Wireless sensing method based on WiFi multi-carrier

Abstract

The invention discloses a wireless sensing method based on WiFi multiple carriers, and belongs to the field of wireless sensing. The method comprises the following steps: selecting three subcarriers with equal frequency difference from acquired CSI data, and dividing the first two subcarriers and the last two subcarriers into two pairs according to the sequence of the frequencies from small to large; calculating the ratio of the CSI of each pair of subcarriers; performing primary division operation on the two ratios, and calculating the ratio of the two ratios to obtain a multicarrier secondary ratio; and constructing a mapping relation between the multicarrier quadratic ratio and a reflection path length change caused by the target motion to obtain a human body motion sensing result. In order to solve the problems of amplitude hopping and random phase deviation in actual channel state information collected on a single-antenna and multi-antenna wireless sensing system with separated receiving and transmitting, fine-grained subcarrier information provided by a new-generation WiFi network card is used, and two-time division operation is carried out, so that the channel state information is obtained. And the influence of gain control and phase deviation is eliminated, so that ideal CSI is obtained for wireless sensing.

Description

Technical Field

[0001] The present invention provides a signal preprocessing method for a single-antenna and multi-antenna wireless sensing system based on channel state information (CSI) for separation of transceiver devices, which preprocesses actual CSI and obtains ideal CSI for wireless sensing. Specifically, it relates to a wireless sensing technology based on multi-carrier secondary ratio, which belongs to the field of ubiquitous computing and wireless sensing. Background Art

[0002] At present, most WiFi perception research mainly uses WiFi network cards that support the 802.11n protocol, such as the Intel 5300 network card and the Atheros AR9k series network card. With the upgrading of WiFi technology, the new generation of WiFi network cards that support 802.11ac / ax have begun to replace 802.11n network cards and become more and more common in life. These new generation WiFi network cards use wider bandwidths and more advanced technologies, such as multi-user multiple input multiple output (MU-MIMO) to provide faster data transmission speeds. However, these new communication characteristics require more fine-grained gain control, which has a negative impact on WiFi-based wireless perception. Gain control is a technology that adjusts the energy of WiFi signals. It can adjust the gain of WiFi signals according to the quality of the signal to improve the reliability of communication. However, fine-grained gain control can also cause random signal distortion of the actual CSI signal collected, which is very different from the ideal CSI. Therefore, some previous WiFi perception methods have degraded performance on these new generation WiFi network cards, and sometimes even fail to work. In order to eliminate the influence of gain control, a direct method is to turn off the automatic gain control (AGC), but this will inevitably affect the communication performance. In order to minimize the impact on communication performance, the best means is to process it through software methods after obtaining the actual CSI. However, the gain control strategies of different manufacturers are different, and the introduced noise performance will be different. In the absence of prior knowledge (such as noise distribution), it is difficult for traditional filters to completely eliminate these random noises. In particular, in order to support the technical features of new protocols such as MU-MIMO, each antenna on the new generation of network cards usually adopts independent gain control. There are also some transceivers that are only equipped with one antenna. This makes the signal preprocessing technology based on multiple antennas no longer applicable to the new generation of network cards. In addition, due to the asynchronous clocks between the transceivers, the actual CSI phase also contains random phase offsets, resulting in the phase of the actual CSI cannot be directly used for perception applications. Summary of the invention

[0003] In order to solve the above problems, the present invention aims to propose a signal preprocessing method based on WiFi multi-carrier, which eliminates the signal distortion caused by gain control and phase offset on the actual CSI in single-antenna and multi-antenna wireless sensing systems with separated transmission and reception, so as to obtain the ideal CSI for two typical wireless sensing applications.

[0004] The technical solution provided by the present invention is:

[0005] A wireless sensing method based on WiFi multi-carrier, the specific steps include:

[0006] S1. Collect CSI data of WiFi multi-carrier;

[0007] S2. Select three subcarriers with equal frequency difference from the collected CSI data, and divide the first two and the last two subcarriers into two pairs in the order of frequency from small to large; for each pair of subcarrier CSI, calculate their ratio; then divide the two ratios again, calculate the ratio of the two ratios, and obtain the multi-carrier secondary ratio;

[0008] S3. Construct a mapping relationship between the multi-carrier quadratic ratio and the change in reflection path length caused by target motion, that is, when the dynamic path length increases / decreases, the trajectory of the multi-carrier quadratic ratio on the complex plane rotates clockwise / counterclockwise;

[0009] S4. Obtain the human body movement status according to the change of the multi-carrier secondary ratio.

[0010] Furthermore, in step S1, one or two pairs of transceiver devices are arranged to support different wireless sensing applications, wherein respiratory monitoring requires a pair of transceiver devices, while trajectory tracking requires a transmitting device and two receiving devices. The distance between the two pairs of transceiver devices is equal and they are placed vertically.

[0011] Furthermore, for the application of breathing detection, all subcarriers are used to construct multiple multi-carrier quadratic ratio signals. The principal component analysis method is used to extract a one-dimensional breathing waveform from each multi-carrier quadratic ratio signal, and then the autocorrelation method is used to estimate the breathing frequency. The accuracy is improved by fusing the breathing frequencies estimated from multiple multi-carrier quadratic ratio signals.

[0012] Furthermore, for trajectory tracking applications, all subcarriers are used to construct multiple multi-carrier quadratic ratio signals, each multi-carrier quadratic ratio signal is converted to a time-frequency spectrum for analysis, and the frequency component with the largest energy is selected from the time-frequency spectrum as the Doppler shift to calculate the radial velocity of the target relative to a pair of transceivers. Accuracy is improved by fusing the radial velocities estimated from multiple multi-carrier quadratic ratio signals. Combined with the radial velocity information of the target relative to the two pairs of transceivers, the true speed of the target is obtained, including the speed and direction of movement.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] 1) The present invention utilizes the fine-grained subcarrier information provided by the new generation WiFi network card and eliminates the influence of gain control and phase offset through two division operations, so that the CSI of the new generation WiFi network card can be used to realize wireless sensing applications;

[0015] 2) The technical solution of the present invention does not need to shut down the AGC, nor does it need to synchronize the clock of the WiFi transceiver device, so it will not affect the communication function of the WiFi, and is compatible with the wireless sensing and communication functions;

[0016] 3) The technical solution of the present invention can simultaneously utilize the amplitude and phase information of the signal for wireless sensing, thereby improving the accuracy and reliability of wireless sensing;

[0017] 4) The technical solution of the present invention can be applied to a variety of wireless sensing applications, such as the perception of small-scale and large-scale motions, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 . Flow chart of the wireless sensing method based on WiFi multi-carrier of the present invention;

[0019] Figure 2 .Amplitude and phase of ideal CSI;

[0020] Figure 3 . The result before and after dividing two carriers, where (a) the amplitude of two subcarriers on one antenna, (b) the amplitude ratio of the two subcarriers;

[0021] Figure 4 Results before and after the multi-carrier double division, where (a) the phase difference between two subcarriers and (b) the phase of three subcarriers divided twice. DETAILED DESCRIPTION

[0022] The present invention proposes a signal preprocessing method based on WiFi multi-carrier. The present invention is further described below through embodiments in conjunction with the accompanying drawings, but the scope of the present invention is not limited in any way.

[0023] like Figure 1 As shown, in this embodiment, one to two pairs of transceiver devices are arranged to support different wireless sensing applications. Among them, respiratory monitoring requires a pair of transceiver devices. Trajectory tracking requires a transmitting device and two receiving devices. The distance between the two pairs of transceiver devices is equal and they are placed vertically. A laptop is used to receive and process CSI data.

[0024] The actual CSI received can be expressed mathematically as

[0025]

[0026] Where G(t) is the time-varying gain effect, θ o (f, t) is the time-varying phase offset. H(f, t) is the ideal CSI, which includes multiple static paths and dynamic paths caused by human motion. Multiple static paths can be superimposed into a constant H s (f) When the target is far away from the transceiver, the dynamic path caused by the target motion can be approximated as a path in breathing detection and trajectory tracking applications. The ideal CSI can be expressed as

[0027]

[0028] in a(f) is the signal strength of the dynamic path, d0 is the initial length of the dynamic path, and Δd(t) is the change in the dynamic path length over time. When the dynamic path length changes by one wavelength, the ideal CSI will rotate one circle, such as Figure 2 As shown, that is, it rotates 360 degrees in the complex plane. The amplitude and phase of the ideal CSI also change, showing peaks and troughs. However, the actual received CSI contains time-varying gain effects and phase offsets that mask the above phenomena. Fortunately, in order to transmit high bit rates on frequency selective fading channels, WiFi uses OFDM signal design. Each WiFi channel is divided into many subcarriers. For example, in 802.11ac, 20MHZ and 40MHZ channels are divided into 64 subchannels and 128 subchannels, called subcarriers. Although the gains vary over time, they are the same on different subcarriers at any specific timestamp. Figure 3 The CSI amplitudes of the two subcarriers are shown, and it can be seen that they have dramatic signal jumps over time.

[0029] The present invention eliminates the signal distortion introduced by the gain control after dividing the actual CSI of the two subcarriers. The ratio of the two subcarriers can be expressed as

[0030]

[0031] Although a single division operation can eliminate the signal distortion introduced by the gain control, the phase of the two subcarrier CSI ratios still contains non-negligible deviations. This is because although the two subcarrier CSIs share exactly the same antenna gain, the random phase offsets of different subcarriers are not exactly the same. Therefore, the phase offset cannot be completely eliminated by the signal division operation between the two subcarriers. Figure 4 (a) shows Figure 3The phase of the ratio of the two subcarrier signals contains the residual phase offset. Considering that the CSI phase offset is linearly related to the subcarrier frequency, the present invention introduces an additional subcarrier to deal with the residual phase offset. Specifically, the present invention selects a group of two pairs of subcarriers with equal subcarrier frequency intervals, and the residual phase offset in the CSI ratios of the two pairs of subcarriers is the same. Therefore, the present invention eliminates the residual phase offset by performing a division operation on the two ratios again. Compared with a single division, the present invention introduces a third subcarrier with a frequency of f+2Δf, which constitutes two pairs of subcarriers with the same frequency difference (the first pair: f and f+Δf, the second pair: f+Δf and f+2Δf). Then the multi-carrier secondary ratio can be defined as

[0032]

[0033] In formula (4), θ o (f,t)-θ o (f+Δf,t)=θ o (f+Δf,t)-θ o (f+2Δf,t). Figure 4 (b) It can be seen that the residual phase offset can be eliminated by two phase division operations. In order to further derive the relationship between the multi-carrier quadratic ratio and the change in the reflection path length caused by human body motion, the present invention now applies the following steps:

[0034] · This is because the frequency difference between two subcarriers (i.e., a few megahertz) is very small compared to the subcarrier frequency (e.g., 5 GHz) which is in the gigahertz range. If the path length change Δd(t) is λ, the dynamic path phase change of all subcarriers in a narrow bandwidth is almost 2π. Therefore, the present invention can use a unified symbol To express and When Δd(t) increases by λ, In the complex plane, it is a unit circle that rotates clockwise.

[0035] · This invention order and Represents H s (f), H s (f+Δf) and H s (f+2Δf). The present invention further allows and They represent A(f), A(f+Δf) and A(f+2Δf) respectively.

[0036] Then the multi-carrier quadratic ratio can be rewritten as

[0037]

[0038] In wireless sensing, the dynamic component refers to the signal reflected by a moving target. Compared with the LoS (Line-of-Sight) path signal, the dynamic component is weaker. This is because the dynamic path propagation distance is longer and the dynamic signal energy received at the receiving device is lower. Therefore, the product of the two dynamic components (i.e., ③ and ⑥) is much smaller than the product of the two static components or the product of the dynamic component and the static component, and can be ignored. The product of the static components of the two subcarriers (i.e., ① and ④) can be regarded as a constant. Through the above analysis, the multi-carrier quadratic ratio calculated by the present invention is in the form of a Möbius transform. Since the Möbius transform still maintains The present invention constructs a mapping relationship between the multi-carrier quadratic ratio and the change in reflection path length caused by target motion, that is, when the dynamic path length changes by one wavelength, the trajectory of the multi-carrier quadratic ratio on the complex plane is still a circle. When the path length changes by less than one wavelength, the trajectory is an arc. In actual sensing applications, the rotation direction of the multi-carrier quadratic ratio is The same is true, that is, when the dynamic path length increases / decreases, the change trajectory of the multi-carrier secondary ratio on the complex plane rotates clockwise / counterclockwise.

[0039] Therefore, the present invention provides a wireless sensing method based on WiFi multi-carrier, and the specific steps include:

[0040] S1. Collect CSI data of WiFi multi-carrier based on wireless sensing applications of different human movements;

[0041] S2. Select three subcarriers with equal frequency difference from the collected CSI data, and divide the first two and the last two subcarriers into two pairs in the order of frequency from small to large; for each pair of subcarrier CSI, calculate their ratio; then divide the two ratios again, calculate the ratio of the two ratios, and obtain the multi-carrier secondary ratio;

[0042] S3. Construct a mapping relationship between the multi-carrier quadratic ratio and the change in reflection path length caused by target motion, that is, when the dynamic path length increases / decreases, the trajectory of the multi-carrier quadratic ratio change on the complex plane rotates clockwise / counterclockwise;

[0043] S4. The human motion perception result is obtained by inversely deducing the change of the multi-carrier secondary ratio.

[0044] In order to make full use of the diversity of subcarriers, the present invention processes all subcarriers to improve the robustness of the system. Specifically, the present invention uses all subcarriers to construct multiple groups of multi-carrier secondary ratios. For example, a WiFi network card can provide CSI information of 52 subcarriers, then subcarriers No. 1, No. 18, and No. 35 can be selected as one group, and subcarriers No. 2, No. 19, and No. 36 can be selected as another group. Continuing in this way, the present invention can obtain a total of 17 groups of subcarriers for two division operations.

[0045] When the present invention is applied in the aspect of breathing detection, based on the properties derived from formula (5), since the change in the length of the reflection path caused by the rise and fall of the chest during each inhalation or exhalation is less than one wavelength, the trajectory of the change of the multi-carrier secondary ratio signal on the complex plane is a circular arc. And the rotation direction of the trajectory of the change of the multi-carrier secondary ratio signal on the complex plane corresponding to exhalation and inhalation is opposite. Based on this phenomenon, the present invention first uses the principal component analysis method to convert the two-dimensional multi-carrier secondary ratio signal on the complex plane into a one-dimensional breathing waveform, which reflects the change pattern of the reflection path length caused by human breathing. Then, the present invention uses the Savitzky-Golay filter to smooth the extracted breathing waveform. After that, the present invention uses the autocorrelation method to estimate the breathing frequency for the smoothed breathing waveform data, wherein the window size is set to 30 seconds. In order to improve the accuracy of the result, the present invention estimates the breathing frequency using the multi-carrier secondary ratio signal of different carrier combinations, and obtains the final breathing frequency by averaging all the breathing frequency estimation results.

[0046] When the present invention is applied in trajectory tracking, three devices need to be arranged, including a transmitting device and two receiving devices, the two receiving devices are represented by rx1 and rx2 respectively, and the line connecting the transmitting device and rx1 is perpendicular to the line connecting the transmitting device and rx2. The key information in the human body trajectory tracking application is the Doppler frequency shift, based on which the target's movement speed can be solved. Combined with the geometric position relationship between the target and the transceiver, the change speed of the dynamic path length can be converted into the radial speed of the target relative to the transceiver. With the help of another pair of transceivers, the radial speed in the other direction can be obtained. The two radial speeds are integrated to calculate the actual speed of the target. Through the mapping relationship between the constructed multi-carrier quadratic ratio and the change value of the reflection path length caused by human body movement, the present invention can estimate the Doppler frequency shift of the target relative to the transceiver. Specifically, the present invention uses continuous wavelet transform (CWT) to convert the multi-carrier quadratic ratio signal to a time-frequency spectrum for analysis. The present invention selects the frequency component with the largest energy from the time-frequency spectrum as the Doppler frequency shift, thereby calculating the radial speed of the target relative to a pair of transceivers. By combining the radial velocity information of the target relative to the two pairs of transceivers, the real speed of the target can be obtained, including the speed and direction of movement. Given the initial position, the present invention can construct the target motion trajectory by continuously estimating the target speed. At the same time, the Kalman filter is used to further smooth the trajectory.

[0047] The present invention can preprocess the actual CSI to obtain the ideal CSI, so that it can be applied to various wireless sensing applications, such as gesture recognition, fall detection, etc., and has broad application prospects.

[0048] It should be noted that the purpose of publishing the embodiments is to help further understand the present invention, but those skilled in the art can understand that various substitutions and modifications are possible without departing from the spirit and scope of the present invention and the appended claims. Therefore, the present invention should not be limited to the contents disclosed in the embodiments, and the scope of protection claimed by the present invention shall be subject to the scope defined in the claims.

Claims

1. A wireless sensing method based on WiFi multi-carrier, characterized in that: The specific steps include: S1. Collect CSI data of WiFi multi-carrier; S2. Select three subcarriers with equal frequency difference from the collected CSI data, and divide the first two and the last two subcarriers into two pairs in the order of frequency from small to large; for each pair of subcarrier CSI, calculate their ratio; then divide the two ratios again, calculate the ratio of the two ratios, and obtain the multi-carrier secondary ratio; S3. Construct a mapping relationship between the multi-carrier quadratic ratio and the change in reflection path length caused by target motion, that is, when the dynamic path length increases / decreases, the trajectory of the multi-carrier quadratic ratio on the complex plane rotates clockwise / counterclockwise; S4. Obtain the human body movement situation according to the change of the multi-carrier secondary ratio.

2. The wireless sensing method based on WiFi multi-carrier according to claim 1, characterized in that: In step S1, one to two pairs of transceiver devices are arranged to support different wireless sensing applications. Respiration monitoring requires a pair of transceiver devices, while trajectory tracking requires a transmitting device and two receiving devices. The distance between the two pairs of transceiver devices is equal and they are placed vertically.

3. The wireless sensing method based on WiFi multi-carrier according to claim 2, characterized in that: For the application of breathing detection, all subcarriers are used to construct multiple multi-carrier quadratic ratio signals. The principal component analysis method is used to extract a one-dimensional breathing waveform from each multi-carrier quadratic ratio signal, and then the autocorrelation method is used to estimate the breathing frequency. The breathing frequency is estimated from multiple multi-carrier quadratic ratio signals by fusion.

4. The wireless sensing method based on WiFi multi-carrier according to claim 3, characterized in that: It is necessary to use a Savitzky-Golay filter to smooth the extracted respiratory waveform. After that, an autocorrelation method is used for the smoothed respiratory waveform data, and a Savitzky-Golay filter is used to smooth the extracted respiratory waveform data.

5. The wireless sensing method based on WiFi multi-carrier according to claim 2, characterized in that: For trajectory tracking applications, all subcarriers are used to construct multiple multi-carrier quadratic ratio signals, each multi-carrier quadratic ratio signal is converted to a time-frequency spectrum for analysis, and the frequency component with the largest energy is selected from the time-frequency spectrum as the Doppler frequency shift to calculate the radial velocity of the target relative to a pair of transceiver devices. The radial velocity is estimated by fusing multiple multi-carrier quadratic ratio signals, and combined with the radial velocity information of the target relative to the two pairs of transceiver devices, the actual movement speed of the target, including the movement rate and movement direction, is obtained.

6. The wireless sensing method based on WiFi multi-carrier according to claim 3 or 5, characterized in that: The multi-carrier dual ratio signal is smoothed using a Savitzky-Golay filter.

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