Target object detection method and device based on WiFi signal and storage medium

Abstract

The application relates to a target object detection method and device based on a WiFi signal and a storage medium, wherein the target object detection method based on the WiFi signal comprises the following steps: obtaining channel state information by acquiring the channel state information extracted by a plurality of single-antenna sensing devices which are distributed in advance and have WiFi communication and WiFi sensing capabilities; comparing the average amplitude values of subcarriers of the channel state information to screen a plurality of target single-antenna sensing devices from the plurality of single-antenna sensing devices which are distributed in advance; reacquiring channel state information characteristic values of a target object by the plurality of target single-antenna sensing devices; and analyzing and calculating the channel state information characteristic values to obtain specific position information of the target object. The application solves the problem that a single-antenna device cannot simultaneously realize target communication and sensing for target object detection.

Description

Technical Field

[0001] This application relates to the field of wireless sensing technology, and in particular to a method, apparatus and storage medium for detecting target objects based on WiFi signals. Background Technology

[0002] Wireless sensing technology utilizes wireless signals (such as WiFi and Bluetooth) to detect and understand the surrounding environment. This technology is primarily based on the scattering, reflection, diffraction, and attenuation properties of wireless signals. When a wireless signal encounters an object (such as a human body), the signal waveform is altered, thus carrying information about the object, such as its position, speed, and shape. By carefully analyzing the received signals, wireless sensing technology can be used for applications such as localization, tracking, and behavior recognition.

[0003] Currently, most sensing technologies in the industry are based on dual-antenna or multi-antenna sensing, requiring wireless hardware with at least two antennas. The direction of signal propagation is measured by the phase difference caused by the multipath effect (different antennas receive the same frame of data with different amplitudes and phases).

[0004] There is currently no effective solution to the problem that related technologies cannot simultaneously achieve target communication and sensing for target object detection using a single antenna device. Summary of the Invention

[0005] This embodiment provides a target object detection method, device, and storage medium based on WiFi signals to solve the problem in related technologies that target object detection cannot be achieved simultaneously through a single antenna device by means of target communication and sensing.

[0006] Firstly, this embodiment provides a target object detection method based on WiFi signals, including:

[0007] The channel status information is obtained by extracting a number of pre-distributed single-antenna sensing devices, which have WiFi communication and WiFi sensing capabilities.

[0008] By comparing the average amplitude of the subcarriers of each channel state information, a number of target single-antenna sensing devices are selected from the pre-distributed number of single-antenna sensing devices.

[0009] The channel state information feature values ​​of the target object are reacquired through the aforementioned target single-antenna sensing devices;

[0010] The specific location information of the target object is obtained by analyzing and calculating the feature values ​​of the channel state information.

[0011] In some embodiments, prior to acquiring the channel state information, the method further includes:

[0012] The WiFi communication status and WiFi sensing status of each single-antenna sensing device are unified at the same time, so that all single-antenna sensing devices are in the same WiFi communication status and WiFi sensing status at the same time.

[0013] In some embodiments, before comparing the average amplitude of the subcarriers of the channel state information and selecting a plurality of target single-antenna sensing devices from the pre-distributed plurality of single-antenna sensing devices, the method further includes:

[0014] The changes in phase and amplitude components in each channel state information are extracted to determine whether a target object exists. If the target object exists, the average amplitude of the subcarriers in each channel state information is compared, and several target single-antenna sensing devices are selected from the several pre-distributed single-antenna sensing devices.

[0015] In some embodiments, the plurality of target single-antenna sensing devices are the two single-antenna sensing devices with the largest average amplitude of the subcarriers of each channel state information.

[0016] In some embodiments, before reacquiring the channel state information feature values ​​of the target object through the plurality of target single-antenna sensing devices, the method further includes:

[0017] The device information list of the target single-antenna sensing devices is sent to the corresponding target single-antenna sensing devices through a custom network message, so that the target single-antenna sensing devices can re-acquire the channel state information feature values ​​of the target object based on the device information list.

[0018] In some embodiments, the device information list of the target single-antenna sensing device includes: the hardware address of the target single-antenna sensing device, the device number of the target single-antenna sensing device, and the device name of the target single-antenna sensing device.

[0019] In some embodiments, the analysis and calculation of the channel state information feature values ​​to obtain the specific location information of the target object includes:

[0020] Using the channel state information ranging principle, the characteristic values ​​of the channel state information are analyzed and calculated to obtain the device distance between the plurality of target single-antenna sensing devices;

[0021] Based on the device distance and the Doppler effect, the angle of the target object relative to the target single-antenna sensing device is obtained;

[0022] The distance between the target object and the target single-antenna sensing device is inferred using a wireless attenuation model.

[0023] The angle relative to the target single-antenna sensing device and the distance relative to the target single-antenna sensing device constitute the specific location information of the target object.

[0024] Secondly, this embodiment provides a target object detection device based on WiFi signals, including: a first acquisition module, a comparison module, a second acquisition module, and an analysis and calculation module, wherein...

[0025] The first acquisition module is used to acquire channel state information, which is extracted by a number of pre-distributed single-antenna sensing devices.

[0026] The comparison module is used to compare the average amplitude of the subcarriers of each channel state information and to select several target single-antenna sensing devices from the pre-distributed set of several single-antenna sensing devices.

[0027] The second acquisition module is used to reacquire the channel state information feature values ​​of the target object through the plurality of target single-antenna sensing devices;

[0028] The analysis and calculation module is used to analyze and calculate the feature values ​​of the channel state information to obtain the specific location information of the target object.

[0029] Thirdly, this embodiment provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the target object detection method based on WiFi signals described in the first aspect above.

[0030] Fourthly, this embodiment provides a storage medium storing a computer program that, when executed by a processor, implements the target object detection method based on WiFi signals described in the first aspect above.

[0031] Compared with related technologies, the target object detection method based on WiFi signals provided in this embodiment obtains channel state information, which is extracted by a plurality of pre-distributed single-antenna sensing devices, each of which has WiFi communication and WiFi sensing capabilities. The method compares the average subcarrier amplitude of each channel state information and selects a plurality of target single-antenna sensing devices from the pre-distributed single-antenna sensing devices. The method then re-acquires the channel state information feature values ​​of the target object through these target single-antenna sensing devices. Finally, it analyzes and calculates these channel state information feature values ​​to obtain the specific location information of the target object. This method solves the problem that it is impossible to simultaneously achieve target communication and sensing for target object detection using a single antenna device.

[0032] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0033] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0034] Figure 1 This is a hardware structure block diagram of the terminal of the target object detection method based on WiFi signals in this embodiment;

[0035] Figure 2 This is a flowchart of the target object detection method based on WiFi signals in this embodiment;

[0036] Figure 3 This is a schematic diagram of the distributed setup of a single-antenna sensing device in the WiFi signal-based target object detection method of this embodiment;

[0037] Figure 4 This is a schematic diagram illustrating the unified status information of a single-antenna sensing device in the target object detection method based on WiFi signals in this embodiment.

[0038] Figure 5 This is a flowchart of another target object detection method based on WiFi signals in this embodiment;

[0039] Figure 6 This is the timing logic diagram for target object detection based on WiFi signals in this embodiment;

[0040] Figure 7 This is a structural block diagram of the target object detection device based on WiFi signals in this embodiment. Detailed Implementation

[0041] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0042] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0043] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. For example, it can run on a terminal. Figure 1 This is a hardware structure block diagram of the terminal for the target object detection method based on WiFi signals in this embodiment. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 and a memory 104 for storing data are also included. The processor 102 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0044] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the WiFi signal-based target object detection method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, thereby implementing the aforementioned method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0045] The transmission device 106 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 can be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0046] Explanation of technical terms:

[0047] CSI: Channel State Information (CSI) is a technique for obtaining channel state information through changes in the phase and amplitude of wireless signals. CSI contains amplitude and phase information of all subcarriers in the channel state.

[0048] LOS / NLOS: The propagation conditions of wireless communication systems are typically divided into two environments: line-of-sight (LOS) and non-line-of-sight (NLOS). Under LOS conditions, the wireless signal propagates in a straight line between the transmitter and receiver without obstruction. Under NLOS conditions, when there are obstructions between the transmitter and receiver, the wireless signal suffers not only attenuation but also reflection, diffraction, and penetration loss. This multipath effect affects the estimation of communication distance, sensing, and transmission bandwidth.

[0049] The Doppler effect refers to the phenomenon where the frequency of sound, light, or other waves changes as a moving object (such as a signal source or receiver) moves closer to or further away from an observer. In wireless communication, the Doppler effect also applies to electromagnetic waves. When there is movement between the wireless signal source and receiver, the Doppler effect shifts the signal frequency, thus affecting the transmission quality.

[0050] RSSI (Received Signal Strength Indication) is an important indicator in wireless communication used to represent the strength of received signals. It is typically expressed as a negative number in dBm (decibels per milliwatt), with larger negative values ​​indicating weaker signal strength. RSSI values ​​can be used to assess the quality of a communication link, thus affecting communication stability and speed.

[0051] This embodiment provides a target object detection method based on WiFi signals. Figure 2 This is a flowchart of the target object detection method based on WiFi signals in this embodiment, as follows: Figure 2 As shown, the process includes the following steps:

[0052] Step S201: Obtain the channel status information. The channel status information is extracted by a number of pre-distributed single-antenna sensing devices, which have WiFi communication and WiFi sensing capabilities.

[0053] Specifically, Figure 3 This is a schematic diagram of the distributed setup of a single-antenna sensing device in the target object detection method based on WiFi signals in this embodiment, as shown below. Figure 3 As shown, the target object detection method based on WiFi signals provided in this embodiment includes several single-antenna sensing devices and a server. Each single-antenna sensing device transmits signals to the server through its own WiFi communication link. In this embodiment, several single-antenna sensing devices are pre-deployed, each possessing both WiFi communication and WiFi sensing capabilities. In this embodiment, the single-antenna sensing devices are also referred to as integrated sensing devices. Integrated sensing devices are installed at different locations, one-to-one. Figure 3 The integrated sensing devices are 1, 2, 3, 4, 5, 6, 7, and 8. The specific number of integrated sensing devices can be set according to actual conditions; the number in this embodiment is merely illustrative and not specifically limited. When any radio frequency signal emitted by a WiFi terminal is detected within the coverage area, the WiFi signal from the WiFi terminal is received by each integrated sensing device, CSI channel status information is extracted, and the CSI feature information is reported to the server through their respective WiFi communication links.

[0054] Step S202: Compare the average amplitude of subcarriers of each channel state information, and select several target single-antenna sensing devices from a number of pre-distributed single-antenna sensing devices.

[0055] Specifically, after collecting CSI channel state information from several distributed single-antenna sensing devices, the amplitude of each subcarrier in each CSI channel state information is calculated, and the average value is obtained. The average amplitude of the subcarriers of each single-antenna sensing device is compared, and several devices with larger amplitudes are selected from all single-antenna sensing devices as target single-antenna sensing devices. For example, two devices with the largest amplitudes are selected. These two devices have the strongest signal strength and are considered to be the devices closest to the target being measured. The number of target single-antenna sensing devices can be set according to actual needs, and this embodiment does not impose a specific limitation on this. Figure 3 As shown, if the target object is located at point 2, the detection paths of integrated sensing devices 1 and 2 are blocked, preventing line-of-sight (LOS) propagation. Therefore, they cannot acquire the WiFi signals emitted by integrated sensing devices 4 and 3 via a straight-line approach. In this case, the optimal sensing devices are integrated sensing devices 3 and 4. These two devices are on a direct path, unobstructed, and closest to the target object. The carrier average amplitude in the CSI channel state information extracted by these two devices is relatively large, and their detected signals are the strongest. Therefore, integrated sensing devices 3 and 4 are identified as the target single-antenna sensing devices. By selecting the optimal devices, the most accurate detection results are provided, supporting further analysis and decision-making, and improving detection accuracy.

[0056] Step S203: Reacquire the channel state information feature values ​​of the target object through several target single-antenna sensing devices; analyze and calculate the channel state information feature values ​​to obtain the specific location information of the target object.

[0057] After selecting several target single-antenna sensing devices from a pre-set pool of single-antenna sensing devices, the server notifies these devices via a custom network message, enabling them to reacquire any WiFi signal from the peer. For example... Figure 3 As shown, after determining the target single-antenna sensing device, the integrated sensing device 3, and the integrated communication device 4 in step S202 above, the server sends a custom network message notification to the integrated sensing device 3 and the integrated communication device 4, commanding them to reacquire the WiFi signals emitted by their respective peer devices and re-extract the CSI channel state information feature values. These CSI channel state information feature values ​​are then uploaded to the server via the WiFi communication link. Based on the newly received CSI channel state information feature values, the server calculates the distance between the target single-antenna sensing devices using the CSI ranging principle, such as... Figure 3The distance between the integrated sensing device 3 and the integrated communication device 4 is distance (D3,4). The CSI ranging method can utilize phase or amplitude data from the received CSI channel state information through a distance model, or it can employ a fingerprint matching algorithm to estimate the distance. The choice can be made based on actual needs, and this embodiment does not impose specific limitations.

[0058] In CSI positioning, the distance *r* of the target object is inferred using a wireless attenuation model based on the Received Signal Strength Indication (RSSI) value. The angle *θ* of the target object relative to the target single-antenna sensing device is calculated using the phase difference method. By observing the phase changes of the signal at different receivers, the incident angle *θ* of the receiving device can be inferred. The high sensitivity of the CSI phase to the channel can help filter the optimal CSI amplitude fingerprint space to construct a fingerprint database. For example, when the target moves a small distance, there is a very small path difference between the two reflection paths; the CSI phase difference can be used to determine the target's movement distance, and the approximate ratio of the angle to the length change can be used for centimeter-level path tracking. After determining the distance and angle of the target object relative to the target single-antenna detection device, the specific location information of the target object is obtained.

[0059] By analyzing the frequency changes of the received signal, the velocity of the wave source relative to the receiving device is calculated, enabling the positioning algorithm to accurately calculate the moving speed and direction of the target object. The direction of movement is determined by the CSI phase difference, and the velocity is calculated using the Doppler effect. Combining these two effects allows for a more accurate inference of the target object's position and motion state.

[0060] Through steps S201 to S203, channel state information is acquired. This information is extracted by a pre-distributed array of single-antenna sensing devices, each possessing both WiFi communication and sensing capabilities. The average subcarrier amplitude of each channel state information is compared, and several target single-antenna sensing devices are selected from the pre-distributed array. The channel state information feature values ​​of the target object are then re-acquired using these target single-antenna sensing devices. The channel state information feature values ​​are analyzed and calculated to obtain the specific location information of the target object. Compared to existing technologies that use dual-antenna or multi-antenna sensing devices to detect target objects, this embodiment detects target objects by distributively deploying multiple single-antenna sensing devices. Each single-antenna sensing device extracts CSI channel state information and uploads it to the server via a WiFi communication link. The server compares the average amplitude of the CSI subcarriers of each single-antenna sensing device, selects the optimal single-antenna sensing device to re-acquire the channel state information features of the target object, and re-reports it to the server. The server further analyzes the channel state information feature values ​​to determine the location information of the target object. This enables the detection of target objects while simultaneously achieving target communication and sensing using only a single-antenna sensing device.

[0061] In some embodiments, the method further includes:

[0062] The WiFi communication status and WiFi sensing status of each single-antenna sensing device are unified at the same time, so that all single-antenna sensing devices are in the same WiFi communication status and WiFi sensing status at the same time.

[0063] Specifically, the single-antenna sensing device has both WiFi communication and WiFi sensing capabilities. To ensure consistency in the detection and judgment of target objects, the server uniformly sets the sensing time slots and sensing windows for all single-antenna sensing devices, so that each single-antenna sensing device is either in WiFi communication mode or in WiFi sensing mode at the same time. Figure 4 This is a schematic diagram illustrating the unified status information of a single-antenna sensing device in the target object detection method based on WiFi signals in this embodiment. Figure 4 As shown, different single-antenna sensing devices are all in the same communication or sensing state at the same time. By unifying the communication and sensing states of different single-antenna sensing devices, the accuracy of target object detection can be improved.

[0064] In another embodiment, before comparing the average amplitude of subcarriers of each channel state information and selecting a plurality of target single-antenna sensing devices from a plurality of pre-distributed single-antenna sensing devices, the method further includes:

[0065] The changes in phase and amplitude components in each channel state information are extracted to determine whether a target object exists. If a target object exists, the average amplitude of the subcarriers in each channel state information is compared, and several target single-antenna sensing devices are selected from a number of pre-distributed single-antenna sensing devices.

[0066] Specifically, after receiving the CSI channel state information extracted by each single-antenna sensing device, the server analyzes the changes in the phase and amplitude components of the CSI channel state information to determine the presence of a target object. The direct path signal is usually the strongest signal and has the shortest time delay. If the CSI data shows that the first arriving signal has the strongest amplitude and is accompanied by the smallest phase change, it can be preliminarily determined that this is a line-of-sight (LOS) environment. Conversely, if all CSI subcarrier data show that multiple signals with different phases and amplitudes arrive at the receiver, and these signals have long delays, it is determined that an obstruction exists, i.e., a target object is present, indicating a non-line-of-sight (NLOS) environment. The CSI phase component is very sensitive to changes in the channel, especially when a target object is present, as the signal propagation path changes, leading to phase changes. These changes can be detected by comparing the CSI phase components at different time points or at different receivers. The CSI amplitude component (i.e., signal strength) also reflects changes in the signal propagation path. When the signal passes through a target object, the signal attenuation increases, resulting in a decrease in the received signal amplitude. By monitoring changes in the CSI amplitude, it can be inferred whether the signal path has been affected by the target object. By simultaneously analyzing the phase and amplitude changes of CSI, the presence of a target object can be determined more accurately. For example, if both phase and amplitude change significantly, it may indicate that the target object is obstructing the signal. The server first uses the phase and amplitude changes in the CSI information to detect the presence of the target object. If the target object is present, the server compares the average amplitude of the subcarriers in the channel state information to select the target single-antenna sensing device from among the single-antenna sensing devices for further detection, thereby improving the accuracy of target object detection and the robustness of the system.

[0067] In some embodiments, the target single-antenna sensing devices are the two single-antenna sensing devices with the largest average amplitude of the subcarriers of each channel state information.

[0068] Specifically, such as Figure 3As shown, if the target object is located at point 2, the detection paths of integrated sensing devices 1 and 2 are blocked, preventing line-of-sight (LOS) propagation. Therefore, the WiFi signals emitted by integrated sensing devices 4 and 3 cannot be obtained in a straight line. In this case, the optimal sensing devices are integrated sensing device 3 and integrated sensing device 4. These two devices are on the direct path, unobstructed, and closest to the target object. The average subcarrier amplitude in the CSI channel state information extracted by these two devices is relatively large, and their detected signal is also the strongest. Therefore, integrated sensing devices 3 and 4 are identified as target single-antenna sensing devices. By selecting the optimal devices, the most accurate detection results are provided, supporting further analysis and decision-making, and improving detection accuracy. In this embodiment, at least two target single-antenna sensing devices are used. Accurate detection of the target object can be achieved using two single-antenna sensing devices with the largest subcarrier average amplitude, reducing the cost of target object detection equipment. In addition, without considering equipment costs, the number of target single-antenna sensing devices can also be other data; the more devices there are, the higher the detection accuracy.

[0069] In another embodiment, before reacquiring the channel state information feature values ​​of the target object through several target single-antenna sensing devices, the method further includes:

[0070] A list of device information for each target single-antenna sensing device is sent to the corresponding target single-antenna sensing device via a custom network message, enabling these devices to re-acquire the channel state information feature values ​​of the target object based on the device information list. The device information list includes: the hardware address, device number, and device name of each target single-antenna sensing device.

[0071] Specifically, firstly, after filtering and identifying the target single-antenna sensing devices, the server constructs a device information list containing all target single-antenna sensing devices. This list should at least include the device's hardware address (MAC address), device number, and device name. Then, the server defines a custom network message protocol to encapsulate the device information list into a network message. This protocol needs to ensure message integrity and correctness, and be easily parsed by the target single-antenna sensing devices. The server sends the device information list to each target single-antenna sensing device via the network. After receiving the custom network message from the server, the target single-antenna sensing device parses the device information list according to the predefined protocol. Based on the parsed device information list, the target single-antenna sensing device reacquires the channel state information (CSI) characteristic values ​​of the target object and sends the collected CSI data back to the server. The server analyzes this data to determine the location and characteristics of the target object. Through this process, the server can effectively manage and direct the target single-antenna sensing devices to reacquire the CSI characteristic values ​​of the target object, thereby achieving accurate detection and positioning of the target object.

[0072] In some embodiments, channel state information feature values ​​are analyzed and calculated to obtain the specific location information of the target object, including:

[0073] By employing the channel state information ranging principle, the characteristic values ​​of the channel state information are analyzed and calculated to obtain the device distance between several target single-antenna sensing devices. Based on the device distance and the Doppler effect, the angle of the target object relative to the target single-antenna sensing device is obtained. The distance of the target object relative to the target single-antenna sensing device is inferred using a wireless attenuation model. The angle relative to the target single-antenna sensing device and the distance relative to the target single-antenna sensing device constitute the specific location information of the target object.

[0074] Specifically, Channel State Information (CSI) includes attenuation factors of the signal along each transmission path, such as signal scattering, environmental attenuation, and distance attenuation. CSI describes the propagation process of the wireless signal between the transmitter and receiver, including the effects of distance, scattering, and fading on the signal. By analyzing the changes in the phase and amplitude components of the CSI, changes in the signal propagation path can be inferred, and thus the distance between devices can be calculated. Using the phase and amplitude information in the CSI, the distance between target single-antenna sensing devices can be calculated. Each element in the CSI represents the amplitude and phase of a subcarrier. By comparing the CSI phase components at different time points or between different target single-antenna sensing devices, changes in the CSI phase components are detected, thereby calculating the distance between the target single-antenna sensing devices. Figure 3The distance D(3,4) mentioned above is the distance between target single-antenna sensing device 3 (i.e., integrated sensing device 3) and target single-antenna sensing device 4 (i.e., integrated sensing device 4). The angle of the target object relative to the target single-antenna sensing device is then calculated using the Doppler effect. When there is relative motion between the signal source and the target single-antenna sensing device, the frequency of the received signal will change. By analyzing this frequency change, the angle of the target object relative to the target single-antenna sensing device can be calculated. The specific formula for calculating the angle θ is as follows: ;

[0075] Where λ is the wavelength of the transmitted signal, and d is the distance between the two single-antenna sensing devices (i.e., the distance D(3,4)). The phase difference of the same signal received by a single-antenna sensing device.

[0076] The distance *r* between the target object and the target single-antenna sensing device is then inferred using a wireless attenuation model, such as the Okumura-Hata model. This model considers the attenuation of signal power with distance and determines the distance based on the relationship between signal power RSSI and distance. A log-normal distance path loss (LDPL) model is used to model the path loss, reflecting the change in signal power with distance. Trilateration methods are then used to locate the target object, obtaining the distance *r* between the target object and the target single-antenna sensing device. The specific wireless attenuation model can be set according to actual conditions; this embodiment does not impose specific limitations. This allows the target object to be located at a distance *r* and an angle of *θ* from the target single-antenna sensing device, thus enabling the single-antenna sensing device to detect the target object.

[0077] This embodiment also provides a target object detection method based on WiFi signals. Figure 5 This is a flowchart of another target object detection method based on WiFi signals in this embodiment, such as... Figure 5 As shown, the process includes the following steps:

[0078] Step S501: Unify the WiFi communication state and WiFi sensing state of each single-antenna sensing device at the same time, so that all single-antenna sensing devices are in the same WiFi communication state and WiFi sensing state at the same time.

[0079] Step S502: Obtain the channel status information. The channel status information is extracted by a number of pre-distributed single-antenna sensing devices, which have WiFi communication and WiFi sensing capabilities.

[0080] Step S503: Extract the changes in phase and amplitude components from each channel state information to determine whether a target object exists;

[0081] Step S504: When it is determined that a target object exists, compare the average amplitude of the subcarriers of each channel state information, and select the two single-antenna sensing devices with the largest average amplitude of the subcarriers of the channel state information from a number of pre-distributed single-antenna sensing devices as the target single-antenna sensing devices.

[0082] Step S505: Send the device information list of the target single-antenna sensing devices to the corresponding target single-antenna sensing devices through a custom network message, so that the target single-antenna sensing devices can re-acquire the channel status information feature values ​​of the target object according to the device information list and re-upload them to the server.

[0083] In step S506, the server uses the channel state information ranging principle to analyze and calculate the channel state information feature values ​​to obtain the device distance between several target single-antenna sensing devices; based on the device distance and the Doppler effect, the angle θ of the target object relative to the target single-antenna sensing device is obtained; the distance r of the target object relative to the target single-antenna sensing device is inferred using a wireless attenuation model; the angle θ and the distance r relative to the target single-antenna sensing device constitute the specific location information of the target object.

[0084] Through steps S501 to S506 above, compared with the prior art of detecting target objects using dual-antenna or multi-antenna sensing devices, this embodiment detects target objects by distributing multiple single-antenna sensing devices. Each single-antenna sensing device extracts CSI channel state information and uploads it to the server via a WiFi communication link. The server extracts the changes in phase and amplitude components in each CSI information to determine whether there is a target object blocking it. Then, by comparing the average amplitude of the CSI subcarriers of each single-antenna sensing device, the two single-antenna sensing devices with the largest amplitudes are selected to re-acquire the CSI feature values ​​of the target object and re-report them to the server. The server uses the CSI ranging principle and the Doppler effect to calculate the angle information of the target object relative to the target single-antenna sensing device. Then, a wireless attenuation model is used to infer the distance of the target object relative to the target single-antenna sensing device, thereby determining the position information of the target object. The position information includes the target angle and the target distance, realizing the detection of target objects by using only a single-antenna sensing device to simultaneously achieve target communication and sensing.

[0085] This embodiment also provides a timing logic for target object detection based on WiFi signals. Figure 6 This is a timing logic diagram for target object detection based on WiFi signals in this embodiment, illustrated using the example of a moving target entering the control area of ​​single-antenna sensing device 3 and single-antenna sensing device 4. Figure 6As shown in the diagram, all sensing devices are single-antenna sensing devices. When a moving target enters the deployment area of ​​single-antenna sensing device 3 and single-antenna sensing device 4, each sensing device connects to the WiFi network. The cloud platform server sends communication and sensing window parameters to each device, unifying the communication and sensing states of each device at the same time, ensuring that each device is either in communication mode or in sensing mode simultaneously. The sensing and communication functions of each device continue to be executed alternately. The server waits for the sensing devices to initiate access requests. After each sensing device initiates an access request, it connects to the cloud platform server. Upon successful access, the server sends an ACK (acknowledgment of successful access) to each sensing device. Each sensing device detects a moving target and reports CSI channel status information to the server. The server receives the CSI channel status information sent by each device, performs data analysis, first determines whether there is an obstruction, and if an obstruction is detected, selects the two best sensing devices with the largest average amplitude of the subcarrier: sensing device 3 and sensing device 4. The server sends a sensing device information list to sensing device 3 and sensing device 4, including the MAC address, device number, device name, etc. of the two devices. Sensing device 3 and sensing device 4 re-extract the peer's CSI feature information and report it to the server. The server calculates the angle and distance information of the moving target relative to sensing device 3 based on the newly received CSI information, and detects the moving target.

[0086] This embodiment also provides a target object detection device based on WiFi signals. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that implement a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0087] Figure 7 This is a structural block diagram of the target object detection device based on WiFi signals in this embodiment, as shown below. Figure 7 As shown, the device 70 includes: a first acquisition module 71, a comparison module 72, a second acquisition module 73, and an analysis and calculation module 74, wherein,

[0088] The first acquisition module 71 is used to acquire channel status information, which is extracted by a number of pre-distributed single-antenna sensing devices.

[0089] Comparison module 72 is used to compare the average amplitude of subcarriers of each channel state information and select several target single-antenna sensing devices from a number of pre-distributed single-antenna sensing devices.

[0090] The second acquisition module 73 is used to reacquire the channel state information feature values ​​of the target object through several target single-antenna sensing devices;

[0091] The analysis and calculation module 74 is used to analyze and calculate the characteristic values ​​of the channel state information to obtain the specific location information of the target object.

[0092] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0093] This embodiment also provides an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0094] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0095] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0096] S1, acquire the channel status information. The channel status information is extracted by a number of pre-distributed single-antenna sensing devices. The single-antenna sensing devices have WiFi communication capability and WiFi sensing capability.

[0097] S2, compare the average amplitude of subcarriers of each channel state information, and select several target single-antenna sensing devices from a number of pre-distributed single-antenna sensing devices;

[0098] S3 reacquires the channel state information feature values ​​of the target object through several target single-antenna sensing devices;

[0099] S4. Analyze and calculate the characteristic values ​​of the channel state information to obtain the specific location information of the target object.

[0100] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0101] Furthermore, in conjunction with the target object detection method based on WiFi signals provided in the above embodiments, this embodiment can also provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the target object detection methods based on WiFi signals in the above embodiments.

[0102] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0103] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0104] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0105] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0106] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0107] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for detecting target objects based on WiFi signals, characterized in that, include: The channel status information is obtained by extracting a number of pre-distributed single-antenna sensing devices, which have WiFi communication and WiFi sensing capabilities. The phase component and amplitude component are extracted from each channel state information. When multiple delayed phase components and different amplitude components are detected, it is determined that a target object exists. In the presence of the target object, the average amplitude of the subcarriers of each channel state information is compared, and several target single-antenna sensing devices are selected from the several pre-distributed single-antenna sensing devices. By reacquiring the WiFi signal emitted by the peer device of the plurality of single-antenna sensing devices, the channel state information feature value of the target object is obtained. The specific location information of the target object is obtained by analyzing and calculating the feature values ​​of the channel state information.

2. The target object detection method based on WiFi signals according to claim 1, characterized in that, Before acquiring the channel state information, the method further includes: The WiFi communication status and WiFi sensing status of each single-antenna sensing device are unified at the same time, so that all single-antenna sensing devices are in the same WiFi communication status and WiFi sensing status at the same time.

3. The target object detection method based on WiFi signals according to claim 1, characterized in that, The plurality of target single-antenna sensing devices are the two single-antenna sensing devices with the largest average amplitude of the subcarriers of each channel state information.

4. The target object detection method based on WiFi signals according to claim 1, characterized in that, Before reacquiring the channel state information feature values ​​of the target object through the plurality of target single-antenna sensing devices, the method further includes: The device information list of the target single-antenna sensing devices is sent to the corresponding target single-antenna sensing devices through a custom network message, so that the target single-antenna sensing devices can re-acquire the channel state information feature values ​​of the target object based on the device information list.

5. The target object detection method based on WiFi signals according to claim 4, characterized in that, The device information list of the target single-antenna sensing device includes: the hardware address of the target single-antenna sensing device, the device number of the target single-antenna sensing device, and the device name of the target single-antenna sensing device.

6. The target object detection method based on WiFi signals according to claim 1, characterized in that, The analysis and calculation of the channel state information feature values ​​to obtain the specific location information of the target object includes: Using the channel state information ranging principle, the characteristic values ​​of the channel state information are analyzed and calculated to obtain the device distance between the plurality of target single-antenna sensing devices; Based on the device distance and the Doppler effect, the angle of the target object relative to the target single-antenna sensing device is obtained; The distance between the target object and the target single-antenna sensing device is inferred using a wireless attenuation model. The angle relative to the target single-antenna sensing device and the distance relative to the target single-antenna sensing device constitute the specific location information of the target object.

7. A target object detection device based on WiFi signals, characterized in that, include: The system comprises a first acquisition module, a comparison module, a second acquisition module, and an analysis and calculation module, wherein... The first acquisition module is used to acquire channel state information, which is extracted by a number of pre-distributed single-antenna sensing devices. The comparison module is used to extract the phase component and amplitude component from each channel state information. When multiple different phase components and different amplitude components with multiple delays are detected, it is determined that a target object exists. In the case of the existence of the target object, the average amplitude of the subcarriers of each channel state information is compared, and several target single-antenna sensing devices are selected from the several pre-distributed single-antenna sensing devices. The second acquisition module is used to reacquire the WiFi signal emitted by the peer device of the plurality of single-antenna sensing devices through the plurality of target single-antenna sensing devices, and obtain the channel state information feature value of the target object; The analysis and calculation module is used to analyze and calculate the feature values ​​of the channel state information to obtain the specific location information of the target object.

8. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the target object detection method based on WiFi signals as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the target object detection method based on WiFi signals as described in any one of claims 1 to 6.

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