Wireless Sensing Methods and Systems Based on GNSS Signals

Abstract

This invention relates to the field of wireless sensing technology and discloses a wireless sensing method and system based on GNSS signals. The method includes reconstructing the amplitude and phase of a GNSS signal from raw GNSS measurements to obtain reconstructed amplitude and phase; analyzing the reconstructed amplitude and phase based on a diffraction and reflection sensing model to obtain diffraction samples and reflection samples; comparing the diffraction samples and reflection samples with reference signal samples based on a dynamic time warping algorithm to obtain dynamic time warping calculation results; and determining the action recognition result of the target object based on the dynamic time warping calculation results. This invention utilizes raw GNSS measurements to reconstruct amplitude and phase, and achieves human action recognition through multi-GNSS signal fusion based on a diffraction and reflection sensing model and a dynamic time warping algorithm. Using GNSS signals for wireless sensing improves the coverage of wireless sensing and eliminates the need for additional transmitter deployment, making it suitable for applications in various outdoor environments.

Description

Technical Field

[0001] This invention relates to the field of wireless sensing technology, and in particular to a wireless sensing method and system based on GNSS signals. Background Technology

[0002] Wireless sensing technology, with its advantages of being contactless, low-cost, and privacy-preserving, shows broad application prospects in fields such as human-computer interaction and environmental monitoring. Various wireless signals, such as Wi-Fi, LoRa, and UWB, are widely used in scenarios such as human motion recognition and environmental information monitoring. Currently, wireless sensing solutions have limited coverage, typically requiring dense deployment of signal transmitters and stable power access, resulting in high maintenance costs and making large-scale deployment in outdoor areas difficult. Summary of the Invention

[0003] The main objective of this invention is to provide a wireless sensing method and system based on GNSS signals, aiming to solve at least one of the aforementioned technical problems.

[0004] To achieve the above objectives, the present invention provides a wireless sensing method based on GNSS signals, comprising:

[0005] The amplitude and phase of the GNSS signal are reconstructed based on the original GNSS measurements of the target object, thus obtaining the reconstructed amplitude and phase.

[0006] Based on the diffraction and reflection sensing model, the reconstructed amplitude and reconstructed phase are analyzed to obtain diffraction samples and reflection samples;

[0007] The diffraction and reflection samples are compared with the reference signal sample based on the dynamic time warping algorithm to obtain the dynamic time warping calculation result.

[0008] The action recognition result of the target object is determined based on the dynamic time warping calculation result.

[0009] In some embodiments, the step of reconstructing the amplitude and phase of the GNSS signal based on the original GNSS measurements of the target object to obtain the reconstructed amplitude and phase includes:

[0010] Obtain the raw GNSS measurements of the target object reported by the GNSS module; wherein the raw GNSS measurements include the carrier noise power density ratio and the cumulative carrier phase measurement.

[0011] The amplitude of the GNSS signal is reconstructed based on the carrier noise power density ratio and the signal amplitude reconstruction formula, and the reconstructed amplitude is obtained.

[0012] The phase of the GNSS signal is reconstructed based on the accumulated carrier phase measurement and the phase change formula, thus obtaining the reconstructed phase.

[0013] In some embodiments, the signal amplitude reconstruction formula is:

[0014]

[0015] Where Amplitude is the reconstruction amplitude; C / N0 is the carrier noise power density ratio.

[0016] In some embodiments, reconstructing the phase of the GNSS signal based on the accumulated carrier phase measurement and the phase change formula to obtain the reconstructed phase includes:

[0017] The satellite's velocity and the unit direction vector between the satellite and the receiver are calculated based on the satellite ephemeris information.

[0018] The phase influence parameters of the satellite motion on the GNSS signal are calculated based on the satellite's velocity and the unit direction vector.

[0019] The clock error of the receiver is obtained based on the least squares positioning algorithm and the pseudorange in the raw GNSS measurements;

[0020] The phase of the GNSS signal is reconstructed based on the cumulative carrier phase measurement, the phase influence parameter, the receiver clock error, and the phase change formula, thus obtaining the reconstructed phase.

[0021] In some embodiments, the phase change formula is:

[0022] φ tar (k)=φ(k)-φ(k-1)+φ d (k)-c(t b (k)-t b (k-1))

[0023] Among them, φ tar (k) represents the reconstructed phase; φ represents the cumulative carrier phase measurement; φ d t represents the phase effect parameter of satellite motion on GNSS signals; c represents the speed of light; t represents the phase effect parameter of satellite motion on GNSS signals. b This represents the receiver's clock error.

[0024] In some embodiments, the step of analyzing the reconstructed amplitude and the reconstructed phase based on the diffraction and reflection sensing model to obtain diffraction samples and reflection samples includes:

[0025] Construct a diffraction and reflection sensing model for GNSS signals;

[0026] The GNSS signal is determined by the satellite's azimuth angle to determine whether it has undergone diffraction or reflection.

[0027] If the GNSS signal is a signal that has undergone reflection, then the reconstructed amplitude and reconstructed phase are analyzed based on the diffraction and reflection sensing model to obtain the reflection sample;

[0028] If the GNSS signal is a signal that has undergone diffraction, then the reconstructed amplitude and reconstructed phase are analyzed based on the diffraction and reflection sensing model to obtain diffraction samples.

[0029] In some embodiments, the comparison of the diffraction samples and reflection samples with the reference signal samples based on the dynamic time warping algorithm to obtain the dynamic time warping calculation result includes:

[0030] The azimuth and elevation information reported by the GNSS module is used to determine the sky distribution map of the satellites, and the sky distribution map is divided into multiple sectors;

[0031] The signal with the largest amplitude variation value of the satellite in each sector is selected as the sector signal sample;

[0032] The diffraction and reflection samples of each sector signal sample are compared with the reference signal sample based on the dynamic time warping algorithm to obtain the calculation results for each sector.

[0033] The calculation results of the sectors are weighted and summed to obtain the dynamic time warping calculation results.

[0034] In some embodiments, the dynamic time warping algorithm compares the diffraction and reflection samples of each sector signal sample with a reference signal sample to obtain the calculation result for each sector, including:

[0035] Based on the diffraction and reflection sensing model, the diffraction amplitude change value and the reflection amplitude change value of each sector signal sample are obtained by analyzing the diffraction sample and the reflection sample.

[0036] The dynamic time warping algorithm is used to obtain the calculation result of each sector based on the signal sample of each sector, the diffraction amplitude change value, the reflection amplitude change value, and the reference signal sample; wherein, the calculation result is the action most similar to the reference signal sample.

[0037] Furthermore, to achieve the above objectives, the present invention also proposes a wireless sensing system based on GNSS signals, comprising:

[0038] The signal reconstruction module is used to reconstruct the amplitude and phase of the GNSS signal based on the original GNSS measurements of the target object, and obtain the reconstructed amplitude and phase.

[0039] The perception analysis module is used to analyze the reconstructed amplitude and the reconstructed phase based on the diffraction and reflection perception model to obtain diffraction samples and reflection samples.

[0040] The action recognition module is used to compare the diffraction samples and reflection samples with the reference signal samples based on the dynamic time warping algorithm to obtain the dynamic time warping calculation results.

[0041] The result output module is used to determine the action recognition result of the target object based on the dynamic time warping calculation result.

[0042] In some embodiments, the signal reconstruction module is specifically used to: acquire the raw GNSS measurements of the target object reported by the GNSS module; wherein the raw GNSS measurements include the carrier-to-noise power density ratio and the cumulative carrier phase measurement;

[0043] The amplitude of the GNSS signal is reconstructed based on the carrier noise power density ratio and the signal amplitude reconstruction formula, and the reconstructed amplitude is obtained.

[0044] The phase of the GNSS signal is reconstructed based on the accumulated carrier phase measurement and the phase change formula, thus obtaining the reconstructed phase.

[0045] This invention provides a wireless sensing method based on GNSS signals, comprising: reconstructing the amplitude and phase of the GNSS signal based on the raw GNSS measurements of the target object, obtaining reconstructed amplitude and phase; analyzing the reconstructed amplitude and phase based on a diffraction and reflection sensing model, obtaining diffraction samples and reflection samples; comparing the diffraction samples and reflection samples with reference signal samples based on a dynamic time warping algorithm, obtaining a dynamic time warping calculation result; and determining the action recognition result of the target object based on the dynamic time warping calculation result. In this invention, the amplitude and phase of the raw GNSS measurements are used, and the relationship between signal change characteristics and human actions is further analyzed based on a diffraction and reflection sensing model. The collected samples are compared with reference signal samples using a dynamic time warping algorithm to achieve multi-GNSS signal fusion for human action recognition. This invention utilizes GNSS signals for wireless sensing, effectively improving the wireless sensing coverage. Furthermore, since GNSS signals are always present, no additional transmitter deployment is required, which is beneficial for deployment and application in various outdoor environments, enabling fine-grained human action recognition. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating an embodiment of the wireless sensing method based on GNSS signals according to the present invention.

[0047] Figure 2 This is a block diagram illustrating the technical implementation of the embodiments of the present invention;

[0048] Figure 3 This is a schematic diagram of the phase reconstruction process in the signal reconstruction algorithm involved in the embodiment of the present invention;

[0049] Figure 4 This is a schematic diagram of the reflection model of the GNSS signal involved in the embodiment of the present invention;

[0050] Figure 5 This is a schematic diagram of the diffraction model of the GNSS signal involved in the embodiment of the present invention;

[0051] Figure 6 This is a flowchart of the human motion recognition process involving multi-GNSS signal fusion in an embodiment of the present invention;

[0052] Figure 7 This is a schematic diagram of the satellite distribution sectors involved in the embodiments of the present invention;

[0053] Figure 8 This is a structural block diagram of an embodiment of the wireless sensing system based on GNSS signals of the present invention;

[0054] Figure 9 This is a schematic diagram of the structure of an electronic device in the hardware operating environment involved in the embodiments of the present invention.

[0055] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0057] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0058] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention.

[0059] GNSS (Global Navigation Satellite System) signals cover approximately 95% of the Earth's surface. GNSS-based wireless sensing systems can utilize the ever-present GNSS signals for wireless sensing. Unlike current wireless sensing systems that typically require fixed transceiver locations, the GNSS-based wireless sensing system described in this application does not require a signal transmitter and can simultaneously receive multiple GNSS signals. Since the incident angle of GNSS signals changes rapidly with satellite movement, this application's GNSS-based wireless sensing method considers the satellite's incident angle and rationally designs a multi-GNSS signal fusion sensing algorithm to improve the performance of the wireless sensing system.

[0060] In view of this, the present invention proposes a wireless sensing method and system based on GNSS signals.

[0061] This invention provides a wireless sensing method based on GNSS signals, referring to... Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the wireless sensing method based on GNSS signals according to the present invention.

[0062] like Figure 1 As shown, the wireless sensing method based on GNSS signals includes:

[0063] Step S100: Reconstruct the amplitude and phase of the GNSS signal based on the original GNSS measurements of the target object to obtain the reconstructed amplitude and phase;

[0064] Step S200: Analyze the reconstructed amplitude and the reconstructed phase based on the diffraction and reflection sensing model to obtain diffraction samples and reflection samples;

[0065] Step S300: Based on the dynamic time warping algorithm, the diffraction sample and reflection sample are compared with the reference signal sample to obtain the dynamic time warping calculation result;

[0066] Step S400: Determine the action recognition result of the target object based on the dynamic time warping calculation result.

[0067] It should be noted that the execution subject in this embodiment can be an electronic device, which can be a computer device with data processing function, or other devices that can achieve the same or similar functions. This embodiment does not limit this. In this embodiment, a computer device is used as an example for explanation.

[0068] Understandably, in combination Figure 1 and Figure 2 This embodiment proposes a wireless sensing method based on GNSS signals. The method uses MATLAB to process raw GNSS measurements, such as cumulative carrier phase and carrier noise power density ratio, reported by the GNSS module. The signal amplitude and phase are reconstructed using a signal reconstruction algorithm. Based on the reconstructed phase and amplitude, a sensing model based on diffraction and reflection is established. The relationship between signal change characteristics and human actions is further analyzed. Dynamic Time Warping (DTW) is used to compare the collected signal change characteristics with the reference signal characteristics corresponding to the actions. The comparison results are then weighted and summed based on the satellite's azimuth and elevation signals, thereby achieving fine-grained human action recognition. The specific process of the wireless sensing method based on GNSS signals is described in detail below with reference to the accompanying drawings.

[0069] In one embodiment, reconstructing the amplitude and phase of a GNSS signal based on the raw GNSS measurements of the target object to obtain the reconstructed amplitude and phase includes: acquiring the raw GNSS measurements of the target object reported by the GNSS module; wherein the raw GNSS measurements include the carrier noise power density ratio and the cumulative carrier phase measurement; reconstructing the amplitude of the GNSS signal according to the carrier noise power density ratio and the signal amplitude reconstruction formula to obtain the reconstructed amplitude; and reconstructing the phase of the GNSS signal according to the cumulative carrier phase measurement and the phase change formula to obtain the reconstructed phase.

[0070] It should be noted that in this embodiment, the amplitude and phase of the GNSS signal are reconstructed based on the raw GNSS measurements reported by the GNSS module.

[0071] Specifically, the reconstruction amplitude: The signal amplitude can be deduced from the formula for calculating the signal-carrier-noise power density ratio. The formula for calculating the signal-carrier-noise power density ratio is:

[0072]

[0073] Among them, P n P is the noise reference power. cLet B be the carrier power of the GNSS signal, C / N0 be the signal bandwidth, and C / N0 be the carrier-to-noise power density ratio, which can be obtained directly from the raw GNSS measurements. Since sensing only requires phase amplitude changes and not absolute amplitude values, the noise reference power P... n Since the signal bandwidth B can be considered as 1, the carrier power P of the GNSS signal can be derived from the above formula for calculating the signal-carrier-noise power density ratio. c The calculation formula for signal amplitude reconstruction can be derived from the fact that signal power equals the square of amplitude. The signal amplitude reconstruction formula is as follows:

[0074]

[0075] Where Amplitude is the reconstruction amplitude; C / N0 is the carrier noise power density ratio.

[0076] In one embodiment, reconstructing the phase of the GNSS signal based on the cumulative carrier phase measurement and the phase change formula to obtain the reconstructed phase includes: calculating the satellite's motion velocity and the unit direction vector between the satellite and the receiver based on satellite ephemeris information; calculating the phase influence parameters of the satellite motion on the GNSS signal based on the satellite's motion velocity and the unit direction vector; obtaining the receiver's clock error based on the least squares positioning algorithm and the pseudorange in the original GNSS measurement; and reconstructing the phase of the GNSS signal based on the cumulative carrier phase measurement, the phase influence parameters, the receiver's clock error, and the phase change formula to obtain the reconstructed phase.

[0077] The phase change formula is as follows:

[0078] φ tar (k)=φ(k)-φ(k-1)+φ d (k)-c(t b (k)-t b (k-1))

[0079] Among them, φ tar (k) represents the reconstructed phase; φ represents the cumulative carrier phase measurement; φ d t represents the phase effect parameter of satellite motion on GNSS signals; c represents the speed of light; t represents the phase effect parameter of satellite motion on GNSS signals. b This represents the receiver's clock error.

[0080] Specifically, such as Figure 3 As shown, the phase reconstruction: the effect of satellite motion on the phase of GNSS signals can be expressed by the following formula:

[0081] φ d =v s ·e n

[0082] Among them, v s e represents the satellite's velocity. n This represents the unit direction vector between the satellite and the receiver. It should be noted that the satellite's velocity and unit direction vector can be calculated from satellite ephemeris information. Satellite ephemeris information can accurately calculate, predict, depict, and track the time, position, velocity, and other operational states of flying objects (such as satellites), expressing the precise parameters of celestial bodies, satellites, spacecraft, missiles, space debris, and other flying objects. It places the flying object in three-dimensional space, using time to depict the past, present, and future of the celestial body. The time of the satellite ephemeris can be calculated according to Coordinated Universal Time (UTC).

[0083] The cumulative phase measurements φ(k-1) and φ(k) at times k-1 and k can be directly obtained from the raw GNSS measurements. Therefore, the phase change caused by the motion of the target object can be expressed as:

[0084] φ tar (k)=φ(k)-φ(k-1)+φ d (k)-c(t b (k)-t b (k-1))

[0085] Where c represents the speed of light, t b The receiver clock error can be obtained from the pseudorange in the raw GNSS measurements using the least squares positioning algorithm.

[0086] In one embodiment, analyzing the reconstructed amplitude and reconstructed phase based on a diffraction and reflection sensing model to obtain diffraction samples and reflection samples includes: constructing a diffraction and reflection sensing model of the GNSS signal; determining whether the GNSS signal undergoes diffraction or reflection based on the satellite's azimuth angle; if the GNSS signal undergoes reflection, analyzing the reconstructed amplitude and reconstructed phase based on the diffraction and reflection sensing model to obtain reflection samples; if the GNSS signal undergoes diffraction, analyzing the reconstructed amplitude and reconstructed phase based on the diffraction and reflection sensing model to obtain diffraction samples.

[0087] Understandably, due to the different azimuth and elevation angles of different satellites, their corresponding satellite signals produce varying degrees of diffraction and reflection effects on the human body surface. Constructing a diffraction and reflection perception model for GNSS signals can include both a reflection model and a diffraction model.

[0088] Specifically, reflection model: modeling the reflection of moving objects, such as... Figure 4As shown, the target object (target device) moves forward and backward along the direct path between the satellite and the receiver (e.g., a GNSS receiver). The receiver captures signals from the target object's direct path and reflections. Figure 4 As shown, the horizontal distance d from the GNSS receiver to the target object can be expressed as:

[0089] d=||(xp)|| cos θ

[0090] Where x and p are the positions of the GNSS receiver and the target object, respectively, and e n Let θ be the direction vector between the satellite and the receiver, and θ be the satellite's elevation angle. Then, the phase difference between the direct signal and the reflected signal from the satellite can be expressed as:

[0091]

[0092] In this equation, the satellite's elevation angle θ is approximately constant over a short period. The phase difference between the reflected and direct signals is linearly related to the horizontal distance d, and λ represents the carrier wavelength (λ can be a constant). Due to the combination of coherent and destructive phases between the reflected and direct signals, the received signal intensity will exhibit periodic peaks and troughs as the object moves. From the above equation, we know that the distance between the object positions corresponding to two peaks is λ / (2cosθ). By measuring the number of observed peaks, the distance the target has moved can be inferred.

[0093] It should be noted that the signal received by the receiver includes both reflected and direct signals. However, because GNSS reflected signals are relatively weak compared to other wireless signals such as Wi-Fi (e.g., -120 dBm), they can only be detected when reflected from larger parts of the body (e.g., the torso). Hand reflections are very weak and difficult to detect. To detect arms and legs, signals from other satellites affected by diffraction effects can be used. For diffraction effects, geometric diffraction theory is used to analyze the relationship between signal changes and human movements.

[0094] Specifically, the diffraction model states that diffraction dominates when a human target is very close or in the direct path of a GNSS signal. When a GNSS signal is blocked by a target object, according to Keller's geometric diffraction theory, the GNSS signal will diffract in a cone shape, known as the Keller cone. Figure 5As shown, consider a scenario where a target moves within a direct GNSS signal between a GNSS satellite and a GNSS receiver. The GNSS signal diffracts at the edge of the moving target. When the object's edge reaches the direct GNSS path, a Keller's cone is generated due to diffraction. At this point, the GNSS receiver receives a combination of the diffracted signal and the direct GNSS signal. Since the direct GNSS signal still dominates, the combined signal strength changes very little. As the target moves further and the direct GNSS path is blocked, the received signal strength drops rapidly. At this point, the direct GNSS signal is blocked, the diffracted signal dominates, and the signal strength fluctuates regularly. The GNSS signal includes, but is not limited to, GPS signals.

[0095] like Figure 5 As shown in the signal variation pattern, the signal strength fluctuation pattern differs depending on the location of the GNSS receiver. (Reference) Figure 5 When the GNSS receiver is deployed at position p1, two minimum values ​​appear on the signal variation pattern diagram as the object moves and blocks the direct path of the GNSS. When the receiver is at position p2, only one minimum value appears. Based on the signal fluctuation pattern, the relative position between the object and the receiver can be inferred.

[0096] As can be understood from the diffraction and reflection models described above, changes in GNSS signals are closely related to the satellite's position. When the satellite and target are on the same side relative to the target, the corresponding signal depends on diffraction; when the satellite and target are on opposite sides relative to the receiver, the corresponding signal change depends on reflection. Therefore, the azimuth angle of the satellite can be used to determine whether the signal has undergone diffraction or reflection.

[0097] Specifically, the GNSS signal is determined to be either diffraction-effect or reflection-effect-effect based on the satellite's azimuth angle. For signals that have undergone diffraction, sample signals (diffraction samples) are obtained through diffraction model analysis. A diffraction sample can be represented as... For a signal that has undergone a reflection effect, a sample signal (reflection sample) is obtained based on the reflection model analysis. The reflection sample can be represented as:

[0098] In one embodiment, the dynamic time warping algorithm is used to compare the diffraction and reflection samples with the reference signal sample to obtain the dynamic time warping calculation result. This includes: determining the satellite sky distribution map based on the azimuth and elevation information reported by the GNSS module, and dividing the sky distribution map into multiple sectors; selecting the signal with the largest amplitude change value of the satellite in each sector as the sector signal sample; comparing the diffraction and reflection samples of each sector signal sample with the reference signal sample based on the dynamic time warping algorithm to obtain the calculation result of each sector; and performing a weighted summation of the calculation results of the sectors to obtain the dynamic time warping calculation result.

[0099] In one embodiment, the diffraction and reflection samples of each sector signal sample are compared with a reference signal sample based on a dynamic time warping algorithm to obtain the calculation result for each sector. This includes: analyzing the diffraction and reflection samples of each sector signal sample based on a diffraction and reflection sensing model to obtain diffraction amplitude change values ​​and reflection amplitude change values; and obtaining the calculation result for each sector based on each sector signal sample, the diffraction amplitude change value, the reflection amplitude change value, and the reference signal sample using the dynamic time warping algorithm. The calculation result is the action most similar to the reference signal sample.

[0100] Specifically, the azimuth angle of the satellite can be used to determine whether the satellite signal has undergone diffraction or reflection. For a signal that has undergone diffraction, its sample signal can be represented as... Correspondingly, the signal sample that undergoes the reflection effect is The dynamic time warping algorithm is used to compare the current signal sample with the reference signal sample, and the action corresponding to the most similar reference signal sample is selected as the output result.

[0101]

[0102] Where N represents the number of types of actions, and This represents the diffraction and reflection samples used as references for the corresponding actions. and This represents the amplitude change value of the current signal sample based on the diffraction and reflection sensing model. The amplitude change value is used as a weight to multiply the corresponding DTW result; where, This represents the change in reflection amplitude based on the reflection model. This represents the diffraction amplitude variation value based on the diffraction model.

[0103] Specifically, such as Figure 6As shown, human motion recognition based on multi-GNSS signal fusion begins by determining the satellite sky distribution map based on the azimuth and elevation information reported by the GNSS module. Since satellite signal changes exhibit similar patterns within a certain azimuth and elevation range, the sky map can be subdivided into different sectors based on the azimuth and elevation ranges.

[0104] For example, such as Figure 7 As shown, the target is located due north (azimuth 0°), with the azimuth range set at 30° and the elevation range at 20°. After dividing the area into sectors, the signal with the largest amplitude variation in each sector is selected as the signal sample for that sector (sector signal sample). The diffraction and reflection characteristics are represented by x, respectively. D c j and x R c j The DTW calculation results of all sectors are weighted and summed, and the action corresponding to the most similar reference signal sample is selected as the output result:

[0105]

[0106] Where M represents the total number of sectors, and The weights of the diffracted and reflected samples of the signal in the j-th sector are represented by ||x D c j || 2 and ||x R c j || 2 This represents the amplitude change value of the corresponding sector signal sample based on the diffraction and reflection sensing model (diffraction model and reflection model), and the amplitude change value is used as a weight to multiply the corresponding DTW result.

[0107] This embodiment discloses a wireless sensing method based on GNSS signals, including reconstructing the amplitude and phase of the signal using raw GNSS measurements, analyzing the sensing model based on diffraction and reflection, and recognizing human actions by fusing multiple GNSS signals. Specifically, the amplitude and phase of the signal are reconstructed from raw GNSS measurements such as the cumulative carrier phase and carrier noise power density ratio reported by the GNSS module. Based on the reconstructed phase and amplitude, a sensing model based on diffraction and reflection is established to further analyze the relationship between signal change characteristics and human actions. The collected signal change characteristics are compared with the reference signal characteristics corresponding to the actions using a dynamic time warping algorithm, and the comparison results are weighted and summed according to the azimuth and elevation signals of the satellite, thereby achieving fine-grained human action recognition.

[0108] This embodiment provides a wireless sensing method based on GNSS signals, including: reconstructing the amplitude and phase of the GNSS signal based on the raw GNSS measurements of the target object, obtaining reconstructed amplitude and phase; analyzing the reconstructed amplitude and phase based on a diffraction and reflection sensing model, obtaining diffraction samples and reflection samples; comparing the diffraction samples and reflection samples with reference signal samples based on a dynamic time warping algorithm, obtaining a dynamic time warping calculation result; and determining the action recognition result of the target object based on the dynamic time warping calculation result. In this embodiment, the amplitude and phase of the raw GNSS measurements are used, and the relationship between signal change characteristics and human actions is further analyzed based on a diffraction and reflection sensing model. The collected samples are compared with reference signal samples using a dynamic time warping algorithm to achieve multi-GNSS signal fusion for human action recognition. This embodiment utilizes GNSS signals for wireless sensing, effectively improving the wireless sensing coverage. Furthermore, since GNSS signals are always present, no additional transmitter deployment is required, which is beneficial for deployment in various outdoor environments and achieving fine-grained human action recognition.

[0109] Furthermore, this embodiment of the invention also proposes a storage medium storing a wireless sensing program based on GNSS signals. When the wireless sensing program based on GNSS signals is executed by a processor, it implements the steps of the wireless sensing method based on GNSS signals as described above.

[0110] Reference Figure 8 , Figure 8 This is a structural block diagram of an embodiment of the wireless sensing system based on GNSS signals according to the present invention.

[0111] like Figure 8 As shown, a wireless sensing system based on GNSS signals includes:

[0112] The signal reconstruction module 10 is used to reconstruct the amplitude and phase of the GNSS signal based on the original GNSS measurement values ​​of the target object, and obtain the reconstructed amplitude and reconstructed phase.

[0113] The perception analysis module 20 is used to analyze the reconstructed amplitude and the reconstructed phase based on the diffraction and reflection perception model to obtain diffraction samples and reflection samples;

[0114] Action recognition module 30 is used to compare the diffraction sample and reflection sample with the reference signal sample based on the dynamic time warping algorithm to obtain the dynamic time warping calculation result;

[0115] The result output module 40 is used to determine the action recognition result of the target object based on the dynamic time warping calculation result.

[0116] In one embodiment, the signal reconstruction module 10 is specifically used to: acquire the raw GNSS measurement values ​​of the target object reported by the GNSS module; wherein the raw GNSS measurement values ​​include the carrier noise power density ratio and the cumulative carrier phase measurement value; reconstruct the amplitude of the GNSS signal according to the carrier noise power density ratio and the signal amplitude reconstruction formula to obtain the reconstructed amplitude; and reconstruct the phase of the GNSS signal according to the cumulative carrier phase measurement value and the phase change formula to obtain the reconstructed phase.

[0117] In one embodiment, the signal amplitude reconstruction formula is:

[0118]

[0119] Where Amplitude is the reconstruction amplitude; C / N0 is the carrier noise power density ratio.

[0120] In one embodiment, the signal reconstruction module 10 is specifically used for: calculating the satellite's motion velocity and the unit direction vector between the satellite and the receiver based on satellite ephemeris information; calculating the phase influence parameters of the satellite motion on the GNSS signal based on the satellite's motion velocity and the unit direction vector; obtaining the receiver's clock error based on the least squares positioning algorithm and the pseudorange in the original GNSS measurements; and reconstructing the phase of the GNSS signal based on the cumulative carrier phase measurements, the phase influence parameters, the receiver's clock error, and the phase change formula to obtain the reconstructed phase.

[0121] In one embodiment, the phase change formula is:

[0122] φ tar (k)=φ(k)-φ(k-1)+φ d (k)-c(t b (k)-t b (k-1))

[0123] Among them, φ tar (k) represents the reconstructed phase; φ represents the cumulative carrier phase measurement; φ d t represents the phase effect parameter of satellite motion on GNSS signals; c represents the speed of light; t represents the phase effect parameter of satellite motion on GNSS signals. b This represents the receiver's clock error.

[0124] In one embodiment, the sensing and analysis module 20 is specifically used for: constructing a diffraction and reflection sensing model of the GNSS signal; determining whether the GNSS signal is a signal subject to diffraction or reflection based on the azimuth angle of the satellite; if the GNSS signal is a signal subject to reflection, then analyzing the reconstructed amplitude and reconstructed phase based on the diffraction and reflection sensing model to obtain a reflection sample; if the GNSS signal is a signal subject to diffraction, then analyzing the reconstructed amplitude and reconstructed phase based on the diffraction and reflection sensing model to obtain a diffraction sample.

[0125] In one embodiment, the action recognition module 30 is specifically used for: determining the satellite sky distribution map based on the azimuth and elevation information reported by the GNSS module, and dividing the sky distribution map into multiple sectors; selecting the signal with the largest amplitude change value of the satellite in each sector as the sector signal sample; comparing the diffraction sample and reflection sample of each sector signal sample with the reference signal sample based on the dynamic time warping algorithm to obtain the calculation result of each sector; and performing a weighted summation of the calculation results of the sectors to obtain the dynamic time warping calculation result.

[0126] In one embodiment, the action recognition module 30 is specifically used to: analyze the diffraction and reflection samples of each sector signal sample based on the diffraction and reflection sensing model to obtain the diffraction amplitude change value and the reflection amplitude change value; and obtain the calculation result of each sector based on the dynamic time warping algorithm according to each sector signal sample, the diffraction amplitude change value, the reflection amplitude change value, and the reference signal sample; wherein, the calculation result is the action most similar to the reference signal sample.

[0127] This embodiment discloses a wireless sensing system based on GNSS signals, including reconstructing the amplitude and phase of the signal using raw GNSS measurements, analyzing the sensing model based on diffraction and reflection, and recognizing human actions by fusing multiple GNSS signals. Specifically, the system reconstructs the amplitude and phase of the signal from raw GNSS measurements such as the cumulative carrier phase and carrier noise power density ratio reported by the GNSS module. Based on the reconstructed phase and amplitude, the system establishes a sensing model based on diffraction and reflection to further analyze the relationship between signal change characteristics and human actions. The system compares the collected signal change characteristics with the reference signal characteristics corresponding to the actions using a Dynamic Time Warping (DTW) algorithm, and performs a weighted summation of the comparison results based on the azimuth and elevation signals of the satellite, thereby achieving fine-grained human action recognition.

[0128] In this embodiment, the amplitude and phase of raw GNSS measurements are used to further analyze the relationship between signal variation characteristics and human movements based on a diffraction and reflection sensing model. A dynamic time warping algorithm is used to compare the collected samples with reference signal samples, achieving multi-GNSS signal fusion for human movement recognition. This embodiment utilizes GNSS signals for wireless sensing, effectively improving the wireless sensing coverage. Furthermore, since GNSS signals are always present, no additional transmitters are required, facilitating deployment in various outdoor environments and enabling fine-grained human movement recognition.

[0129] It should be noted that technical details not described in detail in this embodiment of the GNSS signal-based wireless sensing system can be found in any embodiment of the present invention applied to the GNSS signal-based wireless sensing method described above, and will not be repeated here.

[0130] Furthermore, to achieve the above objectives, the present invention also proposes an electronic device comprising: a memory, a processor, and a GNSS signal-based wireless sensing program stored in the memory and executable on the processor, the GNSS signal-based wireless sensing program being configured to implement the GNSS signal-based wireless sensing method as described above.

[0131] Reference Figure 9 , Figure 9 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of the present invention.

[0132] like Figure 9 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0133] Those skilled in the art will understand that Figure 9 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0134] like Figure 9 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a wireless sensing program based on GNSS signals.

[0135] exist Figure 9 In the electronic device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device, and the electronic device calls the wireless sensing program based on GNSS signals stored in the memory 1005 through the processor 1001 and executes the wireless sensing method based on GNSS signals provided in the embodiment of the present invention.

[0136] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0137] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0138] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0139] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0140] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0141] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A wireless sensing method based on GNSS signals, characterized in that, include: The amplitude and phase of the GNSS signal are reconstructed based on the original GNSS measurements of the target object, thus obtaining the reconstructed amplitude and phase. Based on the diffraction and reflection sensing model, the reconstructed amplitude and reconstructed phase are analyzed to obtain diffraction samples and reflection samples; The diffraction and reflection samples are compared with the reference signal sample based on the dynamic time warping algorithm to obtain the dynamic time warping calculation result. The action recognition result of the target object is determined based on the dynamic time warping calculation result.

2. The method as described in claim 1, characterized in that, The process of reconstructing the amplitude and phase of the GNSS signal based on the original GNSS measurements of the target object, to obtain the reconstructed amplitude and phase, includes: Obtain the raw GNSS measurements of the target object reported by the GNSS module; wherein the raw GNSS measurements include the carrier noise power density ratio and the cumulative carrier phase measurement. The amplitude of the GNSS signal is reconstructed based on the carrier noise power density ratio and the signal amplitude reconstruction formula, and the reconstructed amplitude is obtained. The phase of the GNSS signal is reconstructed based on the accumulated carrier phase measurement and the phase change formula, thus obtaining the reconstructed phase.

3. The method as described in claim 2, characterized in that, The formula for reconstructing the signal amplitude is: Where Amplitude is the reconstruction amplitude; C / N0 is the carrier noise power density ratio.

4. The method as described in claim 2, characterized in that, The process of reconstructing the phase of the GNSS signal based on the accumulated carrier phase measurement and the phase change formula to obtain the reconstructed phase includes: The satellite's velocity and the unit direction vector between the satellite and the receiver are calculated based on the satellite ephemeris information. The phase influence parameters of the satellite motion on the GNSS signal are calculated based on the satellite's velocity and the unit direction vector. The clock error of the receiver is obtained based on the least squares positioning algorithm and the pseudorange in the raw GNSS measurements; The phase of the GNSS signal is reconstructed based on the cumulative carrier phase measurement, the phase influence parameter, the receiver clock error, and the phase change formula, thus obtaining the reconstructed phase.

5. The method as described in claim 4, characterized in that, The phase change formula is: f tar (k)=φ(k)-φ(k-1)+φ d (k)-c(t b (k)-t b (k-1)) Where, φ tar (k) represents the reconstructed phase at time k; c is the speed of light; φ(k) is the cumulative carrier phase measurement at time k; φ(k-1) is the cumulative carrier phase measurement at time k-1; φ d (k) represents the phase effect parameter of the satellite motion at time k on the GNSS signal; t b (k) represents the receiver clock error at time k; t b (k-1) represents the receiver clock error at time k-1.

6. The method according to any one of claims 1 to 5, characterized in that, The analysis of the reconstructed amplitude and reconstructed phase based on the diffraction and reflection sensing model yields diffraction samples and reflection samples, including: Construct a diffraction and reflection sensing model for GNSS signals; The GNSS signal is determined by the satellite's azimuth angle to determine whether it has undergone diffraction or reflection. If the GNSS signal is a signal that has undergone reflection, then the reconstructed amplitude and reconstructed phase are analyzed based on the diffraction and reflection sensing model to obtain the reflection sample; If the GNSS signal is a signal that has undergone diffraction, then the reconstructed amplitude and reconstructed phase are analyzed based on the diffraction and reflection sensing model to obtain diffraction samples.

7. The method according to any one of claims 1 to 5, characterized in that, The dynamic time warping algorithm compares the diffraction and reflection samples with the reference signal sample to obtain the dynamic time warping calculation result, including: The azimuth and elevation information reported by the GNSS module is used to determine the sky distribution map of the satellites, and the sky distribution map is divided into multiple sectors; The signal with the largest amplitude variation value of the satellite in each sector is selected as the sector signal sample; The diffraction and reflection samples of each sector signal sample are compared with the reference signal sample based on the dynamic time warping algorithm to obtain the calculation results for each sector. The calculation results of the sectors are weighted and summed to obtain the dynamic time warping calculation results.

8. The method as described in claim 7, characterized in that, The dynamic time warping algorithm compares the diffraction and reflection samples of each sector signal sample with the reference signal sample to obtain the calculation results for each sector, including: Based on the diffraction and reflection sensing model, the diffraction amplitude change value and the reflection amplitude change value of each sector signal sample are obtained by analyzing the diffraction sample and the reflection sample. The dynamic time warping algorithm is used to obtain the calculation result of each sector based on the signal sample of each sector, the diffraction amplitude change value, the reflection amplitude change value, and the reference signal sample; wherein, the calculation result is the action of the most similar reference signal sample.

9. A wireless sensing system based on GNSS signals, characterized in that, include: The signal reconstruction module is used to reconstruct the amplitude and phase of the GNSS signal based on the original GNSS measurements of the target object, and obtain the reconstructed amplitude and phase. The perception analysis module is used to analyze the reconstructed amplitude and the reconstructed phase based on the diffraction and reflection perception model to obtain diffraction samples and reflection samples. The action recognition module is used to compare the diffraction samples and reflection samples with the reference signal samples based on the dynamic time warping algorithm to obtain the dynamic time warping calculation results. The result output module is used to determine the action recognition result of the target object based on the dynamic time warping calculation result.

10. The system as described in claim 9, characterized in that, The signal reconstruction module is specifically used to: acquire the raw GNSS measurements of the target object reported by the GNSS module; wherein the raw GNSS measurements include the carrier-to-noise power density ratio and the cumulative carrier phase measurement; The amplitude of the GNSS signal is reconstructed based on the carrier noise power density ratio and the signal amplitude reconstruction formula, and the reconstructed amplitude is obtained. The phase of the GNSS signal is reconstructed based on the accumulated carrier phase measurement and the phase change formula, thus obtaining the reconstructed phase.

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