold value, then thinking has the static human body target to exist on a plurality of distances, through the one-dimensional distance value of these targets of algorithm records, is the distance of each target to antenna.
Multiobject differentiation and distance calculation concrete steps are following:
Find out maximum 12 sections of energy value in 22 sections, and find out all energy crests in these 12 sections, be designated as E respectively by the energy size
Peak1, E
Peak2, E
Peak3... crest is such regulation: promptly the energy value of this section is greater than adjacent two sections energy values, and then this section is a crest.Find out after the energy crest, the section sequence number of record crest place section is used for the subsequent calculations target range.
8 sections minimum the average energy value of energy value is designated as in calculating 22 sections: E
Mean, utilize crest energy and the average energy value of minimum 8 sections to make comparisons to confirm the number of target.Compare threshold is following:
(1) if the energy E of energy crest 1
Peak1Minimum average B configuration energy value E greater than 4 times
Mean, i.e. E
Peak1＞4E
Mean, think that then crest 1 position has target to exist, the distance of target is calculated by the sequence number of crest 1;
(2) if the energy E of energy crest 2
Peak2Minimum average B configuration energy value E greater than 3 times
Mean, i.e. E
Peak2＞3E
Mean, think that then crest 2 positions have target to exist, the distance of target is calculated by the sequence number of crest 2;
(3) if the energy E of energy crest 3
Peak2Minimum average B configuration energy value E greater than 2.5 times
Mean, i.e. E
Peak3＞2.5E
Mean, think that then crest 3 positions have target to exist, the distance of target is calculated by the sequence number of crest 3.
Fig. 7 be according to the crest method of discrimination and decide the discrimination result that threshold value is carried out two objective data.
Can find out that there are two crests in 22 segment signals, the energy of these two crests through with separately threshold ratio, draw two crests and be target, through the distance of calculating two targets be: 13 * 0.36=4.68m, 21 * 0.36=7.56m.
So far; We adopt multiple target and have accomplished the identification of a plurality of static targets on the different distance and the calculating of each target range apart from distinguishing algorithm; Promptly had after the multiobject range information; To there being the signal amplitude on the target location to carry out the normalization processing, form the projection signal of two dimensional surface on each passage again.
Confirmatory experiment:
Because usually being partition wall in practical application, radar life-detector surveys, so its detection performance through walls is to estimate a good and bad important indicator of radar life-detector.In the experiment of this part; The data of employing free space collection are verified the feasibility of how quiet target recognition and distance differentiation algorithm earlier; Utilize the data of collection through walls to come the performance of algorithm is estimated again, data through walls are divided into driftlessness, single goal, two objective, four kinds of situation of three targets and estimate respectively.
At first; In experiment, adopt single channel UWB radar life-detector system that 10 volunteers have been carried out the detection under the free space state respectively; And gather, stored all experimental datas; These data comprise aimless data in the investigative range, the data of single goal and the data of two objective.From these data, driftlessness, single goal, two objective data have respectively been selected one group at random, the method that adopts this experiment to narrate is handled and is calculated the data of choosing, its result such as Fig. 8-and shown in Figure 10.Wherein Fig. 8 is the time frequency analysis figure of the data of free space driftlessness situation, differentiates result of calculation to be: this search coverage does not have the human body target and exists; Fig. 9 is the time frequency analysis figure of free space single goal data, differentiates result of calculation to be: have a static human body target to exist in this search coverage, its position is (target physical location 6.5m) at the 6.48m place; Figure 10 is the time frequency analysis result of the data of free space two objective situation, differentiates result of calculation to be: have two static human body targets to exist in this search coverage, its position is respectively 2.88m and 7.56m place (the target physical location is 2.5m and 7.5m).
Can find out from the time frequency analysis result of the data of the various situation of free space; It is driftlessness, single goal or two objective situation that many quiet target recognitions and distance differentiation algorithm can be distinguished in the investigative range at free space well; And can calculate the distance of each static target comparatively exactly, thereby verified the feasibility of the method.
After the feasibility checking of having carried out algorithm; In experiment, adopt single channel UWB radar life-detector system that 10 volunteers have been carried out the detection under (brick wall that 30cm is thick) through walls state respectively again; And gather, stored all experimental datas, these data comprise aimless data in the investigative range, the data of single goal; The data of the data of two objective and three targets; The method that adopts this experiment to narrate is handled and is calculated all data, selects wherein two groups of data at random, its time frequency analysis result such as Figure 11-and shown in Figure 12.Wherein Figure 11 is the time frequency analysis figure that wears 30cm brick wall single goal data, differentiates result of calculation to be: have a static human body target to exist in this search coverage, its position is (target physical location 6.0m) at the 6.48m place; Figure 12 is the time frequency analysis result who wears 30cm brick wall two objective data, differentiates result of calculation to be: have two static human body targets to exist in this search coverage, its position is respectively 3.24m and 6.48m place (the target physical location is 3.0m and 6.0m).
Define objective is differentiated the result and is divided into correct decision, fails to judge, judges by accident, misjudges.The number of target, distance are all differentiated the errorless correct decision that is, target is arranged and differentiate for driftlessness for failing to judge, driftlessness and to differentiate for target is arranged be erroneous judgement, the target location is differentiated wrong for misjudging.The error that the situation of movement of position, thoracic cavity when considering range resolution to the influence of error and human body respiration, regulation are differentiated position and target physical location is target range less than 0.5m and differentiates correctly.According to above mode classification the result being differentiated in the processing of adopting data adds up.
Wherein, driftlessness data through walls have been gathered 23 groups altogether.According to above-mentioned mode classification the result of adopting data is added up, because all data are the driftlessness data, so there is not the situation of failing to judge and misjudging.Statistical result is as shown in table 4.
Table 4 is worn the recognition correct rate situation of 30cm brick wall driftlessness data
Single goal data through walls are gathered 27 groups (target location is randomly dispersed in the investigative range of 0-9m) altogether.According to above-mentioned mode classification the result of adopting data is added up equally, statistical result is as shown in table 5.
The recognition correct rate situation of single goal data when table 5 is worn the 30cm brick wall
Two objective data in the data through walls have been gathered 59 groups (target location is randomly dispersed in the investigative range of 0-9m, and the distance between two targets does not wait at interval, and two targets are in the distance of the shoulder breadth that on the direction of exploring antenna, staggers) altogether from 1m to 6m.According to above-mentioned mode classification the result of adopting data is added up equally, statistical result is as shown in table 6.
Table 6 is worn the recognition correct rate situation of 30cm brick wall two objective data
Three target datas in the data through walls have been gathered 24 groups altogether, and (target location is randomly dispersed in the investigative range of 0-9m; Distance between any two targets does not wait from 1m to 6m at interval, and any two targets are in the distance of the shoulder breadth that on the direction of exploring antenna, all staggers).According to above-mentioned mode classification the result of adopting data is added up equally,, in algorithm, also only considered to maximum three identification of targets and differentiation, so there is not the situation of erroneous judgement because all data are three target datas.Statistical result is as shown in table 7.
Table 7 is worn the recognition correct rate situation of 30cm brick wall three target datas
Result after being handled by above various data to the different target number adds up; It is 91% to 23 groups of driftlessness discriminating data accuracy that many quiet target recognitions and distance are distinguished algorithm; To 27 groups of single goal discriminating data accuracy is 56%; To 59 groups of two objective discriminating data accuracy is 73%, and it is 46% that 24 group of three target data differentiated accuracy.This method is the highest to the recognition correct rate of driftlessness data, and is minimum to three identification of targets accuracy.
Should be understood that, concerning those of ordinary skills, can improve or conversion, and all these improvement and conversion all should belong to the protection domain of accompanying claims of the present invention according to above-mentioned explanation.
Claims (3)
1. radar life-detector that can be used for the single channel of multiple target detection based on UWB; It is characterized in that; Comprise UWB bioradar front end and computing unit, the mid frequency and the bandwidth of said UWB bioradar front end are all 500MHz, and the wave beam angle of coverage is 60 °; Said UWB bioradar front end comprises transmitting antenna, reception antenna, pulse oscillator, electromagnetic pulse generator, Sampling Integral device; Pulse oscillator produces pulse signal, and this signal triggering electromagnetic pulse generator produces burst pulse, and radiate through transmitting antenna; Reflected signal is delivered to the Sampling Integral device through reception antenna; The pulse signal that is produced by pulse oscillator produces range gate through delay circuit and range gate generator simultaneously; Select to received signal, signal is detected through accumulation back small-signal through the Sampling Integral circuit; And via amplifier and wave filter amplify, filtering; After the sampling of high-speed a/d capture card, send into computing unit again, the signal that collects is carried out analyzing and processing, finally extract a plurality of human body target life-informations and each target range by computing unit; Said computing unit comprises signal integration module, signal decomposition reconstructed module, digital filtering module and numerical differentiation module, spatial-frequency analysis module; Said signal integration module is carried out integration to signal on distance; Said signal decomposition reconstructed module signal is broken up decompose, reconstruct; Synthetic target echo signal and distance signal; Said digital filtering and numerical differentiation module are carried out digital filtering and numerical differentiation to target echo signal, and said spatial-frequency analysis module is used for carrying out spatial-frequency analysis according to target echo signal after digital filtering and the numerical differentiation and distance signal, obtains the target one-dimensional distance; Said numerical differentiation module adopts 60 exponent number word differentiators, and the computational process of differentiator is shown in formula:
In the formula: y is the output signal, and x is an input signal, and m is an exponent number, and n is the sequence number of point;
Described radar life-detector also comprises the crest discrimination module, and whether said crest discrimination module is used for putting the threshold decision crest location according to spatial-frequency analysis result and systemic presupposition has human body target to exist; Concrete method of discrimination is: find out maximum 12 sections of energy value in 22 sections, and find out all energy crests in these 12 sections, be designated as E respectively by the energy size
Peak1, E
Peak2, E
Peak3... crest is such regulation: promptly the energy value of this section is greater than adjacent two sections energy values, and then this section is a crest; Find out after the energy crest, the section sequence number of record crest place section is used for the subsequent calculations target range; 8 sections minimum the average energy value of energy value is designated as in calculating 22 sections: E
Mean, utilize crest energy and the average energy value of minimum 8 sections to make comparisons to confirm the number of target; Compare threshold is following:
(1) if the energy E of energy crest 1
Peak1Minimum average B configuration energy value E greater than 4 times
Mean, i.e. E
Peak1＞4E
Mean, think that then crest 1 position has target to exist, the distance of target is calculated by the sequence number of crest 1;
(2) if the energy E of energy crest 2
Peak2Minimum average B configuration energy value E greater than 3 times
Mean, i.e. E
Peak2＞3E
Mean, think that then crest 2 positions have target to exist, the distance of target is calculated by the sequence number of crest 2;
(3) if the energy E of energy crest 3
Peak2Minimum average B configuration energy value E greater than 2.5 times
Mean, i.e. E
Peak3＞2.5E
Mean, think that then crest 3 positions have target to exist, the distance of target is calculated by the sequence number of crest 3.
2. radar life-detector according to claim 1 is characterized in that, said wave filter adopts that gain is 1, passband is a 0.08-5000Hz hardware filtering circuit.
3. radar life-detector according to claim 1 is characterized in that, it is the hamming window Finite Impulse Response filter of 0.5Hz that said digital filtering module adopts 160 rank, cut-off frequency.
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| CN112816960B (en) * | 2021-02-03 | 2023-03-17 | 森思泰克河北科技有限公司 | In-vehicle life detection method, device, equipment and storage medium |
| CN114758476A (en) * | 2022-04-18 | 2022-07-15 | 上海物骐微电子有限公司 | Activity information monitoring method, device, system, equipment and storage medium |
| CN115657000A (en) * | 2022-12-28 | 2023-01-31 | 成都空御科技有限公司 | Low-slow small-adaptive radar detection system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2529267Y (en) * | 2002-03-15 | 2003-01-01 | 中国人民解放军第四军医大学 | Radar non-contact life parameter detecting device |
| US7567200B1 (en) * | 2006-04-27 | 2009-07-28 | Josef Osterweil | Method and apparatus for body position monitor and fall detect ion using radar |
| CN201402322Y (en) * | 2009-04-30 | 2010-02-10 | 薛亚明 | Radar life detection instrument |
-
2010
- 2010-10-11 CN CN2010105023196A patent/CN102008291B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2529267Y (en) * | 2002-03-15 | 2003-01-01 | 中国人民解放军第四军医大学 | Radar non-contact life parameter detecting device |
| US7567200B1 (en) * | 2006-04-27 | 2009-07-28 | Josef Osterweil | Method and apparatus for body position monitor and fall detect ion using radar |
| CN201402322Y (en) * | 2009-04-30 | 2010-02-10 | 薛亚明 | Radar life detection instrument |
Non-Patent Citations (2)
| Title |
|---|
| 张杨等.生物雷达多静目标检测识别技术初步研究.《第八届全国信号与信息处理联合学术会议论文集》.2009,摘要、第2部分、图1-2. * |
| 张杨等.超宽谱雷达人体目标识别的新方法.《信息化研究》.2010,第36卷(第8期),摘要、第1部分,第2部分、图1-2. * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103616729A (en) * | 2013-11-06 | 2014-03-05 | 中国人民解放军第四军医大学 | UWB bio-radar-based multiple-human body object estimation method and system |
| CN103616729B (en) * | 2013-11-06 | 2016-05-18 | 中国人民解放军第四军医大学 | A kind of multiple human body target evaluation methods and system based on UWB bioradar |
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