CN110879389B

CN110879389B - Multi-human-body target identification and positioning method based on multi-base IR-UWB (infrared-ultra wide band) biological radar signals

Info

Publication number: CN110879389B
Application number: CN201911018557.7A
Authority: CN
Inventors: 张杨; 王健琪; 张自启; 梁福来; 吕昊; 李钊; 于霄; 薛慧君; 安强
Original assignee: Fourth Military Medical University FMMU
Current assignee: Fourth Military Medical University FMMU
Priority date: 2019-10-24
Filing date: 2019-10-24
Publication date: 2022-09-16
Anticipated expiration: 2039-10-24
Also published as: CN110879389A

Abstract

The invention belongs to the technical field of biological radars, and discloses a multi-human body target identification and positioning method based on multi-base IR-UWB biological radar signals. According to the invention, a set of target discrimination program combining two characteristic parameters of energy-to-noise ratio and correlation coefficient mean is adopted according to signal characteristics of human respiration and the like, so that the recognition and the distance direction positioning of a multi-human target are realized, and the target recognition accuracy is improved.

Description

Multi-human-body target identification and positioning method based on multi-base IR-UWB (infrared-ultra wide band) biological radar signals

Technical Field

The invention belongs to the technical field of biological radars, and particularly relates to a multi-human-body target identification and positioning method based on multi-base IR-UWB biological radar signals.

Background

The biological radar is a technology which can realize the functions of detecting, monitoring vital signs, imaging and positioning of a vital body and the like by extracting signals related to the vital signs in radar echoes, in a non-contact and long-distance manner and penetrating a certain medium. The principle of the method is that a radar transmits electromagnetic waves to a human body, the electromagnetic waves are modulated by human body physiological activities such as respiration, heartbeat and body movement and then are reflected to a radar receiving antenna, physiological and biological information about a human body target is obtained from radar echoes through a certain signal processing technology after the radar receives the electromagnetic waves, and the information comprises physiological parameters, waveforms, images, target positions and the like. Due to the advantages, the biological radar technology shows great superiority and wide application prospect in the fields of post-disaster rescue, medical monitoring, anti-terrorism maintenance, battlefield search and rescue and the like.

The multi-target identification and positioning in the biological radar detection are difficult points and a bottleneck technology which restricts the biological radar technology from further moving to practical use, and the practical value of the existing biological radar prototype is influenced. The achievement of multi-human-body target identification and positioning based on the biological radar is lacked in the prior art, so that the detection efficiency in non-contact life detection can be greatly improved by solving the multi-human-body target identification and positioning problem, the multi-target detection and positioning problem in actual detection is met, the application range of the biological radar can be expanded, and further development of the biological radar industry is promoted.

Disclosure of Invention

The invention aims to provide a multi-human-body target identification and positioning method based on multi-base IR-UWB (infrared-ultra wide band) biological radar signals, which is used for solving the problem that the identification and positioning of multi-human-body targets realized by biological radars is lacked in the prior art.

In order to realize the task, the invention adopts the following technical scheme, comprising the following steps:

step 1: the transmitting antenna of the three-channel IR-UWB biological radar transmits radar pulse to a target position, the three channels comprise a middle channel, a left channel and a right channel, the radar pulse is reflected at the target position, and a middle channel radar echo signal E (m, n) and a left channel radar echo signal E are obtained through three receiving antennas of the three-channel IR-UWB biological radar _left (m, n) and a right channel radar echo signal E _right (m, n), wherein m is a sampling ordinal number in a fast time direction, n is a sampling ordinal number in a slow time direction, and m and n are positive integers;

step 2: e (m, n) and E obtained in the step 1 _left (m, n) and E _right (m, n) respectively obtaining intermediate channel energy signals E through signal preprocessing ₆ (l) Left channel energy signal E _6left (l) And right channel energy signal E _6right (l)；

And step 3: for E obtained in step 2 ₆ (l)、E _6left (l) And E _6right (l) Performing inflection point extraction to obtain a secondary inflection point signal E of the middle channel ₉ (l) Left channel quadratic inflection point signal E _9left (l) And the right channel quadratic inflection point signal E _9right (l)；

And 4, step 4: obtaining the first three values and the positions of the first three values of each secondary inflection point signal obtained in the step 3, wherein the first three values comprise a maximum value, a second large value and a third large value;

step 5.1: calculation of E ₉ (l) The peak-to-background ratio and the correlation coefficient mean of the first three values of (a) by E ₉ (l) The number of the targets obtained by the peak-to-background ratio and the mean value of the correlation coefficient of the first three values comprises:

A) if V _EtoB1 <σ _1N Or σ _1N ≤V _EtoB1 <σ _1Y And r is _m1 <σ _rm1 If the result is no target, judging that the target is not available;

B) if σ is _1N ≤V _EtoB2 <σ _2N Or σ _2N ≤V _EtoB2 <σ _2Y And r is _m2 <σ _rm1 If yes, judging that the result is a single target;

C) if r is _m3 >σ _rm3 Or σ _rm2 <r _m3 ≤σ _rm3 And V is _EtoB3 ≥σ _1Y If yes, judging that the result is three targets;

D) under other conditions except A) B) C), the judgment result is a dual target;

wherein, V _EtoB1 Represents E ₉ (l) Peak-to-background ratio of maximum, V _EtoB2 Represents E ₉ (l) Second largest peak-to-background ratio, V _EtoB3 Represents E ₉ (l) Third highest peak-to-background ratio, r _m1 Mean value of the correlation coefficient representing the maximum value, r _m2 Mean value of the correlation coefficient, r, representing the second largest value _m3 Mean value of the correlation coefficient, σ, representing the third largest value _1N Representing a no target threshold, σ _1Y Indicating a target threshold, σ _2N Representing two target thresholds, σ _2Y Represents a multiple target threshold value and _1N <σ _2N <σ _1Y <σ _2Y ，σ _rm1 、σ _rm2 and σ _rm3 Represents a correlation threshold value and σ _rm2 <σ _rm1 <σ _rm3 ；

Step 5.2: according to the number of the targets identified in the step 5.1, combining with E ₉ (l)、E _9left (l) And E _9right (l) The positions of the first three values determine the radial distance of the target and the azimuth of the target, and the method comprises the following steps:

a) if the recognition result is a single target, pass E ₉ (l) Determines the radial distance of the target by E _9left (l) And E _9right (l) The position of the maximum value of (a) determines the orientation of the target;

b) if the identification result is a double target, passing through E ₉ (l) Determines the radial distance of the first target by E _9left (l) And E _9right (l) Determines the position of the first object and then passes E ₉ (l) Determines the target radial distance of the second, by E _9left (l) And E _9right (l) Determines the bearing of the second object;

c) if the recognition result is three targets, pass E ₉ (l) Determines the radial distance of the first target by E _9left (l) And E _9right (l) Determines the position of the first object and then passes E ₉ (l) Determines the target radial distance of the second, by E _9left (l) And E _9right (l) Determines the position of the second object, and finally passes through E ₉ (l) Determines the radial distance of a third target by E _9left (l) And E _9right (l) Determines the bearing of a third target;

and identifying and positioning the targets according to the number of the targets, the radial distance of the targets and the azimuth of the targets.

Further, in step 5.1, σ _1N ＝2，σ _1Y ＝3，σ _2N ＝2.4，σ _2Y ＝3.5，σ _rm1 ＝0.8、σ _rm2 0.75 and σ _rm3Y ＝0.88。

Further, the signal preprocessing in step 2 includes the following sub-steps:

step 2.1: for E (m, n), E _left (m, n) and E _right (m, n) performing distance accumulation respectively;

step 2.2: multiplying the signals accumulated in the step 2.1 by an exponential gain curve G (l) of a formula I to perform attenuation compensation;

wherein, V _h Representing the ratio, P, of the maximum value of the radar echo data to the amplitude of the reflected echo of the human target _human Is the human target position, L represents the fast time sequence number after the accumulation of the distance, L is 1,2, …, L is a positive integer;

step 2.3: removing static clutter from the signal subjected to attenuation compensation in the step 2.2;

step 2.4: performing linear trend elimination on the signals after the static clutter is removed in the step 2.3;

step 2.5: low-pass filtering the signal with the linear trend eliminated in the step 2.4 in a slow time dimension;

step 2.6: accumulating the signals subjected to low-pass filtering in the step 2.5 along a slow time axis to obtain an intermediate channel energy signal E ₆ (l) Left channel energy signal E _6left (l) And right channel energy signal E _6right (l)。

Further, the inflection point extraction of step 3 includes the following sub-steps:

step 3.1: for E obtained in step 2 ₆ (l)、E _6left (l) And E _6right (l) Removing direct wave to obtain E ₇ (l)、E _7left (l) And E _7right (l)；

Step 3.2: extracting inflection points from the signals obtained in the step 3.1 to obtain a primary inflection point signal E ₈ (l)、E _8left (l) And E _8right (l)；

Step 3.2: extracting inflection points from the signals obtained in the step 3.2 to obtain a secondary inflection point signal E of the intermediate channel ₉ (l) Left channel quadratic inflection point signal E _9left (l) And the right channel quadratic inflection point signal E _9right (l)。

Further, step 4 comprises the following substeps:

step 4.1: obtaining the maximum value of each secondary inflection point signal obtained in the step 3, and removing the tail of the maximum value;

and 4.2: obtaining a second large value of each secondary inflection point signal obtained in the step 3, and removing the tailing of the second large value;

step 4.3: and obtaining a third maximum value of each secondary inflection point signal obtained in the step 3.

When the first three maximum values are obtained, the adjacent 16 continuous amplitude signals after the maximum value position and the second maximum value position are set to zero, and the most trailing is removed.

Furthermore, the mean value r of the correlation coefficient at the maximum value is calculated by formula II _m1 The mean value r of the correlation coefficient at the second largest value _m2 And the mean value r of the correlation coefficient at the third largest value _m3 ：

Where i denotes the index of the correlation coefficient and i is 1,2,3,4,5,6, k denotes the index of the first three largest values and k is 1 denotes the index of the largest value, k is 2 denotes the index of the second largest value, k is 3 denotes the index of the third largest value, r denotes the index of the third largest value _ik The correlation coefficient, r, representing the first three largest values _mk Mean value of the correlation coefficients representing the first three major values, E ₅ (l, q) represents the signal of the intermediate channel obtained in step 2.5 after low-pass filtering in the slow time dimension, E _maxk (q) represents E ₅ Signals at the first three maximum positions of (l, q), E _(maxk+(i-4)) (q) represents a group represented by ₅ (l, q) signals of the first three positions adjacent to the first three maximum positions, (E) _(maxk+(i-3)) (q) represents and E ₅ (l, Q) signals at the last three positions adjacent to the first three maximum positions, Q representing E ₅ (l, Q) the total number of sampling points of the signal in the slow time direction and Q is a positive integer, Q represents the Q-th signal sampling point in the slow time direction and Q is a positive integer.

Further, in step 5, by E _9left (l) Position l of the first three major values _left-maxk And E _9right (l) Position l of the first three major values _right-maxk Determining the orientation of the target, comprising:

a) if | l _left-maxk -l _right-maxk If the | is less than or equal to 2, the target is on a central axis, and the central axis is a connecting line of the receiving antenna and the middle channel transmitting antenna;

b) if (l) _left-maxk -l _right-maxk ) If the central axis is less than or equal to-2, the target is in the left area of the central axis, and the left area is the area where the left channel transmitting antenna on one side of the central axis is located;

c) if (l) _left-maxk -l _right-maxk ) And if the target is in the right area of the central axis, the right area is the area where the right channel transmitting antenna on one side of the central axis is located.

Compared with the prior art, the invention has the following technical characteristics:

1. according to the invention, a set of target discrimination program combining two characteristic parameters of energy-to-noise ratio and correlation coefficient mean is adopted according to signal characteristics of human respiration and the like, so that the recognition and the distance direction positioning of a multi-human target are realized, and the target recognition accuracy is improved.

2. The invention adopts the steps of pre-judging the position of a human body target, and then carrying out attenuation compensation on the signal in the distance direction according to the calculated exponential gain curve G (l), thereby solving the problem of energy attenuation generated along with the increase of the distance in the transmission process of the radar signal, effectively reducing the miss-judgment rate of the far-end target, and compared with a segmented linear gain compensation mode, the gain compensation adopted by the invention is more accurate.

3. The invention adopts a three-channel radar detection mode, the middle channel identifies and positions at most three human body targets in the distance direction, and the two channels position the targets in the direction, thereby realizing the detection identification and positioning of the three targets (the positioning result contains direction information), and improving the accuracy of multi-target two-dimensional positioning.

4. The invention influences V by research _EtoBk And influence of _mk The optimal target existence threshold and the optimal correlation threshold are found, and the recognition effect is better improved through setting the optimal threshold.

Drawings

FIG. 1 is a schematic block diagram of a three channel IR-UWB biological radar system;

FIG. 2 is a schematic diagram of a three-channel IR-UWB bio-radar system through-wall detection;

FIG. 3 is a schematic diagram of a two-dimensional matrix of radar echo signals;

FIG. 4(a) is a fast time signal waveform diagram;

FIG. 4(b) is a waveform diagram of a slow-time signal;

FIG. 5 is a schematic diagram of IR-UWB radar detecting human breath;

FIG. 6 is a pulse echo diagram of human respiration;

FIG. 7 is a flow chart of a signal pre-processing algorithm;

FIG. 8 is a schematic diagram of a simulated radar echo signal;

FIG. 9 is a schematic diagram of a simulated echo signal after static clutter removal;

FIG. 10 shows a quadratic inflection point signal E ₉ (l) Three maxima of (a) and a "tailing" phenomenon;

FIG. 11 is a schematic diagram of the second inflection point signal E (l) after the "tail" is removed from the middle channel;

fig. 12 is a diagram of the three-target recognition positioning results.

FIG. 13 is a flowchart of a method for determining the number of human targets;

Detailed Description

The technical terms appearing in the present invention are explained first:

the biological radar technology comprises the following steps: the method comprises Continuous Wave (CW) radar and Ultra-wide band (UWB), wherein the UWB is the mainstream of the technical research of the existing biological radar due to the characteristics of high distance resolution, target identification capability and the like. An Impulse-radio Ultra-wide band (IR-UWB) becomes a research hotspot in the fields of post-disaster search and rescue and the like due to the characteristics of excellent performance, simple structure and the like, so that the identification and positioning of multiple human body targets need to be realized by adopting a multi-channel IR-UWB biological radar system.

Slow time: the detection time of the radar for the target is in seconds(s).

Fast time: the time taken for the pulse to propagate is in nanoseconds (ns).

In actual detection, as shown in fig. 3, echo signals sampled by the IR-UWB radar are sampled, integrated and amplified, and then stored in a two-dimensional matrix R (m, n), where m is a row vector, n is a column vector, the horizontal axis in the figure represents slow time, the vertical axis represents fast time, and the fast time can be converted into a detection distance in meters (m) according to the propagation speed of electromagnetic waves in a medium.

The calculation relation of the fast time and the distance is as follows: distance (m) is fast time (ns) × propagation speed of electromagnetic wave in the medium (m/ns)/2.

Fast time signal: at a certain moment, the signal along the fast time dimension, i.e. the column vector of the two-dimensional matrix.

Slow time signal: at some distance point, the signal along the slow time dimension, i.e. the row vector of the two-dimensional matrix.

Inflection point: mathematically, it refers to the point of changing the curve in the upward or downward direction, intuitively speaking the point of inflection where the tangent line crosses the curve (i.e. the concave-convex demarcation point of the curve).

The invention is realized by the following principle: when a transmitting signal of the biological radar irradiates a static object, a radar echo signal is a stable fixed value, but when the biological radar irradiates a living body such as a human or an animal, the original radar echo signal fluctuates due to the micro fluctuation of the body surface caused by the respiration of the living body, and therefore the living body can be detected through the micro fluctuation.

The invention discloses a three-channel biological radar system with three transmitting and receiving channels, which is characterized in that a structural block diagram of the system is shown in figure 1, and a Pulse generator generates Pulse signals at a certain Pulse Repetition Frequency (PRF) under the control of a PRF oscillator. The generated pulse signal is divided into two paths: one path is conditioned and shaped into a bipolar pulse signal through a transmitting circuit and radiated by a transmitting antenna; the other path of pulse signal is sent to a delay unit to generate a series of range gates with adjustable delay time under the control of a microprocessor, the range gates are actually sampling pulse signals, the duration is very short, and under the triggering of the signals, a receiving circuit can selectively receive and sample radar echoes. The signal radiated by the transmitting antenna is reflected when meeting an object, the reflected radar echo is received by the receiving antenna and then sent to a receiving circuit to be selectively sampled, integrated and amplified under the triggering of a range gate, and then the radar echo signal is formed through an Analog to Digital Converter (ADC). And the radar echo signals are sent to a processing display terminal for signal processing and result display through a WiFi module under the control of the microprocessor. The IR-UWB biological radar system consists of three channels: each transmitting antenna and one receiving antenna form a transceiving channel, and three receiving antennas shown in fig. 1 can be respectively combined with the transmitting antenna to form three channels.

FIG. 2 is a schematic plan view of a three-channel IR-UWB biological radar system for multi-target through-wall detection and identification, wherein each radar antenna is placed in close proximity to a brick wall with a thickness of 24cm (the penetration range of the multi-channel radar is brick walls within 200 cm), and is 1.15 m away from the ground (the average height of the breast of an adult is corresponding, the height can be freely adjusted, the radar detection area is a cone space with an opening angle of 120 degrees, and as long as a target can be detected in the detection area), wherein a transmitting antenna Tx and a receiving antenna Rx1 are placed in the middle of the radar system to form a middle channel of the radar system, and data of the middle channel is mainly used for distinguishing the number of the targets and determining the radial distance of each target; the receiving antenna Rx2 and the receiving antenna Rx3 are respectively disposed at 1.2 meters left and 1.2 meters right of the transmitting antenna Tx (the distances between Rx2 and Rx3 and the transmitting antenna are adjustable from 0.3m to 1.2m, different distances correspond to different lateral resolutions, the larger the distance is, the larger the lateral resolution of multiple targets is, the more accurate the positioning is), and form a left channel and a right channel with the transmitting antenna, and data of the left channel and the right channel are mainly used for positioning the direction of the targets.

Fig. 4(a) and (b) show waveforms of a radar echo fast time signal and a slow time signal, respectively. The window width of the IR-UWB radar time window determines the length of the fast time signal, and in the experimental setting of the invention, the time window width of one fast time signal is set to be 80ns, and the corresponding detection distance is in the range of 12 m. Each fast time signal consists of 8192 sampling points, and the time interval Ts between every two fast time signals is 0.0625s, namely, the sampling frequency fs of the slow time signal is 1/Ts is 16Hz, so that the requirement of the Nyquist sampling law on the sampling of the human breath signal is met.

Example 1

The embodiment discloses a multi-human target identification and positioning method based on multi-base IR-UWB biological radar signals, which comprises the following steps:

step 1: the transmitting antenna of the three-channel IR-UWB biological radar transmits radar pulse to a target position, the three channels comprise a middle channel, a left channel and a right channel, the radar pulse is reflected at the target position, and a middle channel radar echo signal E (m, n) and a left channel radar echo signal E are obtained through three receiving antennas of the three-channel IR-UWB biological radar _left (m, n) and a right channel radar echo signal E _right (m, n), wherein m is the sampling ordinal number in the fast time direction, n is the sampling ordinal number in the slow time direction, and m and n are positive integers;

And step 3: for E obtained in step 2 ₆ (l)、E _6left (l) And E _6right (l) Inflection point extraction is carried out to obtain a secondary inflection point signal E of the intermediate channel ₉ (l) Left channel quadratic inflection point signal E _9left (l) And the right channel quadratic inflection point signal E _9right (l)；

step 5.1: calculation of E ₉ (l) The peak-to-background ratio and the correlation coefficient mean of the first three values of (1), through E ₉ (l) The number of the targets is obtained by the peak-background ratio and the mean value of the correlation coefficient of the first three values, and the method comprises the following steps:

wherein, V _EtoB1 Represents E ₉ (l) Peak-to-background ratio of maximum, V _EtoB2 Represents E ₉ (l) Second largest peak-to-background ratio, V _EtoB3 Represents E ₉ (l) Third highest peak-to-background ratio, r _m1 Mean value of the correlation coefficient representing the maximum value, r _m2 Mean value of the correlation coefficient, r, representing the second largest value _m3 Mean value of the correlation coefficient, σ, representing the third largest value _1N Representing a no target threshold, σ _1Y Indicates a target threshold value, σ _2N Representing two target thresholds, σ _2Y Represents a multi-target threshold value and _1N <σ _2N <σ _1Y <σ _2Y ，σ _rm1 、σ _rm2 and σ _rm3 Represents a correlation threshold value and σ _rm2 <σ _rm1 <σ _rm3 ；

And step 5.2: according to the number of the targets identified in the step 5.1, combining with E ₉ (l)、E _9left (l) And E _9right (l) First threeThe large value of the position determines the radial distance of the target and the azimuth of the target, including:

b) if the identification result is a double target, passing through E ₉ (l) Determines the radial distance of the first target by E _9left (l) And E _9right (l) Determining the position of the maximum of (A) and then passing E ₉ (l) Determines the target radial distance of a second one, by E _9left (l) And E _9right (l) Determines the bearing of the second target;

c) if the recognition result is three targets, pass E ₉ (l) Determines the radial distance of the first target by E _9left (l) And E _9right (l) Determines the position of the first object and then passes E ₉ (l) Determines the target radial distance of the second, by E _9left (l) And E _9right (l) Determining the orientation of the second object by the position of the second largest value of (A), and finally passing through (E) ₉ (l) Determines the radial distance of a third target by E _9left (l) And E _9right (l) Determines the bearing of a third target;

Preferably, in step 5.1, σ _1N ＝2，σ _1Y ＝3，σ _2N ＝2.4，σ _2Y ＝3.5，σ _rm1 ＝0.8、σ _rm2 0.75 and σ _rm3Y ＝0.88。

Specifically, the method for obtaining the radar echo signal in step 1 comprises the following steps:

a schematic diagram of IR-UWB radar for detecting human respiration is shown in FIG. 5, assuming that the initial distance between the chest wall surface of a human target and the radar is d ₀ The respiration of the human body causes the periodic expansion and contraction of the thoracic cavity, and in general, the displacement of the chest wall during the respiration of the human body is a sine function x (t) related to slow time, so that the actual distance d (t) between the chest wall surface of the human body target and the radar is determined according to the respiratory frequency f (t) of the human body _r At d ₀ The vicinity varies periodically:

d(t)＝d ₀ +x(t)＝d ₀ +Arsin(2πf _r t)

wherein t represents slow time, x (t) represents the change of the displacement of the chest wall when the human body breathes, and Ar represents the maximum amplitude of the human body breathing.

Since the environment in the detection range is static and the human target remains stationary, only the chest wall motion caused by breathing, the impulse response h (t, τ) of the radar system will vary over time as does the breathing motion:

wherein t represents a slow time, τ represents a fast time,

pulse echo component representing static background object, where _i And τ _i Amplitude and delay in the fast time dimension, alpha, of the ith static target pulse echo, respectively _v δ(τ-τ _v (t) pulse echo component representing respiratory motion of a human target, where α _v For the amplitude of the pulse echo, τ _v (t) is the time delay change of the human body target pulse echo in the fast time dimension, and can be expressed as:

where c is the propagation velocity of the electromagnetic wave in vacuum,. tau _r For maximum delay of respiratory motion in the fast time dimension, τ ₀ The delay of the radar wave between the surface of the human chest wall and the radar (initial distance), i.e.

If pulse distortion and other non-linear effects are ignored, the radar echo signal can be viewed as a convolution of the radar transmit pulse and the system impulse response. Then, without considering the existence of noise, the echo signal of the radar at time t is:

in the formula, p (τ) is a radar emission pulse, and "×" represents a convolution operation.

To explain the signal model more clearly, a pulse echo diagram of human respiration is shown in fig. 6. As can be seen from the figure, the time delay of the pulse echo of the human breath in the fast time dimension is changed with the slow time, while the pulse echo time delay of the static target is unchanged.

In practical detection, the IR-UWB radar system is at each discrete time tau mT along the fast time direction _f (M1, 2.. said, M) samples each point on each pulse waveform, while at each discrete time t nT in the slow time direction _s (N ═ 1, 2.., N) the primary pulse waveform is sampled. The sampled echo signals are stored as an (M × N) two-dimensional array E, and elements in the array E are represented by E (M, N):

the signal E (m, n) is a two-dimensional signal, m is the sampling number in the fast time direction, where n is the sampling number in the slow time direction.

Specifically, the signal preprocessing in step 2 includes the following sub-steps:

step 2.1: for E (m, n), E _left (m, n) and E _right (m, n) distance accumulation is performed separately, taking the middle channel as an example:

according to the IR-UWB radar system adopted in the research, the sampling point number is 8192, the time window is 80ns, and if the original echo data of the radar is directly processed, the calculation amount is large, the calculation is slow, and the real-time performance of detection and identification is not good. In two-dimensional original echo data E (m, n) received by the IR-UWB radar, because the modulation modes of radar echoes at adjacent distance points in the fast time dimension are substantially the same and have a certain correlation, the original echo data E (m, n) of the radar can be first subjected to distance accumulation along the fast time dimension without affecting useful information:

in the formula E ₁ (L, n) (L ═ 1,2, … L) is echo data after distance accumulation, Q is the window width accumulated along the fast time dimension, L is the number of distance points in the fast time dimension after accumulation, and

wherein

Indicating a rounding down. A large number of experimental studies show that the algorithm achieves the optimal effect when the window width Q is 40. Then the slow time signal at 8192 corresponding range points of the raw echo data E (m, n) is reduced to E after range accumulation ₁ 200 of (L, n) (namely L is 200) correspond to the fast time signal on the distance point, thereby greatly reducing the calculation amount in the radar data processing process, reducing the calculation time required by detection and improving the detection efficiency. Meanwhile, distance accumulation along the fast time dimension is equivalent to smooth filtering of the fast time signal of the radar echo, and high-frequency interference on the fast time signal can be suppressed to a certain extent.

wherein, V _h Ratio, P, representing the maximum value of the radar echo data and the amplitude of the reflected echo of the human target _human Is the human target position, L represents the fast time sequence number after the accumulation of the distance, L is 1,2, …, L is a positive integer;

because the radar wave is severely attenuated in the medium propagation process, the amplitude of the reflection echo of the interface of the far-end object is greatly reduced, and the far-end object is difficult to detect, the radar echo E after distance accumulation needs to be detected before the interface reflection echo is identified ₁ (l, n) to compensate. The existing ultra-wide spectrum radar (mainly a ground penetrating radar) is provided with an automatic gain adjustment function, and far-end echo data is amplified by performing segmented linear or exponential gain adjustment on radar echoes, but due to the lack of prior knowledge of electromagnetic wave propagation medium interface information, the accuracy of gain calculation is not high, noise is often amplified excessively due to inaccurate gain, and real interface reflection echoes cannot be amplified properly due to small gain, so that the probability of misjudgment and misjudgment of a target is increased greatly.

The way of compensating attenuation by segments needs to calculate different gains according to the possible attenuation of each echo segment, and the calculation process is too complicated, but it is difficult to accurately calculate the gains, and the gains are easily affected by noise, resulting in wrong compensation. In actual detection, a non-compensation mode can be adopted for detecting and calculating the position of a human body target, the position information of the human body target is used as priori knowledge, and electromagnetic waves are exponentially attenuated in a propagation process in a medium, so that the position of the human body target and the corresponding amplitude of a reflection echo are used as compensation references, an exponential gain compensation method is adopted for carrying out attenuation compensation on radar echo data in a fast time dimension, after compensation, signals are processed according to a signal processing flow, and the targets are detected and distinguished.

The gain curve is calculated as follows:

assume an ideal exponential gain curve such as e ^K×τ Where K is an unknown constant. For pretreated numbersAccording to E ₁ Maximum value A of (l, n) _max (also typically the maximum of the radar echo data) divided by the amplitude A of the human target's reflected echo _human (i.e., human target position P) _human Corresponding amplitude in the radar echo data) and the resulting ratio is V _h . The ratio V is _h As radar echo position P _human The ideal gain value can be used for calculating an exponential gain curve changing along with the fast time sequence number l, the calculated exponential gain curve is multiplied by the radar echo data on the fast time axis, attenuation compensation on the radar echo data is realized, and the signal after the attenuation compensation is E ₂ (l, n), and E ₂ (l,n)＝G(l)E ₁ (l,n)。

in the radar type life detection process, the direct wave of the radar and the reflection of a static object in the detection range both form strong background clutter in the radar echo signal, and because the respiratory signal of a human target is very weak, the background clutter is usually submerged, as shown in fig. 3, in the original echo of the radar, the life signal of the human target can hardly be seen, and only the background clutter can be seen. However, under ideal conditions, these background clutter are all static, called static clutter, and only the human target vital signal is time-varying, so the static clutter can be completely filtered out by subtracting the slow time signal mean of the echo, leaving only the human vital signal:

wherein E ₃ And (l, n) is the radar echo signal after background removal.

Fig. 8 shows a two-dimensional radar signal simulated by matlab software, and it can be seen from the figure that static clutter near 15ns and 65ns do not change with slow time, and a respiratory signal of a human target near 40ns changes regularly along the slow time dimension. After static clutter removal is performed on the simulated two-dimensional radar signal by a mean-elimination method, static clutter components in the echo are completely removed, and only a breathing signal of a human target is left, as shown in fig. 9.

the hardware of an IR-UWB radar system is often accompanied by a drift in the echo baseline during data acquisition. The linear baseline drift can cause energy leakage of echo data in a low frequency range, so that the detection and identification of the human target respiratory signal are influenced. The present invention therefore employs Linear Trend cancellation (LTS) to remove Linear baseline drift in the radar echo signal. LTS estimates echo signal E by linear least squares fitting ₃ (l, n) after the dc component and the low frequency linear drift trend in the slow time dimension, subtracting from the echo data:

in the formula E ₄ Representing radar data after LTS processing, E ₃ Representing mean-removed radar data E ₃ (l,n)；E ₄ ^T And E ₃ ^T Respectively, their transposed determinant.

n＝[0,1,2...,N-1] ^T Where y is a determinant of N rows and 2 columns, 1 _N Is a column vector of length N and elements all 1, N being E ₃ The number of fast and medium time signals. After the linear trend is eliminated, E is added ₄ ^T Transpose to obtain E ₄ (l，n)。

because the hardware of the IR-UWB radar system inevitably generates noise in the working process, the noise belongs to high-frequency noise relative to the human breathing signal, and the breathing signal of a human target is a narrow-band low-frequency quasi-periodic signal, therefore, in order to effectively filter high-frequency interference and further improve the signal-to-noise ratio of radar echo, the invention carries out low-pass filtering on the radar echo signal in a slow time dimension:

E ₅ (l,q)＝E ₄ (l,n)*h(t)

in the formula, E ₅ (l, q) is the filtered radar data, "+" denotes convolution operation, and h (t) is the Impulse function of a Finite Impulse Response (FIR) filter. According to the respiratory rate of the human body, the cut-off frequency of the low-pass filter is set to be 0.5Hz, and the order of the filter is 120. The radar echo signal after low-pass filtering is E ₅ (l,q)。

In the experiment, experimental data t is taken every time _s 80 seconds, according to the sampling frequency f of the slow time signal _s As can be seen from 16Hz, each acquired data includes 16 × 80 to 1280 fast time signals, i.e., the radar echo signal after low-pass filtering is E ₅ Q in (l, Q) is t _s f _s 1280, L is a distance-accumulated value of 200(200 is obtained by distance accumulation from 8192 sampling points of the fast time signal, mainly for reducing the calculation amount, the value can be freely determined, and the accumulation to 200 and 1000 points can be less calculated without affecting the signal quality).

Because steps of mean value removal, low-pass filtering and the like in the preprocessing need a convergence process, 200(200 is related to the sum of orders of mean value removal and low-pass filtering in the preprocessing, the lower the order is, the smaller the value can be taken, the higher the order is, the larger the value needs to be taken) fast time signals are not taken as the basis of target detection identification and are eliminated. We will E ₅ After taking absolute values of 1000 (1000 is determined by sampling time, 16Hz sampling frequency corresponds to about 62.5 seconds of data, and the longer the signal is taken, the larger the value is), the fast time signals (200-1200) are accumulated along the slow time axis to form an energy signal E ₆ (l)。

Energy signal E ₆ (l) And (1, 2.., 200) is a one-dimensional signal whose abscissa is the fast time, corresponding to the distance (m), and ordinate is the amplitude of the energy accumulated along the slow time. After the series of signal processing, the amplitude of the energy signal is closely related to the vital signal of the living body, and the larger the amplitude is, the stronger the vital micro-motion signal at the distance is, and the more likely it is to be a human body or a biological target.

step 3.1: for E obtained in step 2 ₆ (l)、E _6left (l) And E _6right (l) Removing direct wave to obtain E ₇ (l)、E _7left (l) And E _7right (l) Taking the middle channel as an example:

removing direct waves to eliminate the influence of direct waves between the transmitting and receiving antennas on the target discrimination, namely E ₆ (l) The first 50 points are set to zero (the zero setting range is the first 10-50 points), and a new energy signal E obtained after the direct wave is removed is formed ₇ (l)；

Step 3.2: extracting inflection points from the signals obtained in the step 3.1 to obtain a primary inflection point signal E ₈ (l)、E _8left (l) And E _8right (l) Taking the middle channel as an example:

finding the energy signal E ₇ (l) All the points satisfying the following formula are called primary inflection points, the numerical values corresponding to the points not satisfying the inflection point condition are set to zero, and the points satisfying the condition are stored in a new one-dimensional array according to the original amplitude and the original position to form a primary inflection point signal E ₈ (l)；

By removing the energy signal E after the direct wave, due to the complexity of the propagation of the electromagnetic wave ₇ (l) It is difficult to find the detected object directly from the signal. It is therefore desirable to remove interference, one of the main interferers in radar returns that affects target identification is side lobe interference near the peak, and the side lobes of the peak exhibitA feature of attenuation to both sides. Therefore, in the step, the peak signal is identified through inflection point extraction, namely when the amplitude of the energy signal at a certain point is larger than the amplitudes of the signals at the adjacent points on the left side and the right side of the certain point, the point is judged to be a primary inflection point. The first inflection point discrimination rule is as follows:

E ₇ (l)＞E ₇ (l+1)∩E ₇ (l)＞E ₇ (l-1)

step 3.2: extracting inflection points from the signals obtained in the step 3.2 to obtain a secondary inflection point signal E of the intermediate channel ₉ (l) Left channel quadratic inflection point signal E _9left (l) And the right channel quadratic inflection point signal E _9right (l) Taking the middle channel as an example:

finding a first inflection point signal E ₈ (l) All points satisfying the following formula are called secondary inflection points, the numerical values corresponding to the points not satisfying the secondary inflection point condition are set to zero, and the points satisfying the condition are stored in a new one-dimensional array according to the original amplitude and the original position to form a secondary inflection point signal E ₉ (l)。

From the actual processing result, the first inflection point signal E ₈ (l) Interference still exists, so that targets cannot be accurately identified, and particularly multiple targets cannot be accurately identified. Thus, as an optimized implementation, for the first inflection point signal E ₈ (l) Performing secondary inflection point extraction to obtain secondary inflection point signal, i.e. retaining primary inflection point signal E ₈ (l) All points satisfying the following condition are identified as quadratic inflection points:

E ₈ (l)＞E ₈ (l+a)∩E ₈ (l)＞E ₈ (l-b)

wherein E ₈ (l + a) is E ₈ (l) The first non-zero value on the right, E ₈ (l-b) is E ₈ (l) The first value on the left that is not zero.

Energy signal E after extraction by taking secondary inflection point ₉ (l) The side lobe interference of most peak values is removed, the signal energy of each target position is reserved to the maximum extent, and the multi-target identification capability can be greatly improved.

Specifically, step 4.1: obtaining the maximum value of each secondary inflection point signal obtained in the step 3 through a bubble sorting algorithm, and removing the tailing of the maximum value by setting adjacent 16 continuous amplitude signals behind the position of the maximum value to zero;

step 4.2: obtaining a second large value of each secondary inflection point signal through a bubble sorting algorithm, and removing the tailing of the second large value by setting adjacent 16 continuous amplitude signals behind the second large value to zero;

step 4.3: and obtaining a third maximum value of each quadratic inflection point signal through a bubble sorting algorithm.

Finding out a secondary inflection point signal E of the middle channel by a bubble sorting algorithm ₉ (l) Maximum value E of _9-max1 And the position is marked as l _max1 And the maximum value position l _max1 Then, the adjacent 16 continuous amplitude signals are set to zero to remove the trailing of the maximum value, and then the second large value E is found out through a bubble sorting algorithm _9-max2 And the position is marked as l _max2 A 1 is mixing E ₉ (l) The adjacent 16 continuous amplitude signals behind the position are set to zero to remove the trailing of the second big value, and the third big value E is found out through a bubble sorting algorithm _9-max3 In the position l _max3 ；

The echo signal E of the chest wall of the human body has a certain thickness ₉ (l) Not only does high energy amplitude appear at the position of the surface of the chest wall of a human body, but also energy amplitude at a distance behind the position is higher, and the phenomenon called trailing and trailing influences the number judgment of multiple targets. Due to marked E ₉ (l) Maximum value E _9-max1 At the position l _max1 In order to remove the "tail" generated by the target at the position, the maximum value position l is used _max1 After which the adjacent 16 consecutive amplitude signals are zeroed.

Respectively processing the left channel and the right channel according to the method, and successively finding out a secondary inflection point signal E of the left channel _9left (l) The first three maxima E of _9left-max1 、E _9left-max2 、E _9left-max3 (i.e., signal E) _9left (l) Maximum value, second maximum value and third maximum value) of the search space, wherein the maximum value and the second maximum value need to remove the tailing after being found, and then the next maximum value is searched, and the positions of the first three maximum values are respectively recorded as E _9left-max1 、E _9left-max2 、E _9left-max3 。

Finding out a secondary inflection point signal E of a right channel _9right (l) The first three maxima E of _9rihgt-max1 、E _9rihgt-max2 、E _9rihgt-max3 (i.e., signal E) _9right (l) Maximum value, second maximum value and third maximum value) of the search space, wherein the maximum value and the second maximum value need to remove the tailing after being found, and then the next maximum value is searched, and the positions of the first three maximum values are respectively marked as E _9right-max1 、E _9right-max2 、E _9right-max3 。

Specifically, obtain E ₉ (l) Peak-to-background ratio V of the maximum _EtoB1 Second highest peak-to-background ratio V _EtoB2 Third highest peak-to-background ratio V _EtoB3 Wherein, B _ave Which represents the mean value of the background,

and E _9-max1 Represents E ₉ (l) Maximum value of,

And E _9-max2 Represents E ₉ (l) The second maximum value of,

And E _9-max3 Represents E ₉ (l) A third maximum value of;

preferably, the mean value r of the correlation coefficient at the maximum is calculated by formula II _m1 The mean value r of the correlation coefficient at the second largest value _m2 And the mean value r of the correlation coefficient at the third largest value _m3 ：

Where i denotes the index of the correlation coefficient and i is 1,2,3,4,5,6, k denotes the index of the first three largest values and k is 1 denotes the index of the largest value, k is 2 denotes the index of the second largest value, k is 3 denotes the index of the third largest value, r denotes the index of the third largest value _ik The correlation coefficient, r, representing the first three largest values _mk Mean value of the correlation coefficients representing the first three major values, E ₅ (l, q) represents the intermediate channel signal obtained in step 2.5 after low-pass filtering in the slow time dimension, E _maxk (q) represents E ₅ Signals at the first three maximum positions of (l, q), E _(maxk+(i-4)) (q) represents a group represented by ₅ (l, q) signals of the first three positions adjacent to the first three maximum positions, (E) _(maxk+(i-3)) (q) represents a group represented by ₅ (l, Q) signals at the last three positions adjacent to the first three maximum positions, Q representing E ₅ (l, Q) the total number of sampling points of the signal in the slow time direction and Q is a positive integer, Q represents the Q-th signal sampling point in the slow time direction and Q is a positive integer.

If the sampling time is 60s, the total number of sampling points Q of the signal is 16 × 60 to 960 in the case where the sampling frequency fs of the slow-time signal in this example is 16 Hz.

Through the steps, the intermediate channel secondary inflection point signal E is calculated ₉ (l) The first three maxima E of _9-max1 、E _9-max2 、E _9-max3 And their corresponding locations l _max1 、l _max2 、l _max3 (ii) a The peak-to-background ratio V of the target is calculated _EtoBk And calculating the mean value r of the correlation coefficient of the slow signal at the position and six adjacent slow signals _m1 、r _m2 And r _m3 。

Influence V _EtoBk The relational formula of the relevant factors is as follows, the number of the targets is related to the serial numbers of the first three big values, and each big value corresponds to one target:

a _k for constant coefficient, under the condition of the same background energy level (including noise), the larger the chest wall area of the human target k, the stronger the reflection energy, the larger the respiration amplitude, the stronger the respiration signal energy, and the closer the distance between the human target k and the radar, the stronger the respiration signal energy, at this time, the peak-to-background ratio V of the target k _EtoBk The larger V is, the reverse is _EtoBk The smaller. Under the multi-target scene, the first several maximum values E of the secondary inflection point signal _9-max1 、E _9-max2 、E _9-max3 The amplitude is decreased in sequence, and the background mean value B of the quadratic inflection point signal _ave The same value, therefore, we need to set different thresholds for identification for different targets.

Influence r _mk The relational formula of the relevant factors of (1) is as follows:

b _k for a constant coefficient, the thicker the chest wall thickness (distance from the front chest to the back) of the human target k is, the more the respiratory signal (slow time signal) corresponding to each distance point is reflected as regular respiratory signal, i.e. r _1k ～r _6k All show higher and more consistent values, so the mean value r _mk The higher; the higher the respiratory regularity degree of the human target k is, the higher the correlation between the respiratory signal at the target distance point and the left and right adjacent slow time signals is, and the mean value r _mk The larger the value. According to the characteristics, the following principle is determined to judge the number of the human body targets.

Specifically, in step 5, by E _9left (l) Position l of the first three major values _left-maxk And E _9right (l) Position l of the first three major values _right-maxk Determining the orientation of the target, comprising:

c) if (l) _left-maxk -l _right-maxk ) And if the target is more than 2, the target is in the right area of the central axis, and the right area is the area where the right channel transmitting antenna on one side of the central axis is located.

Specifically, the positioning method is described by taking the target 2 as an example. The position l of the second maximum value of the quadratic inflection point signals of the middle channel, the left channel and the right channel is calculated respectively _max2 、l _left-max2 、l _right-max2 . Radial distance of target 2 is l _max2 The corresponding distance is as follows: 12 x l _max2 200 m, it is determined whether the object 2 is located in the left half area, the right half area or on the radial central axis of the detection area according to the following principle.

a) If | l _left-max2 -l _right-max2 If | is less than or equal to 2 (namely the absolute value of the distance difference between the second maximum positions of the two channels is less than 0.12 meter), the target 2 is on the central axis;

b) if (l) _left-max2 -l _right-max2 ) If the central axis is less than or equal to-2, the target 2 is in the left half area of the central axis;

c) if (l) _left-max2 -l _right-max2 ) If the central axis is more than 2, the target 2 is in the right half area of the central axis;

according to the same principle, the position l of the maximum value of the quadratic inflection point signal of the middle channel, the left channel and the right channel is utilized _max1 、l _left-max1 、l _right-max1 Positioning the target 1 by using the position l of the third maximum value of the secondary inflection point signals of the middle channel, the left channel and the right channel _max3 、l _left-max3 、l _right-max3 The target 3 is located, thus completing the location of all three targets.

Example 2

In the embodiment, a multi-human-body target identification and positioning method and a three-channel IR-UWB biological radar are adopted, and on the basis of the embodiment 1, the following technical characteristics are disclosed, and a wall-through detection positioning verification experiment is carried out in a laboratory. The experimental subjects are three healthy young men (target 1, target 2 and target 3) to carry out detection experiments under the condition of penetrating through a single brick wall, and the number judgment and positioning results of the targets are given. Wherein the target 1 stands still at the position 2.3 meters behind the wall and is deviated to the right side direction; the target 2 stands still at the position 5.7 meters behind the wall and is deviated to the left side direction; the target 3 stands still in the direction of the central axis 7.8 meters behind the wall.

After three channels of IR-UWB biological radar are adopted for through-wall detection, radar echo signals of the three channels are processed and calculated to obtain V _EtoB1 ＝4.01，V _EtoB2 ＝2.93，V _EtoB ＝2.17，r _m1 ＝0.97，r _m2 ＝0.95，r _m1 0.89. The detection result can be judged to be three human body targets according to the multi-target number judging method flow. According to the position l of the three maximum values before the secondary inflection point signal after the tailing is removed from the middle channel _max1 、l _max2 、l _max3 The radial distances of the three targets are known to be 2.34 meters, 5.64 meters and 7.86 meters, respectively. After the radial distance of each target is determined, the three maximum positions before the left channel and the three maximum positions before the right channel are combined to determine that the target 1 is positioned in the right direction of the detection area, the target 2 is positioned in the left direction of the detection area, and the target 3 is positioned in the central axis direction of the detection area. The detection result is consistent with the actual standing distribution condition of the three targets, the radar identification and positioning result is correct, and the three-target identification and positioning result is shown in fig. 13.

Claims

1. A multi-human target identification and positioning method based on multi-base IR-UWB biological radar signals is characterized by comprising the following steps:

step 1: the method comprises the steps that a transmitting antenna of the three-channel IR-UWB biological radar transmits radar pulse to a target position, the three channels comprise a middle channel, a left channel and a right channel, the radar pulse is reflected at the target position, and radar echo signals E (m, n) of the middle channel and radar echo signals E (m, n) of the left channel are obtained through three receiving antennas of the three-channel IR-UWB biological radar _left (m, n) and a right channel radar echo signal E _right (m,n) Wherein m is a sampling ordinal number in a fast time direction, n is a sampling ordinal number in a slow time direction, and m and n are positive integers;

Wherein:

l represents the fast time sequence number after accumulation of distance, L is 1,2, …, and L is a positive integer;

l is the number of distance points in the fast time dimension after accumulation;

A) if V _EtoB1 <σ _1N Or σ _1N ≤V _EtoB1 <σ _1Y And r is _m1 <σ _rm1 If the judgment result is no target;

wherein, V _EtoB1 Represents E ₉ (l) Peak-to-background ratio of maximum, V _EtoB2 Represents E ₉ (l) Second largest peak-to-background ratio, V _EtoB3 Represents E ₉ (l) Third highest peak-to-background ratio, r _m1 Mean value of the correlation coefficient representing the maximum value, r _m2 Mean value of the correlation coefficient, r, representing the second largest value _m3 Mean value of the correlation coefficient, σ, representing the third largest value _1N Representing a no target threshold, σ _1Y Indicating a target threshold, σ _2N Representing two target thresholds, σ _2Y Represents a multi-target threshold value and _1N <σ _2N <σ _1Y <σ _2Y ，σ _rm1 、σ _rm2 and σ _rm3 Represents a correlation threshold value and σ _rm2 <σ _rm1 <σ _rm3 ；

b) if the identification result is a dual target, pass E ₉ (l) Determines the radial distance of the first target by E _9left (l) And E _9right (l) Determining the position of the maximum of (A) and then passing E ₉ (l) Determines the target radial distance of the second, by E _9left (l) And E _9right (l) Determines the bearing of the second object;

c) if the recognition result is three targets, pass E ₉ (l) Determines the radial distance of the first target by E _9left (l) And E _9right (l) Determines the first order of the maximum ofThe target orientation is then passed through E ₉ (l) Determines the target radial distance of the second, by E _9left (l) And E _9right (l) Determines the position of the second object, and finally passes through E ₉ (l) Determines the radial distance of a third target by E _9left (l) And E _9right (l) Determines the bearing of a third target;

2. The multi-human target recognition and positioning method based on multi-base IR-UWB biological radar signals of claim 1, wherein in step 5.1, σ is _1N ＝2，σ _1Y ＝3，σ _2N ＝2.4，σ _2Y ＝3.5，σ _rm1 ＝0.8、σ _rm2 0.75 and σ _rm3 ＝0.88。

3. The multi-human target recognition and positioning method based on multi-base IR-UWB biological radar signals as claimed in claim 1, wherein the signal preprocessing in step 2 comprises the following sub-steps:

wherein, V _h Representing the ratio, P, of the maximum value of the radar echo data to the amplitude of the reflected echo of the human target _human Is the human body target position, L represents the fast time sequence number after the accumulation of the distance, L is 1,2, …, L is a positive integer;

4. The multi-human target recognition and positioning method based on multi-base IR-UWB biological radar signals of claim 1, wherein the inflection point extraction of step 3 comprises the following sub-steps:

step 3.1: for E obtained in step 2 ₆ (l)、E _6left (l) And E _6right (l) Removing the direct wave to obtain E ₇ (l)、E _7left (l) And E _7right (l)；

5. The multi-human target recognition and positioning method based on multi-base IR-UWB biological radar signals as claimed in claim 1, wherein the step 4 comprises the following sub-steps:

step 4.1: obtaining the maximum value of each secondary inflection point signal obtained in the step 3 through a bubble sorting algorithm, and removing the tailing of the maximum value by setting adjacent 16 continuous amplitude signals behind the position of the maximum value to zero;

step 4.3: obtaining a third large value of each secondary inflection point signal through a bubble sorting algorithm;

6. The multi-human target recognition and positioning method based on multi-base IR-UWB biological radar signals of claim 3, wherein the mean value r of the correlation coefficient at the maximum is obtained by formula II _m1 The mean value r of the correlation coefficient at the second largest value _m2 And the mean value r of the correlation coefficient at the third largest value _m3 ：

Where i denotes the index of the correlation coefficient and i is 1,2,3,4,5,6, k denotes the index of the first three largest values and k is 1 denotes the index of the largest value, k is 2 denotes the index of the second largest value, k is 3 denotes the index of the third largest value, r denotes the index of the third largest value _ik The correlation coefficient, r, representing the first three largest values _mk Mean value of the correlation coefficients representing the first three major values, E ₅ (l, q) represents the signal of the intermediate channel obtained in step 2.5 after low-pass filtering in the slow time dimension, E _maxk (q) represents E ₅ Signal at the first three maximum positions of (l, q), E _(maxk+(i-4)) (q) represents a group represented by ₅ (l, q) signals of the first three positions adjacent to the first three maximum positions, (E) _(maxk+(i-3)) (q) represents a group represented by ₅ (l, Q) signals at the last three positions adjacent to the first three maximum positions, Q representing E ₅ (l, Q) the total number of sampling points of the signal in the slow time direction and Q is a positive integer, Q represents the Q-th signal sampling point in the slow time direction and Q is a positive integer.

7. The multi-human target recognition and positioning method based on multi-base IR-UWB biological radar signals of claim 6, wherein in step 5, through E _9left (l) Position l of the first three major values _left-maxk And E _9right (l) Position l of the first three major values _right-maxk Determining the orientation of the target, comprising: