Radar indoor positioning method and device based on CFAR and clustering algorithm, and medium

Abstract

The invention discloses a radar indoor positioning method and device based on CFAR and a clustering algorithm, and a medium, and the method comprises the steps: collecting radar data, and carrying out the alternate storage of the collected detection data through a plurality of memories; dividing the data in the memory into a plurality of data fragments according to each frame, performing correlation operation according to the data fragments, and identifying potential target point cloud distribution P by using a CFAR algorithm of data variance; clustering the potential target point cloud distribution P by using a density-based mean value clustering algorithm, and determining the number N and the center position R of each cluster in a clustering target set; and according to the shoulder breadth and the chest size of the target individual, and the number N and the central position R of each cluster in the clustering target set, adopting an exponential function rule to adaptively identify the distribution of the real target individual. The invention provides a more accurate positioning scheme, target distribution can be accurately identified in a non-contact and complex indoor environment, and the method can be widely applied to the technical field of indoor monitoring.

Description

Technical Field

[0001] The present invention relates to the field of indoor monitoring technology, and in particular to a radar indoor positioning method, equipment and medium based on CFAR and clustering algorithm. Background Art

[0002] With the aging of the global population and the growing demand for smart homes and smart healthcare, radar technology has shown great application potential in the field of indoor positioning due to its advantages of non-contact, all-weather and high-precision monitoring. Radar indoor positioning technology can not only effectively cope with the accuracy and stability challenges faced by traditional positioning methods in complex indoor environments, but also provide strong support for the health management and care services for the elderly.

[0003] However, radar technology also faces some challenges in practical applications. First, the complexity of the indoor environment makes the propagation of radar signals affected by many factors, such as reflection and scattering from obstacles such as walls and furniture, as well as multipath effects. These factors may cause radar signal distortion, thereby increasing positioning errors. Secondly, there may be multiple moving targets in the indoor environment, and the uncertainty of their movement also brings additional challenges to radar indoor positioning. Therefore, how to accurately identify and locate these moving targets while avoiding false alarms and missed alarms has become a key issue that needs to be solved in radar indoor positioning technology.

[0004] Despite the challenges, radar indoor positioning also contains huge opportunities. With the rapid development of technologies such as the Internet of Things, big data and artificial intelligence, radar indoor positioning technology can be combined with other technologies to achieve smarter and more accurate positioning services. This development direction not only improves the reliability of positioning, but also provides greater flexibility for various application scenarios.

[0005] Therefore, improving the efficiency and high-precision positioning of radar in dynamic and complex environments has become the focus of current research. Summary of the invention

[0006] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the object of the present invention is to provide a radar indoor positioning method, device and medium based on CFAR and clustering algorithm.

[0007] The first technical solution adopted by the present invention is:

[0008] A radar indoor positioning method based on CFAR and clustering algorithm comprises the following steps:

[0009] Collect radar data, and use multiple memories to alternately store the collected detection data;

[0010] Divide the data in the memory into multiple data segments for each frame, perform relevant operations based on the data segments, and use the CFAR algorithm of data variance to identify the potential target point cloud distribution P;

[0011] Use the density-based mean clustering algorithm to cluster the potential target point cloud distribution P, and determine the number N and the central position R of each cluster in the clustering target set;

[0012] According to the shoulder width and chest size of the target individual, the number N and the central position R of each cluster in the clustering target set, adaptively identify the distribution of the real target individual using the exponential function rule.

[0013] Further, the multiple memories include memory A and memory B;

[0014] Store the collected detection data into memory A and memory B alternately without interruption:

[0015] Store the detection data T1_signal with a collection duration of T into memory A; when memory A is full, store the detection data T2_signal with a collection duration of T into memory B, and clear memory A; when memory B is full, return to store the detection data T1_signal with a collection duration of T into memory A, and clear memory B.

[0016] Further, the step of dividing the data in the memory into multiple data segments for each frame, performing relevant operations based on the data segments, and using the CFAR algorithm of data variance to identify the potential target point cloud distribution P includes:

[0017] Divide the detection data in the memory into multiple data segments G1, G2, …, Gm for each frame, perform relevant operation preprocessing to obtain data J1, J2, …, Jm, and perform shift register into memory C;

[0018] According to the data in memory C, calculate the variance of each sampling point of each frame under M chirps in parallel, perform CFAR algorithm calculation based on the variance data, and draw the variance threshold curve of N sampling points for each frame;

[0019] When the variance of a certain sampling point in the i-th frame exceeds the variance threshold curve of the corresponding sampling point of this frame, it is determined that this sampling point is a potential target; when the variance of a certain sampling point in the i-th frame is lower than the variance threshold curve of the corresponding sampling point of this frame, it is determined that this sampling point is not a potential target.

[0020] Further, the relevant operation preprocessing includes: vector mean cancellation processing, time-domain correlation based on convolution, or frequency-domain correlation after Fourier transform.

[0021] Further, clustering the potential target point cloud distribution P using a density-based mean clustering algorithm to determine the number N and the central position R of each cluster in the clustering target set, including:

[0022] Using a density-based clustering algorithm to cluster the obtained target point cloud distribution P to obtain the distribution of the clustering target set and the number N of each cluster in the clustering target set;

[0023] Adopting an averaging algorithm to calculate the central position R of each clustering target set.

[0024] Further, the density-based clustering algorithm includes: DBSCAN algorithm, OPTICS algorithm, DENCLUE algorithm; the averaging algorithm includes: arithmetic mean algorithm, weighted mean algorithm, geometric mean algorithm, harmonic mean algorithm, simple moving average algorithm, weighted moving average algorithm, exponential weighted moving average algorithm.

[0025] Further, according to the shoulder width and chest size of the target individual, the number N and the central position R of each cluster in the clustering target set, adaptively identifying the distribution of the real target individual using an exponential function rule, including:

[0026] According to the shoulder width W and chest size D of the target individual, partitioning each cluster in the target set; when the lateral width of cluster c in the target set exceeds a preset multiple of the shoulder width W of the target individual, or the longitudinal width of cluster c exceeds the chest size D, the cluster c is divided into two sub-target clusters according to the central position R;

[0027] For each cluster in the target set, according to the radial distance abs(R) between the target set center and the radar, and the maximum number max(N) of target points in the cluster, calculate the expected number of target points Tp of each cluster as Tp = αmax(N)exp(-abs(R) / β), where α adjusts the maximum number of detectable potential human target points in all clusterings, and β makes the threshold distance-sensitive;

[0028] When the number of clusters in the clustering target set is greater than Tp, it is determined that the cluster is a target individual.

[0029] Further, the radar indoor positioning method further includes:

[0030] After successfully positioning the target, performing vital sign monitoring and posture recognition during idle time to achieve comprehensive monitoring and analysis of the target individual;

[0031] Among them, within the duration T = T1 + T2, the first half of the time T1 is used for target positioning, and the second half of the time T2 is used for vital sign monitoring and posture recognition; the second half of the time T2 is only performed after successfully positioning the target, and if the target is not positioned, there is no need to perform vital sign monitoring and posture recognition.

[0032] The second technical solution adopted by the present invention is as follows:

[0033] An electronic device, the electronic device includes a processor and a memory, and at least one instruction, at least one program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the above-mentioned radar indoor positioning method based on CFAR and clustering algorithms.

[0034] The third technical solution adopted by the present invention is as follows:

[0035] A computer-readable storage medium, at least one instruction, at least one program, a code set or an instruction set is stored in the storage medium, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the above-mentioned radar indoor positioning method based on CFAR and clustering algorithms.

[0036] The fourth technical solution adopted by the present invention is as follows:

[0037] A computer program product or a computer program, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above method.

[0038] The beneficial effects of the present invention are as follows: The present invention adopts the alternating storage and shift register technology of multiple memories in indoor positioning processing, thereby significantly improving the processing speed. In addition, the present invention introduces the CFAR algorithm based on variance and the mean clustering algorithm based on density, making target recognition more efficient. In order to avoid the appearance of false targets, the present invention also additionally adds an exponential function rule to achieve adaptive target recognition. These improvements effectively meet the requirements of the radar positioning method in terms of high efficiency and high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following introduces the accompanying drawings of the related technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings introduced below are only for conveniently and clearly expressing some embodiments of the technical solutions in the present invention, and those skilled in the art can also obtain other accompanying drawings based on these drawings without creative efforts.

[0040] Figure 1It is a flowchart of a radar indoor positioning method based on CFAR and clustering algorithms in an embodiment of the present invention;

[0041] Figure 2 It is the transmission and reception signal model corresponding to each chirp in the radar in an embodiment of the present invention;

[0042] Figure 3 It is a flowchart of the non-stop alternating storage of memory A and memory B in an embodiment of the present invention;

[0043] Figure 4 It is a flowchart of obtaining the variance threshold curve of each frame in an embodiment of the present invention;

[0044] Figure 5 It is a flowchart of obtaining each cluster after clustering from the point cloud distribution in an embodiment of the present invention;

[0045] Figure 6 It is a flowchart of adaptively identifying real target individuals in an embodiment of the present invention;

[0046] Figure 7 It is a schematic structural diagram of a radar indoor positioning device based on CFAR and clustering algorithms in an embodiment of the present invention;

[0047] Figure 8 It is an application schematic diagram of a radar indoor positioning device based on CFAR and clustering algorithms in an embodiment of the present invention. Detailed implementation manners

[0048] The embodiments of the present application are described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary only for explaining the present application and should not be construed as limiting the present application. For the step numbers in the following embodiments, they are only set for the convenience of description and explanation, and no limitation is imposed on the order between the steps. The execution order of each step in the embodiments can be adjusted adaptively according to the understanding of those skilled in the art.

[0049] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present application. The singular forms "a", "the" and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. In addition, unless otherwise clearly defined, words such as "set", "installed", "connected" should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0050] In the description of the present application, it should be understood that for the orientation descriptions, such as the orientations or positional relationships indicated by up, down, front, back, left, right, etc., they are based on the orientations or positional relationships shown in the drawings. This is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present application.

[0051] In the description of the present application, the meaning of "a number of" is one or more, the meaning of "a plurality of" is two or more, and understandings such as "greater than", "less than", "exceeding", etc. do not include the corresponding number, while understandings such as "above", "below", "within", etc. include the corresponding number. If there is a description of "first" and "second", it is only for the purpose of distinguishing technical features and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features or the sequence relationship of the indicated technical features.

[0052] In the description of the present application, "and / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0053] Term Explanation:

[0054] CFAR algorithm: Constant False Alarm Rate detection algorithm, which is a target detection algorithm widely used in the fields of radar, communication, and image processing. The core goal is to detect the target signal as accurately as possible under the premise of keeping the false alarm rate (false alarm probability) constant in a clutter environment.

[0055] chirp: In a radar system, it is a signal whose frequency changes linearly with time. A simple linear frequency modulation chirp signal can be expressed as: s(t) = Acos(2πf0t + πSt 2 ), where A is the signal amplitude, f0 is the starting frequency, S is the frequency modulation slope, and t is the time.

[0056] Embodiment 1

[0057] As Figure 1 shown, this embodiment provides a radar indoor positioning method based on CFAR and clustering algorithms, including the following steps:

[0058] S1. Collect radar data and alternately store the collected detection data using multiple memories.

[0059] Based on the radar system, the A memory and the B memory are used for data storage. The detection data T1_signal with a collection duration of T is stored in the A memory. After the A memory is full, the detection data T2_signal with a collection duration of T is collected and stored in the B memory. The A memory and the B memory store data alternately without interruption.

[0060] In some embodiments, step S1 specifically includes the following steps:

[0061] S11. Within the duration T, mix and sample the transmitted signal and the received signal of the radar to obtain the detection data T1_signal, and store it in the A memory.

[0062] Further as an optional implementation manner, as Figure 2 shown in (a) of [], the transmitted signal and the received signal are frequency-modulated continuous signals chirp, the frequency of chirp increases linearly with time, one chirp period is the duration of the signal from the start frequency to the cut-off frequency, there will be idle time between chirps, one frame period contains multiple chirp periods, starting from the first chirp to the cut-off, then followed by the start to the cut-off of the second chirp, and finally the start to the cut-off of the last chirp. One frame period is at least greater than two chirp periods.

[0063] Among them, as Figure 2 shown in (b) of [], the intermediate-frequency signal obtained by mixing is sampled, that is, the multi-antenna multi-frame multi-chirp signal dataset G1, which is a three-dimensional dataset of F*M*N, where F represents the frame, M represents the number of chirps included in each frame, and N represents the number of sampling points included in each chirp.

[0064] S12. When the A memory is full, start collecting new detection data T2_signal and store it in the B memory.

[0065] S13. Achieve non-stop alternate storage between the A memory and the B memory.

[0066] Specifically, as Figure 3As shown, the sampled data set G1 is stored in memory A. When memory A is full, the subsequent data set G1 is determined to be stored in memory B through the input data stream 2-to-1 selection unit. At the same time, through the output data stream 2-to-1 selection unit, the data in memory A is transmitted to the data stream operation and processing module for subsequent processing. When memory B is full and the data in memory A has been processed, again through the input data stream 2-to-1 selection unit, the subsequent data set G1 is determined to be stored in memory A, and at the same time, through the output data stream 2-to-1 selection unit, the data in memory B is transmitted to the data stream operation and processing module for subsequent processing. This processing method realizes uninterrupted alternating storage and data processing.

[0067] S2. Divide the data in the memory into multiple data segments for each frame, perform correlation operations based on the data segments, and use the CFAR algorithm of data variance to identify the potential target point cloud distribution P.

[0068] In some embodiments, for the specific steps of obtaining the variance threshold curve for each frame, please refer to Figure 4 , and step S2 specifically includes the following steps:

[0069] S21. At the start of the calculation, extract each frame of data DATA from memory A or memory B in step S1.

[0070] In this process, take the data in the memory corresponding to the continuous duration T = T1 + T2 of the radar acquisition. The first half of the time T1 contains each frame of data DATA, and the second half of the time T2 is used for vital sign monitoring and attitude recognition.

[0071] S22. Divide the data in the memory in step S21 into multiple processing data segments G1, G2,..., G m for each frame, perform correlation operation preprocessing to obtain J1, J2,..., J m , and perform shift register into memory C.

[0072] Further as an optional implementation manner, the correlation operation preprocessing is that the data segments G1, G2,..., G m first perform vector mean cancellation processing, and then perform Fourier transform to obtain J1, J2,..., J m . Among them, the vector mean cancellation processing method for each frame is the same, and the steps are as follows:

[0073] For the processing data segments G1, G2,..., G of M chirps m, each segment contains N sampling points. Let g[m,n] represent the signal of the nth sampling point under the mth chirp, and g[i,n] represent the signal of the nth sampling point under the ith chirp. Then, the data of M chirps at the same sampling point are averaged to obtain the average value. Subtract the average value from the chirp data set in this frame. to obtain a new data set.

[0074] S23. According to the data in the C memory, calculate the variance of each sampling point under M chirps in each frame in parallel.

[0075] Among them, the calculation of the variance is based on J1, J2, …, J m and is carried out.

[0076] S24. Perform CFAR calculation based on the variance data and draw the variance threshold curve of N sampling points in each frame.

[0077] The CFAR algorithm includes a cell under test (CUT), guard cells around the CUT, and training cells near the guard cells, and there are threshold factors and noise power estimates in each frame. In CFAR, the noise power estimate is expressed as The threshold factor is expressed as After that, the calculation method for drawing the threshold curve of each frame is T = αP n , where K is the number of cells used for training, f a is the desired false alarm rate, and D[i] is the variance of the ith frame calculated in step S23.

[0078] When the variance of a certain sampling point in the ith frame exceeds the variance threshold curve of the corresponding sampling point in this frame, then identify this sampling point as a potential target; when the variance of a certain sampling point in the ith frame is lower than the variance threshold curve of the corresponding sampling point in this frame, then this sampling point is not identified as a potential target.

[0079] S3. Use the density-based mean clustering algorithm to cluster the potential target point cloud distribution P, and determine the number N and the center position R of each cluster in the clustering target set.

[0080] Specifically, referring to Figure 5 , the specific steps for obtaining each cluster after clustering from the point cloud distribution are as follows:

[0081] S31. Determine the point cloud distribution P of potential target individuals in the plane, and use the density-based clustering algorithm for clustering to obtain i clusters Td1, Td2, …, Td i of the target set distribution, and the corresponding quantities are N1, N2, …, N i .

[0082] S32. Calculate the central positions R1, R2, …, Ri of the i clusters using the average algorithm i .

[0083] S4. According to the shoulder width and chest size of the target individual, the number N and central position R of each cluster in the clustering target set, adaptively identify the distribution of the real target individual using the exponential function rule

[0084] Specifically, the specific steps for adaptively identifying the real target individual are as Figure 6 shown

[0085] Take the i clusters Td1, Td2, …, Tdi of the target set distribution i , with corresponding quantities N1, N2, …, Ni i and central positions R1, R2, …, Ri i .

[0086] According to the shoulder width and chest size of the target individual, divide each cluster in the target set. When the horizontal width of the k-th cluster exceeds multiple times the shoulder width W of the target individual, or its vertical width exceeds the chest size D, divide this cluster into two sub-target clusters according to the central position R k , and store the corresponding data sets in different regions of the memory E respectively; otherwise, directly store the data of this cluster in a certain region of the memory E. The above steps are repeated until all the original clusters are judged. In this memory E, the number of clusters becomes j

[0087] After that, for the v-th cluster in the target set, according to the radial distance abs(R v ) between the target set center and the radar, and the maximum number of target points max({N1, N2, …, Ni j}) in all clusters of this target set, calculate the expected number of target points Tp for each cluster as Tp = αmax({N1, N2, …, Ni j})exp(-abs(R v ) / β), where α adjusts the maximum number of detectable potential human target points in all clusterings, and β makes the threshold distance-sensitive. When the number of the v-th cluster is greater than Tp, identify this cluster as a target individual, and subsequent vital sign monitoring and pose recognition can be further performed on this cluster. Repeat until all j clusters are judged, update the storage space, and enter another A / B memory for data processing

[0088] Compared with the prior art, the method of the present invention adopts the alternating storage and shift register technology of multiple memories in indoor positioning processing, thereby significantly improving the processing speed. In addition, the present invention introduces the CFAR algorithm based on variance and the mean clustering algorithm based on density, making target recognition more efficient. To avoid the appearance of false targets, the present invention also additionally adds an exponential function rule to achieve adaptive target recognition. These improvements effectively meet the requirements of radar positioning methods in terms of high efficiency and high precision.

[0089] Embodiment 2

[0090] As Figure 7 shown, this embodiment also provides a radar indoor positioning device based on CFAR and clustering algorithms, including:

[0091] A memory module, used to update and store data sets, save the intermediate obtained potential target distribution data, and finally the data for display;

[0092] An extraction module, used to extract data fragments from the memory;

[0093] A preliminary decision module, used to compare the variance of each frame with the variance threshold curve to determine whether there is a potential target distribution; when the value of the variance of the i-th frame is above the variance threshold curve, it is identified as a potential target, and the coordinate distance between the potential target and the radar is calculated; when the value of the variance of the i-th frame is below the variance threshold curve, it is not identified as a potential target;

[0094] A classification module, used to cluster and classify the identified potential targets, and separate them into different clusters and data sets;

[0095] A final decision module, used to compare the number of each cluster in the clustered target set with the expected number of target points of each cluster to determine whether there are real target individuals; when the number of the v-th cluster is greater than its expected number of target points, identify this cluster as a target individual; otherwise, this cluster is not regarded as a target individual.

[0096] See Figure 8 , a radar indoor positioning device based on CFAR and clustering algorithms in this embodiment is respectively connected to a radar antenna array, and the distribution and recognition of the target can be displayed through a display device.

[0097] Since this device is a radar indoor positioning device based on CFAR and clustering algorithms in an embodiment of the present invention, and the principle of solving problems by this device is similar to that of this method, the implementation of this device can refer to the implementation process of the above method embodiment, and the repeated parts will not be described again.

[0098] Embodiment 3

[0099] An embodiment of the present invention further provides an electronic device, which includes a processor and a memory. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement as Figure 1 a radar indoor positioning method based on CFAR and clustering algorithms as shown.

[0100] It can be understood that the memory may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory includes a non-transitory computer-readable storage medium. The memory can be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the above method embodiments, etc.; the data storage area can store data created according to the use of the server, etc.

[0101] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts within the entire server, and by running or executing instructions, programs, code sets, or instruction sets stored in the memory, and by calling data stored in the memory, it executes various functions of the server and processes data. Optionally, the processor may be implemented in at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor may integrate a Central Processing Unit (CPU) and a modem, etc. in one or several combinations. Among them, the CPU mainly processes the operating system and application programs, etc.; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor and may be implemented separately by a single chip.

[0102] Since this electronic device is the electronic device corresponding to a radar indoor positioning method based on CFAR and clustering algorithms in an embodiment of the present invention, and the principle of this electronic device to solve problems is similar to that of this method, the implementation of this electronic device can refer to the implementation process of the above method embodiment, and the repeated parts will not be elaborated here.

[0103] Embodiment 4

[0104] An embodiment of the present invention further provides a computer-readable storage medium, in which at least one instruction, at least one program, a code set or an instruction set is stored, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement a Figure 1 radar indoor positioning method based on CFAR and clustering algorithms as shown.

[0105] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and this program can be stored in a computer-readable storage medium. The storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically-erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc memories, magnetic disk memories, tape memories, or any other computer-readable medium capable of carrying or storing data.

[0106] Since this storage medium is the storage medium corresponding to the radar indoor positioning method based on CFAR and clustering algorithms in the embodiment of the present invention, and the principle of solving problems by this storage medium is similar to that of this method, the implementation of this storage medium can refer to the implementation process of the above method embodiment, and the repeated parts will not be described again.

[0107] Embodiment 5

[0108] In some possible embodiments, aspects of the method of the embodiments of the present invention can also be implemented in the form of a program product, which includes program code. When the program product runs on a computer device, the program code is used to cause the computer device to execute the steps of a radar indoor positioning method based on CFAR and clustering algorithms according to various exemplary embodiments described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment can be written in high-level programming languages such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0109] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0110] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0111] The above embodiments are only for illustrating the technical concept and characteristics of the present invention, and their purpose is to enable those of ordinary skill in the art to understand the content of the present invention and implement it accordingly, and cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the essence of the content of the present invention should be covered by the protection scope of the present invention.

Claims

1. A radar indoor positioning method based on CFAR and clustering algorithms, characterized in that, Including the following steps: Collect radar data and alternately store the collected detection data using multiple memories; Divide the data in the memory into multiple data segments for each frame, perform correlation operations based on the data segments, and use the CFAR algorithm of data variance to identify the potential target point cloud distribution P; Use the density-based mean clustering algorithm to cluster the potential target point cloud distribution P, and determine the number N and central position R of each cluster in the clustering target set; According to the shoulder width and chest size of the target individual, the number N and central position R of each cluster in the clustering target set, adaptively identify the distribution of the real target individual using the exponential function rule.

2. The radar indoor positioning method based on CFAR and clustering algorithm according to claim 1, wherein The multiple memories include memory A and memory B; Continuously and alternately store the collected detection data into memory A and memory B: Store the detection data T1_signal with a collection duration of T into memory A; when memory A is full, store the detection data T2_signal with a collection duration of T into memory B and clear memory A; when memory B is full, return to store the detection data T1_signal with a collection duration of T into memory A and clear memory B.

3. A radar indoor positioning method based on CFAR and clustering algorithms according to claim 1, characterized in that, The step of dividing the data in the memory into multiple data segments for each frame, performing correlation operations based on the data segments, and using the CFAR algorithm of data variance to identify the potential target point cloud distribution P includes: The detection data in the memory is divided into multiple data segments G1, G2, …, G for each frame m , and relevant operation preprocessing is performed to obtain data J1, J2, …, J m , and then they are shift-registered into the C memory; According to the data in memory C, parallelly calculate the variance of each sampling point of each frame under M chirps, perform CFAR algorithm calculation based on the variance data, and draw the variance threshold curve of N sampling points for each frame; When the variance of a certain sampling point in the i-th frame exceeds the variance threshold curve of the corresponding sampling point in this frame, it is determined that this sampling point is a potential target.

4. A radar indoor positioning method based on CFAR and clustering algorithms according to claim 3, characterized in that, The correlation operation preprocessing includes: vector mean cancellation processing, time-domain correlation based on convolution, or frequency-domain correlation after Fourier transform.

5. A radar indoor positioning method based on CFAR and clustering algorithms according to claim 1, characterized in that, The step of using the density-based mean clustering algorithm to cluster the potential target point cloud distribution P and determine the number N and central position R of each cluster in the clustering target set includes: Use the density-based clustering algorithm to cluster the obtained target point cloud distribution P to obtain the distribution of the clustering target set and the number N of each cluster in the clustering target set; Use the averaging algorithm to calculate the central position R of each clustering target set.

6. A radar indoor positioning method based on CFAR and clustering algorithms according to claim 5, characterized in that, The density-based clustering algorithm includes: DBSCAN algorithm, OPTICS algorithm, DENCLUE algorithm; the averaging algorithm includes: arithmetic averaging algorithm, weighted averaging algorithm, geometric averaging algorithm, harmonic averaging algorithm, simple sliding averaging algorithm, weighted sliding averaging algorithm, exponential weighted sliding averaging algorithm.

7. A radar indoor positioning method based on CFAR and clustering algorithms according to claim 1, characterized in that The step of adaptively identifying the distribution of the real target individual using the exponential function rule according to the shoulder width and chest size of the target individual, the number N and central position R of each cluster in the clustering target set includes: Separate each cluster in the target set according to the shoulder width W and chest size D of the target individual; when the horizontal width of cluster c in the target set exceeds a preset multiple of the shoulder width W of the target individual, or the vertical width of cluster c exceeds the chest size D, divide the cluster c into two sub-target clusters according to the central position R; For each cluster in the target set, calculate the expected number of target points Tp = αmax(N)exp(-abs(R) / β) for each cluster according to the radial distance abs(R) between the center of the target set and the radar and the maximum number of target points max(N) in the cluster, where α adjusts the maximum number of detectable potential human target points in all clusters, and β makes the threshold distance-sensitive; When the number of clusters in the clustering target set is greater than Tp, determine that the cluster is the target individual.

8. A radar indoor positioning method based on CFAR and clustering algorithms according to claim 1, characterized in that, The radar indoor positioning method further includes: After successful target positioning, perform vital sign monitoring and posture recognition during idle time to achieve comprehensive monitoring and analysis of the target individual; Among them, within the duration T = T1 + T2, the first half of the time T1 is used for target positioning, and the second half of the time T2 is used for vital sign monitoring and posture recognition; the second half of the time T2 is only performed after successful target positioning. If the target is not located, there is no need to perform vital sign monitoring and posture recognition.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, At least one instruction, at least one program, a code set, or an instruction set is stored in the storage medium. The at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 8.

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