A method and system for measuring the speed of a moving target based on Wi-Fi signals

Abstract

The present invention proposes a method and system for measuring the speed of a moving target based on Wi-Fi signals, which relates to the field of wireless sensing technology. The problem addressed is that existing technologies cannot overcome the problems of noise interference and signal attenuation in complex environments, are not suitable for multi-scenario speed measurement, and have low speed estimation accuracy. The method obtains the Wi-Fi dynamic signal caused by the moving target; extracts the automatic gain control value and Doppler spectrum from it; calculates the shadow fading parameter based on the automatic gain control value; divides the Doppler spectrum and calculates the multi-scale speed; detects the shadow fading area based on the shadow fading parameter; determines whether it is within the shadow fading area, and if so, reconstructs the attenuation speed to obtain the reconstructed scale speed, and fuses the reconstructed scale data with the unreconstructed scale data to obtain the speed of the moving target. The present invention improves the accuracy and robustness of speed estimation, can effectively deal with the problems of noise interference and signal attenuation in complex environments, and is suitable for multi-scenario speed measurement.

Description

Technical Field

[0001] The present invention belongs to the field of wireless sensing technology, and in particular relates to a method and system for measuring the speed of a moving target based on Wi-Fi signals. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of intelligent IoT technologies, wireless sensing technology is becoming increasingly important in a variety of application scenarios, including smart homes, smart healthcare, and autonomous driving. In particular, traditional methods for measuring the velocity of moving objects often rely on wearable devices, visual sensors, radar, or ultrasonic sensors. However, these methods suffer from complex equipment deployment, high costs, and susceptibility to environmental constraints, making them unable to meet the low-cost, high-precision requirements of diverse scenarios. In recent years, non-contact velocity measurement technology based on Wi-Fi signals has gradually become a mainstream research direction and hotspot in this field due to its advantages such as strong device versatility, good privacy protection, and wide applicability.

[0004] Wi-Fi signals, as ubiquitous wireless communication signals, can propagate in complex environments and penetrate obstacles. When a target object moves, the Wi-Fi signal undergoes changes in amplitude, phase, and frequency due to reflection, diffraction, and the Doppler effect. These changes contain rich motion information. Through signal processing and spectrum analysis, dynamic features related to the target object's motion can be extracted and its velocity can be estimated. Doppler spectrum analysis, in particular, can accurately capture the signal frequency shift caused by target motion, providing important support for velocity measurement.

[0005] Wi-Fi signals show great potential in the field of motion speed sensing, but still face many technical challenges, mainly in the following three aspects:

[0006] (1) The impact of noise interference on signal quality. When using Wi-Fi signals for speed measurement, the signal will be significantly interfered with by the surrounding environment during propagation. For example, static or dynamic multipath reflection signals from walls, furniture, and other people will be superimposed on the signal of the target object, forming a complex signal mixing environment. This complex signal environment increases the difficulty of extracting effective information from the Wi-Fi signal, which seriously affects the accuracy of speed estimation. In addition, Wi-Fi devices themselves also have inherent noise problems. For example, when commercial Intel 5300 network cards collect Wi-Fi signals, there are problems such as center frequency offset, sampling frequency offset, and message detection delay, which further reduce signal quality. Therefore, before performing speed measurement, effective signal preprocessing technology must be used to eliminate environmental noise and device noise to ensure signal quality and the reliability of speed estimation results.

[0007] (2) Insufficient scenario applicability. When the target object passes through the shadow fading area, the Wi-Fi signal is prone to strong attenuation due to the serious impact of the target object on the direct, reflected and diffracted signals in the signal propagation process. This signal attenuation not only reduces the signal quality, but also leads to a significant decrease in signal perception performance, making the speed estimation unstable and inaccurate. Therefore, how to describe the motion characteristics of the target object in the shadow fading area, accurately reconstruct the signal and compensate for the attenuation has become the key to improving the accuracy and robustness of the speed measurement system.

[0008] (3) Traditional speed estimation methods are not accurate enough. Different moving targets have significantly different effects on Wi-Fi signal propagation. In particular, during motion, the reflection and diffraction characteristics of the signal are closely related to the relative position and speed of the target object. However, traditional Wi-Fi-based speed estimation methods usually only calculate speed by extracting the spectrum energy peak, ignoring the different spectrum responses of different objects during motion. This simplified estimation method cannot fully reflect the dynamic characteristics of the moving target in the spectrum. Therefore, how to extract velocity features of different scales based on the Doppler spectrum and accurately characterize the motion characteristics of the target object through velocity dynamic weight fusion technology is another major challenge to improve the accuracy and robustness of speed measurement.

[0009] In summary, environmental noise, multipath effects, and hardware attenuation significantly degrade signal quality, reducing the reliability and accuracy of velocity measurements. Furthermore, the diversity of targets and the complexity of scenarios make it difficult for existing technologies to simultaneously address multiple scenarios. Therefore, overcoming environmental interference and enhancing the robustness and accuracy of velocity measurement systems have become key challenges in this field. Summary of the Invention

[0010] To overcome the shortcomings of the aforementioned existing technologies, the present invention provides a method and system for measuring the speed of moving targets based on Wi-Fi signals. By leveraging key technologies such as dynamic signal extraction, multi-scale spectrum analysis, shadow fading parameter estimation, and multi-scale velocity fusion, the system significantly improves the accuracy and robustness of velocity estimation. It effectively addresses noise interference and signal attenuation in complex environments, is applicable to speed measurement of a variety of targets, and provides a new solution for the application of wireless sensing technology in the intelligent Internet of Things.

[0011] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0012] A first aspect of the present invention provides a method for measuring the speed of a moving target based on a Wi-Fi signal, comprising:

[0013] Acquire dynamic Wi-Fi signals caused by moving targets;

[0014] Extract automatic gain control values ​​based on the dynamic Wi-Fi signal caused by moving targets;

[0015] Calculate shadow fading parameters according to the automatic gain control value;

[0016] Extract Doppler spectrum based on the dynamic Wi-Fi signal caused by moving targets;

[0017] Perform multi-scale division on the Doppler spectrum and calculate multi-scale velocity;

[0018] Detect shadow fading area according to shadow fading parameters;

[0019] Determine whether the moving target enters the shadow fading area. If it is in the shadow fading area, reconstruct the attenuation velocity to obtain the reconstructed scale velocity. If not, no reconstruction is performed and the unreconstructed scale velocity is obtained.

[0020] The velocity fusion algorithm with dynamic weights is used to fuse the reconstructed scale data and the unreconstructed scale data to obtain the velocity of the moving target.

[0021] As an implementation method, the dynamic Wi-Fi signal caused by a moving target is obtained. The specific process is as follows:

[0022] Calculate the subcarrier performance parameters in the Wi-Fi dynamic signal and extract the subcarriers with the largest dynamic response based on the subcarrier performance parameters;

[0023] The dynamic signal ratio method is used to perform hardware noise suppression on subcarriers with large dynamic response;

[0024] Use Gaussian filter to perform Gaussian noise suppression on the signal after hardware noise suppression;

[0025] The signal after Gaussian noise suppression is processed by adaptive filtering to obtain the Wi-Fi dynamic signal caused by the moving target.

[0026] As an implementation method, the signal after Gaussian noise suppression is processed by adaptive filtering, and the specific process is as follows:

[0027] Perform linear interpolation on the signal after Gaussian noise suppression to obtain a continuous suppressed signal;

[0028] Utilize linear phase technology to keep all frequency components of the continuous suppression signal with the same time delay;

[0029] Use Butterworth bandpass filter to remove irrelevant static frequency components and suppress low-frequency drift and high-frequency noise;

[0030] Wavelet transform filtering is used to perform fine-grained dynamic signal extraction in each frequency band to obtain the Wi-Fi dynamic signal caused by the moving target.

[0031] As an implementation method, the automatic gain control value is extracted, and the specific process is: using a Python or MATLAB script of a channel state information tool to parse the automatic gain control value.

[0032] As an implementation method, the Doppler spectrum is extracted, and the specific process is as follows:

[0033] Extract the principal components from the Wi-Fi dynamic signal caused by the moving target through the singular value decomposition technique;

[0034] The Doppler spectrum is extracted from the principal components using short-time Fourier transform.

[0035] As an implementation method, it is determined whether there is shadow attenuation in the multi-scale velocity. If there is shadow attenuation, the attenuation velocity is reconstructed. The specific process is as follows:

[0036] Calculate the decay rate of multi-scale velocities in the presence of shadow decay;

[0037] The decay velocity is reconstructed by folding the multi-scale velocity data with shadow attenuation.

[0038] As an implementation method, a dynamic weighted velocity fusion algorithm is used to fuse the reconstructed scale velocity and the unreconstructed scale velocity to obtain the velocity of the moving target. The specific process is as follows:

[0039] Calculate multi-scale velocity characteristic parameters;

[0040] Calculate the correlation between scales of multi-scale velocities;

[0041] Based on the correlation calculation, the weights of the reconstructed scale velocity and the unreconstructed scale velocity are calculated respectively;

[0042] According to the reconstructed multi-scale velocity and the corresponding weights, the reconstructed scale velocity and the unreconstructed scale velocity are fused to obtain the velocity of the moving target.

[0043] A second aspect of the present invention provides a mobile target speed measurement system based on Wi-Fi signals, comprising:

[0044] Data acquisition module, used to obtain Wi-Fi dynamic signals caused by moving targets;

[0045] A shadow fading parameter estimation module is used to extract an automatic gain control value based on the Wi-Fi dynamic signal caused by the moving target; and calculate the shadow fading parameter based on the automatic gain control value;

[0046] The multi-scale spectrum analysis module is used to extract the Doppler spectrum based on the dynamic Wi-Fi signal caused by the moving target; divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity;

[0047] The shadow fading area detection module is used to detect the shadow fading area according to the shadow fading parameters; determine whether the moving target enters the shadow fading area. If it is in the shadow fading area, the attenuation velocity is reconstructed to obtain the reconstructed scale velocity; if not, no reconstruction is performed and the scale velocity is obtained;

[0048] The moving target speed measurement module is used to fuse the reconstructed scale data and the unreconstructed scale data using a dynamic weighted speed fusion algorithm to obtain the speed of the moving target.

[0049] As an implementation method, the data acquisition module includes a transmitting end and a receiving end for sending and receiving signals.

[0050] As an implementation manner, both the transmitting end and the receiving end are provided with a collection system platform based on a channel state information tool, and the collection system platform is set to a Monitor mode.

[0051] One or more of the above technical solutions have the following beneficial effects:

[0052] In this embodiment, through key technologies such as dynamic signal extraction, multi-scale spectrum analysis, shadow fading area detection and reconstruction, the challenges brought by environmental noise, hardware interference and multipath effects are effectively overcome, the signal quality and the reliability of the speed estimation results are improved, and the high precision and strong robustness of the speed measurement are ensured. At the same time, it is applicable to the speed measurement of various target objects and provides a new solution for the application of wireless sensing technology in the intelligent Internet of Things.

[0053] In this embodiment, based on the shadow fading area detection and the motion characteristics of the target object in the shadow fading area, the signal is accurately reconstructed and the attenuation is compensated, thereby improving the accuracy and robustness of the speed measurement system.

[0054] In this embodiment, velocity features of different scales are extracted based on the Doppler spectrum, and the motion characteristics of the target object are accurately characterized through the velocity dynamic weight fusion technology, which improves the reliability and applicability of velocity estimation and is applicable to various target objects and complex scenes.

[0055] In this embodiment, there is no need to deploy additional dedicated equipment, and the existing Wi-Fi environment is fully utilized, which greatly reduces the system deployment cost.

[0056] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0058] Figure 1 This is a schematic diagram of the Wi-Fi device used in the first embodiment;

[0059] Figure 2 This is a flow chart of a method for measuring the speed of a moving target based on Wi-Fi signals according to the first embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of multi-scale Doppler division and multi-scale velocity estimation in the first embodiment;

[0061] Figure 4 This is the effect diagram of the shadow fading area of ​​the first embodiment;

[0062] Figure 5 This is the effect diagram of speed reconstruction with shadow fading in the first embodiment;

[0063] Figure 6 This is a diagram showing the effect of the speed fusion calculation in the first embodiment. DETAILED DESCRIPTION

[0064] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0065] It should be noted that the terms used herein are for describing particular embodiments only and are not intended to limit the exemplary embodiments according to the present invention.

[0066] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0067] Example 1

[0068] This embodiment discloses a method for measuring the speed of a moving target based on Wi-Fi signals.

[0069] To more clearly illustrate this embodiment, a process for implementing a moving target speed measurement based on Wi-Fi signals can be specifically described as follows:

[0070] A method for measuring the speed of a moving target based on Wi-Fi signals, comprising:

[0071] S1. Acquire the Wi-Fi dynamic signal caused by the moving target;

[0072] S2. Extracting an automatic gain control value based on the Wi-Fi dynamic signal caused by the moving target;

[0073] Calculate shadow fading parameters according to the automatic gain control value;

[0074] S3. Extract Doppler spectrum based on the Wi-Fi dynamic signal caused by the moving target

[0075] Perform multi-scale division on the Doppler spectrum and calculate multi-scale velocity;

[0076] S4. Detecting the shadow fading area according to the shadow fading parameters;

[0077] Determine whether the moving target enters the shadow fading area. If it is in the shadow fading area, reconstruct the attenuation velocity to obtain the reconstructed scale velocity. If not, no reconstruction is performed and the unreconstructed scale velocity is obtained.

[0078] S5. Use the dynamic weighted velocity fusion algorithm to fuse the reconstructed scale data and the unreconstructed scale data to obtain the velocity of the moving target.

[0079] like Figure 1 、 Figure 2 As shown, in step S1, a Wi-Fi dynamic signal caused by a moving target is acquired.

[0080] S1-1. Collect Wi-Fi dynamic signal data.

[0081] In this embodiment, Wi-Fi transmitters and receivers are placed on either side of the target object's motion area for signal transmission and reception. To fully unleash the sensing capabilities of the transceiver devices, both are equipped with an acquisition system platform based on the Channel State Information Tool (CSI TOOL) and set to Monitor mode. In Monitor mode, the transmitter device can stably send data packets at a high transmission rate. To improve the accuracy of speed estimation, the signal transmission rate of the transmitter device is set to 1000 Hz. Python and Matlab are used to extract and process the Wi-Fi CSI data in the received data packets.

[0082] After the above steps, Wi-Fi dynamic signal data is collected, providing a data foundation for subsequent operations such as extracting Wi-Fi dynamic signals related to mobile targets, multi-scale spectrum analysis, and shadow fading area detection and reconstruction.

[0083] S1-2. Extract the Wi-Fi dynamic signal caused by the moving target.

[0084] In this embodiment, the Wi-Fi dynamic signals related to the mobile target are extracted from the collected Wi-Fi dynamic signals. The specific process is as follows:

[0085] (1) Calculate the subcarrier performance parameters in the Wi-Fi dynamic signal and extract the subcarrier with the largest dynamic response based on the subcarrier performance parameters;

[0086] In this embodiment, each transceiver link includes 30 subcarriers. To select subcarrier data that is least affected by noise and better reflects the motion characteristics of the target object, it is necessary to select subcarriers with the maximum dynamic response based on subcarrier performance parameters. The subcarrier performance parameter calculation formula is:

[0087]

[0088] Among them, γ represents the subcarrier performance parameter, represents the average value of 30 subcarriers, and σ represents the standard deviation of 30 subcarriers.

[0089] The larger the γ, the better the subcarrier dynamic performance. According to formula (1), the maximum γ value is calculated from 30 subcarriers. When the γ value is the largest, the dynamic response is the largest. The smaller the parameter is, the better the subcarrier dynamic performance. This is a comparison between subcarriers. By comparing the size of this parameter between subcarriers, the maximum value can be found.

[0090] After the above steps, the subcarriers with greater dynamic response to the moving target are screened out, providing a data basis for subsequent operations.

[0091] (2) Using the dynamic signal ratio method to perform hardware noise suppression on subcarriers with large dynamic response;

[0092] In this embodiment, the commercial Intel 5300 network card has problems such as center frequency offset, sampling frequency offset, and message detection delay when collecting Wi-Fi signals. However, these hardware noises are constant across different antennas of the same device. The dynamic signal CSI ratio method is used to effectively eliminate the impact of these hardware noises. The CSI ratio method formula is:

[0093]

[0094] Among them, CSI afteratio (t) represents the CSI data after CSI ratio is calculated. ant1 (t) represents the CSI data of the first antenna, CSI ant2 (t) represents the CSI data of the second antenna.

[0095] After the above steps, the impact of hardware noise can be effectively reduced, the signal quality can be improved, and a data basis can be provided for subsequent operations.

[0096] (3) Using a Gaussian filter to perform Gaussian noise suppression on the signal after hardware noise suppression;

[0097] In this embodiment, a Gaussian filter is used to perform an additive Gaussian noise removal operation on the signal after hardware noise suppression to obtain a Gaussian noise suppressed signal.

[0098] (4) The signal after Gaussian noise suppression is processed through adaptive filtering to obtain the Wi-Fi dynamic signal caused by the moving target.

[0099] In this embodiment, the influence of discontinuity or jitter caused by missing data on subsequent signal processing operations is eliminated, and the signal after Gaussian noise suppression is linearly interpolated to obtain a continuous suppressed signal.

[0100] To prevent phase distortion, linear phase techniques are used to keep all frequency components of the continuously suppressed signal with the same time delay.

[0101] In order to obtain a cleaner spectrum with the dynamic characteristics of the target object, a Butterworth bandpass filter is used to remove irrelevant static frequency components and suppress low-frequency drift and high-frequency noise.

[0102] In order to obtain cleaner frequency features at multiple frequency scales, wavelet transform filtering is used to perform fine-grained dynamic signal extraction in each frequency band to obtain the Wi-Fi dynamic signal caused by moving targets.

[0103] After the above processing, the Wi-Fi dynamic signal caused by the moving target is expressed as follows:

[0104]

[0105] Where i represents the number of subcarriers, k represents the number of data packets, and f i represents the frequency of the ith subcarrier, c is the speed of light, Δt is the sampling time interval, v is the moving speed of the target object, β i,k is the amplitude response.

[0106] like Figure 2 As shown, in step S2, an automatic gain control value is extracted based on the Wi-Fi dynamic signal caused by the moving target; and a shadow fading parameter is calculated according to the automatic gain control value.

[0107] S2-1. Extract the automatic gain control value.

[0108] In this embodiment, the automatic gain control (AGC) value is extracted from the dynamic Wi-Fi signal associated with a mobile target. Specifically, the AGC value is parsed using a Python or MATLAB script in the Channel State Information Tool (CSI Tool). The AGC value is an intrinsic parameter in the physical layer and is obtained using the parsing code provided by the CSI Tool.

[0109] S2-2. Calculate shadow fading parameters based on the automatic gain control value.

[0110] In this embodiment, the shadow fading parameter is calculated according to the automatic gain control value AGC, and the formula is:

[0111]

[0112] Among them, SFA(t) is the time corresponding window w d Standard deviation of internal AGC, S t+j is the AGC value at time t+j, S t+j is the AGC value at time t; SFP(t) is the shadow fading parameter.

[0113] In this embodiment, the threshold is set to 1. When SFA(t) is greater than the threshold, the shadow fading parameter SFP at that moment is equal to 1; when SFA(t) is less than the threshold, the shadow fading parameter SFP at that moment is equal to 0.

[0114] After the above steps, it is possible to effectively determine whether the target object enters the shadow fading area and achieve accurate detection, which provides a basis for subsequent velocity reconstruction and fusion.

[0115] like Figure 2 As shown, in step S3, based on the Wi-Fi dynamic signal caused by the moving target, the Doppler spectrum is extracted; the Doppler spectrum is divided into multiple scales, and the multi-scale velocity is calculated.

[0116] S3-1. Extract the Doppler spectrum based on the dynamic Wi-Fi signal caused by the moving target.

[0117] In this embodiment, the Doppler spectrum is extracted, and the specific process is as follows:

[0118] (1) The principal components of the Wi-Fi dynamic signals caused by moving targets are extracted through singular value decomposition technology.

[0119] In this embodiment, the singular value decomposition (SVD) technique is used to extract the principal components in the CSI data, retaining the main features of the signal while performing dimensionality reduction to reduce computational complexity and storage requirements.

[0120] (2) Use short-time Fourier transform to extract the Doppler spectrum from the principal component.

[0121] In this embodiment, the Doppler spectrum of the CSI data is calculated using the short-time Fourier transform (STFT) technique in the extracted principal component space.

[0122] In order to improve storage efficiency and reduce the amount of calculation, the Doppler spectrum is compressed and the multi-dimensional matrix is ​​simplified.

[0123] After the above steps, the system's robustness to interference signals in complex environments is significantly improved, while the amount of calculation is reduced and the calculation efficiency is improved.

[0124] S3-2. Perform multi-scale division on the Doppler spectrum and calculate the multi-scale velocity.

[0125] In this embodiment, in order to capture the signal propagation characteristics of a moving target in different frequency ranges, the Doppler spectrum is divided into multiple sub-ranges according to frequency. Different speeds are estimated and their motion characteristics at different scales are observed, further enhancing the sensitivity of spectral feature extraction and its adaptability to dynamic environments. The multi-scale velocity calculation formula is:

[0126]

[0127] Among them, f d represents the Doppler frequency, f t represents the signal frequency at time t, and c represents the speed of light.

[0128] In this embodiment, the frequency range is divided into three sub-ranges, and three scale velocities are calculated according to formula (5), namely: scale 1 velocity, scale 2 velocity, and scale 3 velocity.

[0129] like Figure 2As shown, in step S4, the shadow fading area is detected according to the shadow fading parameters; it is determined whether the moving target enters the shadow fading area. If it is in the shadow fading area, the attenuation speed is reconstructed to obtain the reconstructed scale speed. If not, no reconstruction is performed and the scale speed is not reconstructed.

[0130] S4-1. Use the shadow fading parameter SFP to detect whether there is a shadow fading area.

[0131] Calculate the number of SFP=1 in the sliding window. When the number of 1 in the window exceeds the threshold, it indicates that the shadow fading area has been entered. When the number of 1 in the window is less than the threshold, it indicates that the shadow fading area has been left. The calculation formula is:

[0132]

[0133] According to formula (6), the range of the shadow fading area is obtained.

[0134] In this embodiment, shadow attenuation is detected in the scale 1 and scale 2 velocities of the moving target, and the attenuated velocities of the scale 1 and scale 2 velocities are reconstructed to obtain a reconstructed scale velocity. If shadow attenuation is not detected in the scale 3 velocity of the moving target, the attenuated velocities are not reconstructed to obtain an unreconstructed scale velocity.

[0135] Through the above steps, it is possible to accurately locate the object entering the fading zone and accurately reconstruct the attenuation speed.

[0136] S4-2. Calculate the attenuation rate of the multi-scale velocity in the shadow fading region.

[0137] In this embodiment, for the linear motion of a moving target, when the target passes through a shadow fading zone, a model related to speed and diffraction path is derived based on the Fresnel zone diffraction perception model. The calculation formula is:

[0138]

[0139] Where λ represents the wavelength of the Wi-Fi signal, Δt represents the time it takes to pass through the shadow fading area, and s represents the diffraction path length.

[0140] The attenuation speeds of scale 1 and scale 2 with shadow attenuation are calculated according to formula (7).

[0141] S4-3. Reconstructing the decay velocity by folding the multi-scale velocity data in the shadow fading region

[0142] After the velocity attenuation of shadow fading is negatively correlated with the signal attenuation of the Fresnel zone diffraction perception model, the attenuation velocity is reconstructed by folding the velocity value in the shadow fading area. The reconstruction formula is:

[0143] v reconstruct =2*max(v SFP (t))-v SFP (t)(8)

[0144] Among them, v SFP (t) represents the velocity value at time t in the shadow fading area.

[0145] In this embodiment, according to formula (8), the attenuated velocities of scale 1 and scale 2, which are subject to shadow attenuation, are reconstructed by flipping the velocity values ​​within the shadow fading region to obtain reconstructed scale 1 and reconstructed scale 2 velocities. Scale 3, which is free of shadow attenuation, remains the scale 3 velocity.

[0146] In order to make the reconstructed velocity values ​​continuous, the boundary positions are smoothed using the weighted average method.

[0147] After the above steps, the velocity value is ensured to maintain continuity and smoothness in the reconstructed signal, thereby enhancing the stability of subsequent processing.

[0148] like Figure 2 As shown, in step S5, a speed fusion algorithm with dynamic weights is used to fuse the reconstructed scale data and the unreconstructed scale data to obtain the speed of the moving target.

[0149] In this embodiment, a velocity fusion algorithm with dynamic weights is used to fuse the reconstructed scale velocity and the unreconstructed scale velocity. The specific process is as follows:

[0150] (1) Calculate multi-scale velocity characteristic parameters.

[0151] In this embodiment, the scale velocity characteristic parameter C is calculated. v , the formula is:

[0152]

[0153] Where σ represents the standard deviation of the scale velocity, represents the mean of the scaled velocity.

[0154] The characteristic parameters of the reconstructed scale 1 velocity, the reconstructed scale 2 velocity and the scale 3 velocity are calculated respectively according to formula (9).

[0155] (2) Calculate the correlation between multi-scale velocities.

[0156] In this embodiment, the correlation between scales is calculated as follows:

[0157] CorrMatrix=[Corr(v1,v2),Corr(v1,v3),Corr(v2,v3)](10)

[0158] Among them, v1, v2 and v3 represent the reconstruction scale 1 speed, reconstruction scale 2 speed and scale 3 speed respectively.

[0159] (3) Based on the correlation calculation, the weights of the reconstructed scale velocity and the unreconstructed scale velocity are calculated respectively.

[0160] Calculate the weight ω for the speed in the shadow fading area detect , the formula is:

[0161] ω detect =1-C v (11)

[0162] Among them, C v Represents the scale velocity characteristic parameter.

[0163] In this embodiment, the weights of the scale 1 velocity and the scale 2 velocity in the shadow fading area are calculated using formula (11).

[0164] At the same time, for the speed without shadow fading, calculate its weight ω undetect , the formula is:

[0165]

[0166] In this embodiment, the weight of the scale 3 velocity in the absence of the shadow fading region is calculated using formula (12).

[0167] (4) According to the reconstructed multi-scale velocity and the corresponding weights, the reconstructed scale velocity and the unreconstructed scale velocity are fused to obtain the velocity of the moving target.

[0168] In this embodiment, the reconstructed multi-scale velocity and weight vectors are respectively expressed as:

[0169]

[0170] According to the multi-scale velocity and weight vector, the reconstructed scale velocity and the unreconstructed velocity are fused. The formula is:

[0171] v estimate =ω T v(14)

[0172] Among them, v represents the reconstructed multi-scale velocity vector, and ω represents the corresponding weight vector.

[0173] Through these steps, multi-scale data is integrated to reduce the impact of single-scale errors on the overall estimate, improving the accuracy of velocity estimation. Dynamically adjusting weight parameters suppresses the interference of abnormal scales on the overall velocity estimate, improving the system's adaptability to complex environments.

[0174] In this embodiment, key technologies such as dynamic signal extraction, multi-scale spectrum analysis, shadow fading parameter estimation, and multi-scale velocity fusion are used to accurately measure the velocity of moving targets. This method effectively addresses noise interference and signal attenuation in complex environments and is suitable for measuring the velocity of a variety of targets.

[0175] Example 2

[0176] The purpose of this embodiment is to provide a moving target speed measurement system based on Wi-Fi signals, including:

[0177] Data acquisition module, used to obtain Wi-Fi dynamic signals caused by moving targets;

[0178] A shadow fading parameter estimation module is used to extract an automatic gain control value based on the Wi-Fi dynamic signal caused by the moving target; and calculate the shadow fading parameter based on the automatic gain control value;

[0179] The multi-scale spectrum analysis module is used to extract the Doppler spectrum based on the dynamic Wi-Fi signal caused by the moving target; divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity;

[0180] The shadow fading area detection module is used to detect the shadow fading area according to the shadow fading parameters; determine whether the moving target enters the shadow fading area. If it is in the shadow fading area, the attenuation velocity is reconstructed to obtain the reconstructed scale velocity; if not, no reconstruction is performed and the scale velocity is obtained;

[0181] The moving target speed measurement module is used to fuse the reconstructed scale data and the unreconstructed scale data using a dynamic weighted speed fusion algorithm to obtain the speed of the moving target.

[0182] Based on providing a moving target speed measurement system based on Wi-Fi signals, the method steps in embodiment 1 are implemented.

[0183] The data acquisition module includes a transmitting end and a receiving end, which are used for sending and receiving signals.

[0184] Both the transmitter and receiver are equipped with an acquisition system platform based on the channel state information tool. The acquisition system platform is set to Monitor mode. In Monitor mode, the signal transmission rate of the transmitter device is set to 1000 Hz. The transmitter device can stably send data packets at a high transmission rate. Python and Matlab are used to extract and process the Wi-Fi CSI data in the received data packets.

[0185] The steps involved in the apparatus of the above embodiment correspond to those of the method embodiment 1. For detailed implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0186] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0187] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for measuring the speed of a moving target based on Wi-Fi signals, characterized in that: include: Acquire dynamic Wi-Fi signals caused by moving targets; Extract automatic gain control values ​​based on the dynamic Wi-Fi signal caused by moving targets; Calculate shadow fading parameters according to the automatic gain control value; Extract Doppler spectrum based on the dynamic Wi-Fi signal caused by moving targets; Perform multi-scale division on the Doppler spectrum and calculate multi-scale velocity; Detect shadow fading area according to shadow fading parameters; Determine whether the moving target enters the shadow fading area. If it is in the shadow fading area, reconstruct the attenuation velocity to obtain the reconstructed scale velocity. If not, no reconstruction is performed and the unreconstructed scale velocity is obtained. The velocity fusion algorithm with dynamic weights is used to fuse the reconstructed scale data and the unreconstructed scale data to obtain the velocity of the moving target.

2. The method for measuring the speed of a moving target based on Wi-Fi signals according to claim 1, wherein: The specific process of obtaining the dynamic Wi-Fi signal caused by the moving target is as follows: Calculate the subcarrier performance parameters in the Wi-Fi dynamic signal and extract the subcarriers with the largest dynamic response based on the subcarrier performance parameters; The dynamic signal ratio method is used to perform hardware noise suppression on subcarriers with large dynamic response; Use Gaussian filter to perform Gaussian noise suppression on the signal after hardware noise suppression; The signal after Gaussian noise suppression is processed by adaptive filtering to obtain the Wi-Fi dynamic signal caused by the moving target.

3. The method for measuring the speed of a moving target based on Wi-Fi signals according to claim 2, wherein: The signal after Gaussian noise suppression is processed by adaptive filtering. The specific process is as follows: Perform linear interpolation on the signal after Gaussian noise suppression to obtain a continuous suppressed signal; Utilize linear phase technology to keep all frequency components of the continuous suppression signal with the same time delay; Use Butterworth bandpass filter to remove irrelevant static frequency components and suppress low-frequency drift and high-frequency noise; Wavelet transform filtering is used to perform fine-grained dynamic signal extraction in each frequency band to obtain the Wi-Fi dynamic signal caused by the moving target.

4. The method for measuring the speed of a moving target based on Wi-Fi signals according to claim 1, wherein: Extract the automatic gain control value by using the Python or MATLAB script of the Channel State Information tool to parse the automatic gain control value.

5. The method for measuring the speed of a moving target based on Wi-Fi signals according to claim 1, wherein: Extract the Doppler spectrum. The specific process is: Extract the principal components from the Wi-Fi dynamic signal caused by the moving target through the singular value decomposition technique; The Doppler spectrum is extracted from the principal components using short-time Fourier transform.

6. The method for measuring the speed of a moving target based on Wi-Fi signals according to claim 1, wherein: Determine whether there is shadow attenuation in the multi-scale velocity. If there is shadow attenuation, reconstruct the attenuation velocity. The specific process is: Calculate the decay rate of multi-scale velocities in the presence of shadow decay; The decay velocity is reconstructed by folding the multi-scale velocity data with shadow attenuation.

7. The method for measuring the speed of a moving target based on Wi-Fi signals according to claim 1, wherein: The velocity fusion algorithm with dynamic weights is used to fuse the reconstructed scale velocity and the unreconstructed scale velocity to obtain the velocity of the moving target. The specific process is as follows: Calculate multi-scale velocity characteristic parameters; Calculate the correlation between scales of multi-scale velocities; Based on the correlation calculation, the weights of the reconstructed scale velocity and the unreconstructed scale velocity are calculated respectively; According to the reconstructed multi-scale velocity and the corresponding weights, the reconstructed scale velocity and the unreconstructed scale velocity are fused to obtain the velocity of the moving target.

8. A moving target speed measurement system based on Wi-Fi signals, characterized in that: include: Data acquisition module, used to obtain Wi-Fi dynamic signals caused by moving targets; A shadow fading parameter estimation module is used to extract automatic gain control values ​​based on the Wi-Fi dynamic signal caused by the moving target; and calculate shadow fading parameters based on the automatic gain control values; The multi-scale spectrum analysis module is used to extract the Doppler spectrum based on the dynamic Wi-Fi signal caused by the moving target; divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity; The shadow fading area detection module is used to detect the shadow fading area according to the shadow fading parameters; determine whether the moving target enters the shadow fading area. If it is in the shadow fading area, the attenuation velocity is reconstructed to obtain the reconstructed scale velocity; if not, no reconstruction is performed and the scale velocity is obtained; The moving target speed measurement module is used to fuse the reconstructed scale data and the unreconstructed scale data using a dynamic weighted speed fusion algorithm to obtain the speed of the moving target.

9. The mobile target speed measurement system based on Wi-Fi signals according to claim 8, characterized in that: The data acquisition module includes a transmitting end and a receiving end, which are used for sending and receiving signals.

10. The mobile target speed measurement system based on Wi-Fi signals according to claim 8, characterized in that: Both the transmitting end and the receiving end are provided with a collection system platform based on a channel state information tool, and the collection system platform is set to Monitor mode.

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