Millimeter wave frequency modulated continuous wave radar detection method for detecting human body in non-line-of-sight area

Abstract

The invention discloses a method for detecting a human body in a non-line-of-sight area by using a millimeter wave frequency modulated continuous wave radar. The method comprises the following steps: scanning a non-line-of-sight environment by frequency modulated continuous wave to obtain main features of the non-line-of-sight environment; establishing an electromagnetic wave propagation multipath model for radar detection in the non-line-of-sight environment according to the main features of the non-line-of-sight environment, and determining a main detection path; the main detection path comprises a primary wall reflection path, a secondary wall reflection path and a round-trip path of a combination of primary and secondary wall reflections; performing radar beam scanning on the non-line-of-sight area according to the main detection path to obtain radar data of different frames; preprocessing the radar data of different frames and locating a target determined to exist to obtain a target position; extracting a respiratory feature of the target position to obtain a respiratory frequency; if the respiratory frequency is determined to be a human body, the target position is a living body in the non-line-of-sight area.

Description

Technical Field

[0001] The present invention belongs to the technical field of millimeter wave radar signal processing, and in particular relates to a method for detecting a human body in a non-line-of-sight area using a millimeter wave frequency modulated continuous wave radar. Background Art

[0002] Radar detection of targets in the NLOS (Non-line-of-sight, NLOS) area has important applications in both civil and military operations; for some application scenarios, it is necessary to determine whether the target in the NLOS area is a living person. When a human body in the NLOS area remains in a hidden state without moving, the radar usually identifies it as a target. A common way to determine whether the target is a person is to perform high-precision imaging of the target in the NLOS area and detect whether there are human features in the target image. If so, it is determined to be a human body.

[0003] High-resolution imaging detection requires high radar data and algorithm resources, while target detection in non-line-of-sight areas is relatively easy. If human vital signs can be detected from radar echoes, it can be determined that the hidden target is a human, without the need for high-resolution imaging. Therefore, combining non-line-of-sight target detection with vital sign detection can achieve the detection of non-moving life forms hidden in non-line-of-sight areas. Among them, the detection of human vital signs in non-line-of-sight areas is a core challenge.

[0004] In terms of human vital signs detection, there have been some research reports. J. Lin et al. from the University of Florida in the United States have achieved non-contact human vital signs detection based on Doppler radar.

[0005] G. Wang et al. from Texas Tech University proposed a non-contact vital signs monitoring system using frequency-modulated continuous wave radar, which successfully acquired breathing and heart rate information.

[0006] E. Schires et al. from the Imperial College of London proposed using body-coupled antennas and ultra-wideband pulse mines to detect hidden human bodies by analyzing ultra-wideband radar responses.

[0007] K.Shyu et al. proposed to use ultra-wideband radar to simultaneously extract heartbeat and breathing information using the first valley value of the characteristic time index and intrinsic mode function (FVPIEF) energy function.

[0008] J. He et al. from the University of Electro-Communications in Japan focused on the distinction between human bodies and synthetic targets in NLOS scenarios by applying the support vector machine (SVM) method to diffraction signals.

[0009] Although the above detection using Doppler radar, ultra-wideband radar and frequency modulated continuous wave radar can successfully retrieve breathing and heart rate information, these vital sign radar detections are all performed in a direct line of sight (LOS) environment. For NLOS environments, weak vital signs will be affected by strong multipath effects, and there is no effective detection solution for how to achieve accurate and effective radar detection. Summary of the invention

[0010] In view of this, the main purpose of the present invention is to provide a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency-modulated continuous wave radar.

[0011] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0012] An embodiment of the present invention provides a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency-modulated continuous wave radar, the method comprising:

[0013] Scan the non-line-of-sight environment through frequency-modulated continuous waves to obtain the main characteristics of the non-line-of-sight environment;

[0014] According to the main characteristics of the non-line-of-sight environment, an electromagnetic wave propagation multipath model for radar detection in a non-line-of-sight environment is established, and a main detection path is determined; the main detection path includes a primary wall reflection path, a secondary wall reflection path, and a round-trip path of a combination of primary and secondary wall reflections;

[0015] Performing radar beam scanning on the non-line-of-sight area according to the main detection path to obtain radar data of different frames;

[0016] Preprocessing the radar data of the different frames and locating the target determined to exist to obtain the target position;

[0017] Extracting respiratory features of the target position to obtain a respiratory frequency;

[0018] If the breathing frequency is determined to be that of a human body, then the target location is a living body in a non-line-of-sight area.

[0019] In the above scheme, the radar data is preprocessed and the target is located to obtain the target position, specifically:

[0020] Perform discrete Fourier transform on radar data of different frames in the distance direction to obtain distance-Fourier transform diagrams of different frames;

[0021] Performing environmental noise suppression processing on the distance-Fourier transform images of different frames to obtain noise-reduced distance-Fourier transform images of different frames;

[0022] Randomly select a frame of denoised distance-Fourier transform image;

[0023] Determine the distance information r of all static targets in the noise-reduced distance-Fourier transform image i ′={r1′,r′2,r′3,...} and sort them; determine the estimated target coordinate position, the primary wall reflection path, the secondary wall reflection path and the round-trip path of the primary and secondary wall reflection combination in the distance information of the static target;

[0024] Determine the secondary wall reflection path r2 and the azimuth angle θ2 by using the estimated target coordinate position and the distance relationship of the primary wall reflection path;

[0025] Determine the length of the round trip path of the combination of the primary and secondary wall reflections according to the primary wall reflection path r1 and the secondary wall reflection path r2;

[0026] If the length of the round-trip path of the combination of the primary and secondary wall reflections can be matched in the distance information of the static target, the estimated target coordinate position is the target position.

[0027] In the above scheme, the distance information r of all static targets in the noise-reduced distance-Fourier transform image is determined. i ′={r1′,r′2,r′3,...} and sort them, specifically: define the accumulator A corresponding to different distance directions i ={a1,a2,..,a M}, traverse the distance peaks corresponding to different chirps, add 1 to the accumulator corresponding to the distance peak, set a suitable threshold s, and filter out those that are greater than the threshold and are within the boundary distance range (r min , r max ) in the accumulator f Ai , the accumulator f Ai Sort in descending order according to the value, obtain the distance information of all static targets within the search range and add the accumulator f Ai Sort by the value in descending order and obtain the distance information r of all static targets within the search range. i ′={r1′,r′2,r′3,...}.

[0028] In the above scheme, the distance information r of all static targets i The largest value r1′ among ′={r1′,r′2,r′3,...} is assumed to be the main reflection path, and the azimuth angle θ1 corresponds to A siThe estimated target coordinate position is determined based on the maximum angle peak value and the distance relationship of the main reflection path; then, the secondary wall reflection path r2 and the azimuth θ2 are determined based on the estimated target coordinate position and the distance relationship of the primary wall reflection path; finally, the length of the round-trip path of the combination of the primary and secondary wall reflections is determined based on the primary wall reflection path r1 and the secondary wall reflection path r2.

[0029] In the above scheme, the azimuth angle θ1 corresponds to A si The maximum angular peak of A is obtained by performing horizontal scanning by rotating the radar system in steps of 2° to obtain amplitude values ​​at different angles. si ={a s1 ,a s2 ,a s3 ,...,a sn}, find the azimuth angle θ1 corresponding to A si The maximum angle peak value of the wall reflection path r1 is used to determine the estimated target coordinate position (x p ,y p ): The second wall reflection path r2 and the azimuth angle θ2 are determined by the estimated target coordinate position and the distance relationship of the first wall reflection path. Among them, L1 is the distance between the antenna and Wall-2, L3 is the distance between the millimeter wave radar antenna and the diffraction angle, (x p ,y p ) is the target coordinate position.

[0030] In the above solution, the length of the combined reflection path is determined according to the main reflection path r1 and the secondary reflection path r2, specifically:

[0031] In the above scheme, if the length of the round-trip path of the combination of the primary and secondary wall reflections can match the distance information of the static target, then the estimated target coordinate position is the target position, specifically: verify the distance information r of the static target i r ′={r1′,r′2,r′3,...} i ′, whether r2 and r c , if they exist, r1 and the corresponding angle r1 are determined, and the target coordinate position is successfully estimated.

[0032] In the above scheme, the breathing feature extraction of the target position to obtain the breathing frequency is specifically as follows:

[0033] Get the position of the life form in the non-line-of-sight area and obtain the length r of the three reflection paths i , including r1, r2 and rc ;

[0034] Set the corresponding distance bin and randomly select a chirp from it;

[0035] Extract the time-varying phase of the selected chirp corresponding to the continuously changing slow time and convert it into micro-motion information of human chest fluctuation;

[0036] The phase value is passed through a bandpass filter with a passband between 0.12 Hz and 0.6 Hz. After completing FFT, peak finding, and peak interval calculation, the clean respiratory signal is separated and the respiratory frequency is obtained.

[0037] Compared with the prior art, the present invention does not require complex high-resolution imaging detection, but combines simple NLOS target detection with NLOS vital sign (human breathing) detection to achieve detection of life forms in non-line-of-sight areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to disclose a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0039] FIG1( a ) is a diagram of a radar non-line-of-sight observation scene in a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency-modulated continuous wave radar according to an embodiment of the present invention.

[0040] FIG1( b ) is a detection echo of a non-line-of-sight area feature of an L-shaped corner in a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency-modulated continuous wave radar according to an embodiment of the present invention.

[0041] Figure 2 A typical propagation path of electromagnetic waves in an L-shaped corner area in a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency-modulated continuous wave radar is provided for an embodiment of the present invention.

[0042] Figure 3 An embodiment of the present invention provides an equivalent virtual detectable area of ​​a first reflection and a second reflection in a method for detecting a human body in a non-line-of-sight area by using a millimeter-wave frequency-modulated continuous wave radar.

[0043] Figure 4 The present invention provides a non-line-of-sight human body positioning algorithm processing flow in a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency-modulated continuous wave radar.

[0044] Figure 5 The present invention provides a non-line-of-sight radar breathing detection algorithm processing flow in a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency-modulated continuous wave radar.

[0045] Figure 6 A distance-Fourier transform diagram of a target frame in a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency-modulated continuous wave radar is provided for an embodiment of the present invention.

[0046] Figure 7 The present invention provides a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency modulated continuous wave radar, and extracts breathing waveforms corresponding to different detection paths: (a) path #1, (b) path #2, and (c) path #3.

[0047] In the figure: (100) distance FFT; (101) background subtraction; (102) random selection of data frames; (103) traversal of distance bins, corresponding to A i Add 1; (104)r min <Distance(f Ai >s) <r max ; (105) Arrange in descending order f Ai , calculate r i ′, assuming r1 = r1′; (106) Rotate the radar to find θ1; (107) Calculate (x p ,y p ),r2,θ2,r c ;(108) Determine r2,r c , whether the conditions are met; (109) judge r i ′ is empty; (110) remove r1′; (111) non-line-of-sight multipath target search and position estimation algorithm; (112) range bin setting and Chirp selection; (113) phase extraction and removal of baseline drift; (114) bandpass filtering, spectrum analysis and respiratory rate extraction. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] The same or similar numbers in the drawings of this embodiment correspond to the same or similar parts; in the description of the present invention, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limitations on this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0050] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, article or device. In the absence of more restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, article or device including the element.

[0051] An embodiment of the present invention provides a method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency-modulated continuous wave radar, the method comprising:

[0052] Step 101: Scan the non-line-of-sight environment by frequency modulated continuous wave to obtain main features of the non-line-of-sight environment;

[0053] Specifically, a local coordinate system is also established to facilitate the subsequent determination of the target coordinate position.

[0054] Step 102: establishing a multipath model of electromagnetic wave propagation for radar detection in a non-line-of-sight environment according to the main features of the non-line-of-sight environment, and determining a main detection path; the main detection path includes a primary wall reflection path, a secondary wall reflection path, and a round-trip path of a combination of primary and secondary wall reflections;

[0055] Step 103: performing radar beam scanning on the non-line-of-sight area according to the main detection path to obtain radar data of different frames;

[0056] Step 104: pre-processing the radar data of the different frames and locating the target determined to exist to obtain the target position;

[0057] Specifically, discrete Fourier transform is performed on radar data of different frames in the distance direction to obtain distance-Fourier transform graphs of different frames;

[0058] Performing environmental noise suppression processing on the distance-Fourier transform images of different frames to obtain noise-reduced distance-Fourier transform images of different frames;

[0059] Randomly select a frame of denoised distance-Fourier transform image;

[0060] Determine the distance information r of all static targets in the noise-reduced distance-Fourier transform image i ′={r1′,r′2,r′3,...} and sort them; determine the estimated target coordinate position, the primary wall reflection path, the secondary wall reflection path and the round-trip path of the primary and secondary wall reflection combination in the distance information of the static target;

[0061] Determine the secondary wall reflection path r2 and the azimuth angle θ2 by using the estimated target coordinate position and the distance relationship of the primary wall reflection path;

[0062] Determine the length of the round trip path of the combination of the primary and secondary wall reflections according to the primary wall reflection path r1 and the secondary wall reflection path r2;

[0063] If the length of the round-trip path of the combination of the primary and secondary wall reflections can be matched in the distance information of the static target, the estimated target coordinate position is the target position.

[0064] Determining the distance information r of all static targets in the noise-reduced distance-Fourier transform image i ′={r1′,r′2,r′3,...} and sort them, specifically: define the accumulator A corresponding to different distance directions i ={a1,a2,..,a M}, traverse the distance peaks corresponding to different chirps, add 1 to the accumulator corresponding to the distance peak, set a suitable threshold s, and filter out those that are greater than the threshold and are within the boundary distance range (r min ,r max ) in the accumulator f Ai , the accumulator f Ai Sort in descending order according to the value, obtain the distance information of all static targets within the search range and add the accumulator f Ai Sort by the value in descending order and obtain the distance information r of all static targets within the search range. i ′={r1′,r′2,r′3,...}.

[0065] The distance information r of all static targets i The largest value r1′ among ′={r1′,r′2,r′3,...} is assumed to be the main reflection path, and the azimuth angle θ1 corresponds to A siThe estimated target coordinate position is determined based on the maximum angle peak value and the distance relationship of the main reflection path; then, the secondary wall reflection path r2 and the azimuth θ2 are determined based on the estimated target coordinate position and the distance relationship of the primary wall reflection path; finally, the length of the round-trip path of the combination of the primary and secondary wall reflections is determined based on the primary wall reflection path r1 and the secondary wall reflection path r2.

[0066] The azimuth angle θ1 corresponds to A si The maximum angular peak of is obtained by performing horizontal scanning by rotating the radar system in steps of 2° to obtain the amplitude values ​​at different angles, A si ={a s1 ,a s2 ,a s3 ,...,a sn}, find the azimuth angle θ1 corresponding to A si The maximum angle peak value of the wall reflection path r1 is used to determine the estimated target coordinate position (x p ,y p ): The second wall reflection path r2 and the azimuth angle θ2 are determined by the estimated target coordinate position and the distance relationship of the first wall reflection path. Among them, L1 is the distance between the antenna and Wall-2, L3 is the distance between the millimeter wave radar antenna and the diffraction angle, (x p ,y p ) is the target coordinate position.

[0067] The length of the combined reflection path is determined according to the main reflection path r1 and the secondary reflection path r2, specifically:

[0068] If the length of the round-trip path of the combination of the primary and secondary wall reflections can match the distance information of the static target, then the estimated target coordinate position is the target position, specifically: verify the distance information r of the static target i r ′={r1′,r′2,r′3,...} i ′, whether r2 and r c , if they exist, r1 and the corresponding angle r1 are determined, and the target coordinate position is successfully estimated.

[0069] Step 105: extracting respiratory features of the target position to obtain a respiratory frequency;

[0070] Specifically, the breathing feature extraction is performed on the target position to obtain the breathing frequency, specifically as follows:

[0071] Get the position of the life form in the non-line-of-sight area and obtain the length r of the three reflection pathsi , including r1, r2 and r c ;

[0072] Set the corresponding distance bin and randomly select a chirp from it;

[0073] Extract the time-varying phase of the selected chirp corresponding to the continuously changing slow time and convert it into micro-motion information of human chest fluctuation;

[0074] The phase value is passed through a bandpass filter with a passband between 0.12 Hz and 0.6 Hz. After completing FFT, peak finding, and peak interval calculation, the clean respiratory signal is separated and the respiratory frequency is obtained.

[0075] Step 106: If the breathing frequency is determined to be that of a human body, then the target location is a living body in a non-line-of-sight area.

[0076] Example

[0077] Considering an L-shaped corner scenario (for example, in a corridor with a corner), the millimeter-wave FMCW radar is placed near the wall on the side and needs to detect stationary life targets in the NLOS area after the corner. As shown in Figure 1(a), a Cartesian coordinate system is defined, where the radar is located at the origin O and the target is at coordinate P(x p , y p ).

[0078] The method of the present invention does not rely on any prior information about the geometric structure of the detection environment. Based on the deployed radar, characteristic parameters of the non-line-of-sight detection scene can be obtained on-site, including the distance between the millimeter-wave radar antenna and the wall corner C, and the distance between the radar antenna and wall-2 and wall-3. That is, in Figure 1(a), the characteristics of the L-shaped corner geometry are OC, OB and OA. These three distances can be measured by rotating the radar antenna at an appropriate angle on-site. The specific operation is: when the radar beam points to an azimuth angle of θ=0°, the distance from the antenna to wall-2 measured by the radar is L1. When the azimuth angle is 90°, the distance from the antenna to wall-3 measured by the radar is L2. Finally, select a suitable azimuth angle to measure the distance L3 from the antenna to the wall corner. The real-time echo intensity of the rotating radar beam is shown in Figure 1(b).

[0079] Based on the obtained non-line-of-sight detection area composition characteristics and geometric characteristic parameters, the electromagnetic wave propagation model entering the non-line-of-sight area can be simplified to the following model assumptions:

[0080] 1) The wall is flat enough so that the electromagnetic waves are reflected by the mirror surface.

[0081] 2) Electromagnetic waves cannot penetrate the corner areas of corridors.

[0082] 3) The corridor contains no other major scatterers.

[0083] 4) Reflection mainly occurs on the surface of the human body.

[0084] According to the theory of electromagnetic field reflection, diffraction and scattering, the electromagnetic waves formed by diffraction from the corners of the wall enter the NLOS area and cover the entire corridor area with a smaller electromagnetic wave power, making it difficult to effectively detect the weak breathing of living organisms at a certain distance from the corners in the corridor.

[0085] For electromagnetic waves that enter the NLOS area through wall reflection, reflection will also cause energy attenuation. Electromagnetic waves that are reflected by multiple walls will produce significant power attenuation. Therefore, radar signals that propagate through diffraction, three or more reflection paths are too weak when projected onto the target, and their target scattered signals are difficult to be detected by the system. Therefore, in practice, only three types of propagation paths that go through a single reflection path (path #1), a double reflection path (path #2), and a combination of a single reflection path and a double reflection path (path #3) can be considered and included in the NLOS detection model, such as Figure 2 As shown. Includes:

[0086] #1 Main reflection path: has the path O→P21→P→P21→O. The electromagnetic wave is transmitted to P through the main reflection path and returns to the radar along the same main reflection path.

[0087] #2nd reflection path: has the path O→P22→P11→P→P11→P22→O. The electromagnetic wave is transmitted to P through the secondary reflection path and returns to the radar through the same secondary reflection path.

[0088] #3 Primary and secondary reflection combined path: with path O→P21→P→P11→P22→O or using path O→P22→P11→P→P21→O. The signal detects the target P through a primary reflection path and a secondary reflection path.

[0089] Since there are only three types of electromagnetic wave paths (paths #1, #2, and #3) in this typical NLOS area target detection, a "path dictionary" can be established for target detection at different locations in the NLOS area, including all possible "target radar path" models. The location of the target in the NLOS area can be achieved by simply comparing the radar echo intensity characteristics and looking up the "path dictionary".

[0090] For NLOS area detection after an L-shaped corner, both the first reflection round-trip path and the second reflection round-trip path have minimum-maximum path lengths in the "path dictionary". For the first reflection path, Figure 3 As shown, the minimum length r 1min and the maximum length r1max This can be calculated as follows:

[0091]

[0092]

[0093] obey:

[0094] x′ p <2L1-L3cosβ

[0095] x′ p tanβ-y′ p >0

[0096] x′ p tanβ+y′ p -2L1tanβ>0.

[0097] For the secondary reflection path, Figure 3 As shown, the minimum length r 2min and the maximum length r 2max This can be calculated as follows:

[0098]

[0099]

[0100] obey:

[0101] x″ p <L3cosβ

[0102] x″ p tanβ-y″ p +2(L1tanβ-L2)>0

[0103] x″ p tanβ+y″ p -2L2>0.

[0104] Therefore, you can limit the search path to the following:

[0105] r min =min{r 1min ,r 2min} <r<max{r 1max ,r 2max} = r max

[0106] Once the radar wave path (i.e., path #1, path #2, or path #3) and the path direction and length (i.e., azimuth and path length) are determined by table lookup, the target position in the non-line-of-sight area can be determined.

[0107] In actual detection, it is necessary to scan the radar beam in the non-line-of-sight area to find the target. When scanning the radar beam for target detection, if the radar beam entering the NLOS area scans the target, the echo signal will be enhanced. According to the value of the scanning azimuth angle and the measured path length from the radar to the target position, the target position can be determined by looking up the path dictionary.

[0108] Once the position of the target in the NLOS area is obtained, the echo signals with different paths from the target can be analyzed. If the breathing signal can be extracted from these radar echo signals, the target can be determined to be a living being. By combining the accurate position of the living being in the non-line-of-sight area with the extracted breathing features by searching the path dictionary, the positioning and determination of the living being in the NLOS area can be achieved. Figure 4 and 5 describe the search algorithm for target positioning in NLOS and the flowchart for life body detection in NLOS, respectively. Figure 6 is the distance-Fourier transform map of the target frame, Figure 7 To correspond to the respiratory waveforms extracted by different detection paths, the respiratory frequencies and .

[0109] The specific process algorithm can be implemented as follows:

[0110] Step 1 (100), performing discrete Fourier transform in the distance direction on radar data of different frames to obtain distance-Fourier transform graphs of different frames.

[0111] Step 2 (101), using a background subtraction algorithm based on exponential averaging to suppress environmental noise.

[0112] Step 3 (102), randomly select a frame of radar data for further analysis and processing.

[0113] Step 4 (103), define accumulators A corresponding to different distance directions (also in fast time) i ={a1,a2,..,a M}. Traverse the distance peaks corresponding to different chirps and add 1 to the accumulator corresponding to the distance peak.

[0114] Step 5 (104), set a suitable threshold s, filter out the values ​​greater than the threshold and within the boundary distance range (r min ,r max ) in the accumulator f Ai .

[0115] Step 6 (105), set the accumulator f Ai By sorting in descending order according to the value, the distance information of all static targets within the search range can be obtained and the accumulator f AiBy sorting in descending order according to the value, the distance information r of all static targets within the search range can be obtained. i ′={r1′,r′2,r′3,...}. Since the reflection of the main path is relatively strong, it is assumed here that r1=r1′. Since the reflection of the main path is relatively strong, it is assumed here that r1=r1′.

[0116] Step 7 (106), performing horizontal scanning by rotating the radar system in steps of 2° to obtain amplitude values ​​at different angles, A si ={a s1 ,a s2 ,a s3 ,...,a sn}, find the azimuth angle θ1 corresponding to A si The maximum angle peak.

[0117] Step 8 (107), estimate the target coordinate position (x p ,y p ):

[0118]

[0119] The secondary reflection path r2 and azimuth angle θ2 are derived based on the target coordinate position and the distance relationship of the primary reflection path:

[0120]

[0121] Calculate the length of the combined reflection path:

[0122]

[0123] Step 9 (108), verify r i ′, whether r2 and r c If they exist, r1 and the corresponding angle r1 are determined, and the target coordinate position is successfully estimated. If not, continue to step 10 (109).

[0124] Step 10 (109), determine r i ' is empty, if not, please i Delete r1′ from ′ and continue searching. Otherwise, reselect a frame of data.

[0125] Once the human target can be detected and located, breathing detection can then be performed. FMCW radar breathing detection consists of the following 4 steps:

[0126] Step 11 (110): Obtain the position of the person in the non-line-of-sight area and obtain the lengths r of the three reflection paths i , including r1, r2 and rc .

[0127] Step 12 (111): Set the distance bin corresponding to , and randomly select a Chirp from it.

[0128] Step 13 (112): Extract the time-varying phase of the selected chirp corresponding to the continuously varying slow time and convert it into micro-motion information of human chest fluctuation. The method of eliminating baseline drift is used to enhance the respiratory signal and eliminate any phase drift.

[0129] Step 14 (113): The phase value passes through a bandpass filter with a passband between 0.12 Hz and 0.6 Hz, which can filter out clutter components and generate a continuous respiratory signal stream. After completing FFT, peak finding and peak interval calculation, the clean respiratory signal is separated and the respiratory frequency is obtained.

[0130] In summary, the present invention does not require obtaining the geometric parameters of the scene in advance, and can accurately obtain the position information of the target object in the non-line-of-sight area from the radar data measured on site. In order to detect people hidden in the NLOS area, the present invention combines NLOS target detection with NLOS vital sign detection, and uses the detected breathing of the living body as the most direct criterion for distinguishing between stationary living bodies and ordinary objects in the NLOS area.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency modulated continuous wave radar, characterized in that: The method is: Scan the non-line-of-sight environment through frequency-modulated continuous waves to obtain the main characteristics of the non-line-of-sight environment; According to the main characteristics of the non-line-of-sight environment, an electromagnetic wave propagation multipath model for radar detection in a non-line-of-sight environment is established, and a main detection path is determined; the main detection path includes a primary wall reflection path, a secondary wall reflection path, and a round-trip path of a combination of primary and secondary wall reflections; Performing radar beam scanning on the non-line-of-sight area according to the main detection path to obtain radar data of different frames; Preprocessing the radar data of the different frames and locating the target determined to exist to obtain the target position; Extracting respiratory features of the target position to obtain a respiratory frequency; If the breathing frequency is determined to be that of a human body, then the target is a living being in a non-line-of-sight area; The preprocessing of the radar data of the different frames and locating the target determined to exist to obtain the target position is specifically as follows: Perform discrete Fourier transform on radar data of different frames in the distance direction to obtain distance-Fourier transform diagrams of different frames; Performing environmental noise suppression processing on the distance-Fourier transform images of different frames to obtain noise-reduced distance-Fourier transform images of different frames; Randomly select a frame of denoised distance-Fourier transform image; Determine the distance information r of all static targets in the noise-reduced distance-Fourier transform image i '={r1', r2', r3', ...} and sort them; determine the estimated target coordinate position, the first wall reflection path, the second wall reflection path and the round-trip path of the first and second wall reflection combination in the distance information of the static target; Determine the secondary wall reflection path r2 and the azimuth angle θ2 by using the estimated target coordinate position and the distance relationship of the primary wall reflection path; Determine the length of the round trip path of the combination of the primary and secondary wall reflections according to the primary wall reflection path r1 and the secondary wall reflection path r2; If the length of the round-trip path of the combination of the primary and secondary wall reflections can be matched in the distance information of the static target, the estimated target coordinate position is the target position.

2. The method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency modulated continuous wave radar according to claim 1, characterized in that: Determining the distance information r of all static targets in the noise-reduced distance-Fourier transform image i ′={r1′,r2′,r3′,...} and sort them, specifically: define the accumulator A corresponding to different distance directions i ={a1,a2,..,a M }, traverse the distance peaks corresponding to different chirps, add 1 to the accumulator corresponding to the distance peak, set a suitable threshold s, and filter out those that are greater than the threshold and are within the boundary distance range (r min ,r max ) in the accumulator f Ai , the accumulator f Ai Sort in descending order according to the value, obtain the distance information of all static targets within the search range and add the accumulator f Ai Sort in descending order according to the value, and obtain the distance information r of all static targets within the search range. i ′={r1′,r2′,r3′,...}.

3. The method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency modulated continuous wave radar according to claim 2, characterized in that: The distance information r of all static targets i The largest value r1′ among ′={r1′,r2′,r3′,...} is assumed to be the main reflection path, and the azimuth angle θ1 corresponds to A si The estimated target coordinate position is determined based on the maximum angle peak value and the distance relationship of the main reflection path; then, the secondary wall reflection path r2 and the azimuth θ2 are determined based on the estimated target coordinate position and the distance relationship of the primary wall reflection path; finally, the length of the round-trip path of the combination of the primary and secondary wall reflections is determined based on the primary wall reflection path r1 and the secondary wall reflection path r2.

4. The method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency modulated continuous wave radar according to claim 3, characterized in that: The azimuth angle θ1 corresponds to A si The maximum angular peak of is obtained by performing horizontal scanning by rotating the radar system in steps of 2° to obtain the amplitude values ​​at different angles, A si ={a s1 ,a s2 ,a s3 ,...,a sn }, find the azimuth angle θ1 corresponding to A si The maximum angle peak value of the wall reflection path r1 is used to determine the estimated target coordinate position (x p ,y p ): The second wall reflection path r2 and the azimuth angle θ2 are determined by the estimated target coordinate position and the distance relationship of the first wall reflection path. Among them, L1 is the distance between the antenna and Wall-2, L3 is the distance between the millimeter wave radar antenna and the corner of the wall, (x p ,y p ) is the target coordinate position.

5. The method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency modulated continuous wave radar according to claim 4, characterized in that: The length of the round-trip path of the combination of the primary and secondary wall reflections is determined according to the primary wall reflection path r1 and the secondary wall reflection path r2, specifically:

6. The method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency modulated continuous wave radar according to claim 5, characterized in that: If the length of the round-trip path of the combination of the primary and secondary wall reflections can match the distance information of the static target, then the estimated target coordinate position is the target position, specifically: verify the distance information r of the static target i r ′={r1′,r2′,r3′,...} i ′, whether r2 and r c , if they exist, r1 and the corresponding angle θ1 are determined, and the target coordinate position is successfully estimated.

7. The method for detecting a human body in a non-line-of-sight area using a millimeter-wave frequency modulated continuous wave radar according to claim 6, characterized in that: The respiratory feature extraction of the target position to obtain the respiratory frequency is specifically as follows: Get the position of the life form in the non-line-of-sight area and obtain the length r of the three reflection paths i , including r1, r2 and r c ; Set the corresponding distance bin and randomly select a chirp from it; Extract the time-varying phase of the selected chirp corresponding to the continuously changing slow time and convert it into micro-motion information of human chest fluctuation; The phase value is passed through a bandpass filter with a passband between 0.12 Hz and 0.6 Hz. After completing FFT, peak finding, and peak interval calculation, the clean respiratory signal is separated and the respiratory frequency is obtained.

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