CN112218303B - Signal conversion method based on Wi-Fi identification system - Google Patents
Signal conversion method based on Wi-Fi identification system Download PDFInfo
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- CN112218303B CN112218303B CN202011043349.5A CN202011043349A CN112218303B CN 112218303 B CN112218303 B CN 112218303B CN 202011043349 A CN202011043349 A CN 202011043349A CN 112218303 B CN112218303 B CN 112218303B
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Abstract
A signal conversion method based on a Wi-Fi identification system is characterized in that a geometrical relation of space position change of a receiving end and a propagation characteristic of Wi-Fi are modeled, so that a CSI signal acquired by the moving receiving end is directly converted into a signal received by Wi-Fi receiving equipment at an original position, and the equipment position independence of Wi-Fi action identification is achieved. According to the Wi-Fi gesture recognition method, propagation characteristics of Wi-Fi signals are utilized, through mathematical modeling and basic transformation, receivers placed at other positions can estimate the gesture of human body actions by means of collected signals, the signals are finally converted into signals which a user should receive when facing Wi-Fi equipment, and finally the effect that the user actions can be recognized by the equipment at almost any position is achieved.
Description
Technical Field
The invention relates to a technology in the field of wireless communication, in particular to a signal conversion method based on a Wi-Fi identification system.
Background
With the rapid development of internet of things (IoT) technology and smart home in recent years, researchers have conducted extensive research on the related work of identifying and authenticating human actions and further controlling smart home by using Wi-Fi devices. In a Wi-Fi based motion recognition system, a user station typically performs motion recognition and verification at a fixed location facing a transmitting end and a receiving end of a Wi-Fi device. However, due to changes in the home environment, the location of the Wi-Fi device may change accordingly, and although the user still stands at the same location for authentication, the received signal is completely different due to the change in the location of the Wi-Fi receiver.
The channel state information CSI refers to channel properties of a communication link, which describe the variation of signals on different subcarrier transmission paths, such as multipath effects, propagation attenuation, signal scattering, signal reflection, etc. Thus, changes in body posture can often result in changes in the CSI data received by the receiving end. Since different actions have different effects on the CSI, the human body action can be identified by using the change of the CSI. But at present, the user action recognition in most indoor environments needs the user and the Wi-Fi recognition equipment to be in fixed positions to achieve a good recognition effect. The need to retrain the motion recognition model if the device location changes greatly increases the user's use threshold, making commercialization of Wi-Fi recognition devices difficult. Therefore, a method for identifying a user action based on the independence of the Wi-Fi device position is particularly important.
Disclosure of Invention
Aiming at the defects of the existing Wi-Fi-based human body gesture recognition system, the invention provides a signal conversion method based on a Wi-Fi recognition system, which utilizes the propagation characteristics of Wi-Fi signals, enables receivers placed at other positions to estimate the human body action gesture by utilizing collected signals and finally converts the signals into signals which a user should receive when facing Wi-Fi equipment by utilizing the collected signals, and finally achieves the effect that the equipment can recognize the user action at almost any position.
The invention is realized by the following technical scheme:
the invention relates to a signal conversion method based on a Wi-Fi identification system, which directly converts a CSI signal acquired by a moving receiving end into a signal received by Wi-Fi receiving equipment at an original position by modeling the geometric relation of the space position change of the receiving end and the propagation characteristic of Wi-Fi, thereby realizing the equipment position independence of Wi-Fi action identification.
The invention relates to a system for realizing the method, which comprises the following steps: signal acquisition unit, signal conversion unit and signal action classification unit, wherein: the signal acquisition unit is responsible for collecting action signals reflected by a human body and transmitting the acquired signals into the signal conversion unit; the signal conversion unit outputs the converted signal to the signal action classification unit through signal differentiation, parameter solving through polynomial fitting and signal conversion, and the signal action classification unit carries out signal conversion and identification irrelevant to the position of equipment.
Technical effects
The invention integrally solves the problem that in the prior art, the position of the Wi-Fi equipment is possibly changed along with the position of a home, and at the moment, the signal from the same action received by a receiving end is greatly different from the signal at the original position due to the change of the position of the equipment, so that the action cannot be identified.
Compared with the prior art, the method utilizes the equipment position information and the Wi-Fi signal propagation characteristic to carry out modeling, and converts the signal actions of the receiving ends at different positions to ensure that the converted signals are consistent with the signals which the receiving end at the initial position should receive. The position of the signal receiving equipment can be changed at will theoretically, and the flexibility and the expansibility of the motion recognition system are greatly improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram illustrating an exemplary linear motion of a user;
FIG. 3 is a diagram of an embodiment of user non-linear behavior;
FIG. 4 is a diagram illustrating an exemplary signal conversion process.
Detailed Description
As shown in fig. 1, the present embodiment relates to a signal conversion method based on a Wi-Fi recognition system, which is based on a Wi-Fi signal conversion manner, senses a human motion by using Channel State Information (CSI) data collected by a Wi-Fi receiver, and achieves a purpose of signal conversion by using a propagation characteristic of a Wi-Fi signal, and the specific steps include:
step 1) modeling the reflection of the signal by the arm according to the propagation characteristic of the wireless signal, which specifically comprises the following steps:
1.1) when the Wi-Fi signal reaches the signal receiving end after being reflected by a unit object, the method comprises the following steps: y (f, t) ═ a (f, t) e-j2πftWherein: t is the propagation time of the signal from the unit object to the receiving end, f is the carrier frequency, a (f, t) is the signal attenuation, and Y (f, t) is the received signal;
1.2) when the propagation distance at the moment t is D (t), according to the inverse square law,where k is the propagation coefficient, and a (f, 0) ═ x (f) is the signal at the object at the beginning, so that the received signal is reduced to: and the channel frequency response is obtained as:i.e., the expression of signal state information in an ideal state vacuum.
1.3) because the signal of the Wi-Fi identification system is reflected by the whole arm of the human body and then reaches a receiving end instead of an object with unit length, the movement of the linear object which is regarded as the process of arm movement needs to be modeled: take linear motion as an example. Fig. 2 is a schematic diagram illustrating the linear motion of the arm at the speed v until the arm moves for the time t, and the moving distance d of the arm is vt; by modeling the palm (i.e., the unit object). According to simple geometric relation calculation, the following results are obtained: d2sinα=D1sinθ,D2cosα=D1cos θ -d, wherein: d is the distance the arm moves within time t, D1Is the distance from the palm to the receiving end at the beginning, D2The distance from the palm to the receiving end at the time t, theta is an included angle between the moving direction of the arm and a connecting line from the palm to the receiving end at the initial time, and alpha is an included angle between the moving direction of the arm at the time t and the connecting line from the palm to the receiving end; elimination of alpha to give Further obtainI.e., the channel state equation for the palm motion.
1.4) to calculate the channel state equation of the whole arm, the channel state information reflected by the whole arm is superposedNamely, the channel frequency response equation of the whole arm motion is specifically: first, t in the equation is derived:further simplifying to: because the existing Wi-Fi-based motion recognition model mostly only utilizes amplitude and does not consider the influence of phase, because | | e-j2πft1, so the cancellation of the influence of phase is followed by:
1.5) for most non-linear motions, it can be converted into several linear motion processes. Since most non-linear motion can be considered linear motion within a relatively small period of time. Fig. 3 is a schematic diagram of the nonlinear operation, which is obtained similarly by selecting two time nodes i and i +1 with shorter intervals: linear motion and non-linear motion can be handled in the same way.
Step 2) signal conversion: as shown in fig. 4, the user performing motion recognition usually stands on the midperpendicular between the signal transmitting end (Tx) and the signal receiving end (Rx), and the differentiated channel state information is as follows according to the geometrical relationship in the figureWherein: k is the propagation factor, v is the velocity of movement, DRxIs the distance, θ, of the user to the receiving end of the initial positionRxThe included angle between the moving direction of the user and the distance from the user to the receiving end when the receiving end is at the initial position; along with the change of the family environment, the receiving end moves to other positions, and at the moment, the moved receiving end receivesThe CSI signal will change, and at this time, the received CSI of the receiving end is differentiated to obtain:wherein: dSpIs the distance, θ, of the user to the receiving end after the moving positionSpThe unknown parameter is k, and the angle between the moving direction of the user and the receiving end after the user arrives at the moving position is obtained by differentiating the signal of the receiving end after moving and then carrying out polynomial fitting, v, DSp,θSpCan be measured to obtain the position information D in the initial stateRx,θRxIt can also be obtained by measurements, i.e. converting the differential signal received at the new position into the differential signal in the initial state: i.e. from dHSp(f, t) to dHRx(f, t); finally by mixing dHRxIntegration of (f, t) yields HRx(f,t)。
In this embodiment, two desktop computers equipped with Intel 5300 wireless network cards and running Ubuntu 12.04 operating systems are used as a sending end and a receiving end, and a Support Vector Machine (SVM) is used as a signal classification mode. Experiments show that even if the signal receiving equipment moves to change the action signals of the user, the signal sorting machine can still correctly identify the corresponding arm action types after the signal receiving equipment is processed by the method.
Compared with the existing recognition system, the recognition method has the advantages that the motion recognition irrelevant to the position of the equipment can be realized, and even if the receiving equipment moves, the accurate recognition of the motion can be still carried out by the aid of the conversion method classifier, so that the method is a method which is never proposed in documents before. Is a beneficial addition to and a great improvement over existing Wi-Fi device based identification systems.
The foregoing embodiments may be modified in many different ways by those skilled in the art without departing from the spirit and scope of the invention, which is defined by the appended claims and all changes that come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Claims (3)
1. A signal conversion method based on a Wi-Fi identification system is characterized in that a CSI signal acquired by a moving receiving end is directly converted into a signal received by Wi-Fi receiving equipment at an original position by modeling a geometric relation of space position change of the receiving end and a propagation characteristic of Wi-Fi, so that equipment position independence of Wi-Fi action identification is realized, and the method specifically comprises the following steps:
step 1) modeling the reflection of the signal by the arm according to the propagation characteristic of the wireless signal;
step 2) signal conversion: the user station for action recognition is on the middle vertical line of the signal transmitting terminal Tx and the signal receiving terminal Rx, and the differentiated channel state information isWherein: k is the signal propagation factor, v is the velocity of movement, DRxIs the distance, θ, of the user to the receiving end of the initial positionRxThe included angle between the moving direction of the user and the distance from the user to the receiving end when the receiving end is at the initial position; along with the change of the family environment, the receiving end moves to other positions, at the moment, the CSI signal received by the moving receiving end changes, and at the moment, the received CSI differential of the receiving end is obtained as follows: wherein: dSpIs the distance, θ, of the user to the receiving end after the moving positionSpThe signal propagation factor k is obtained by differentiating the signal of the moved receiving end and then carrying out polynomial fitting on the included angle between the moving direction of the user and the receiving end after the user reaches the moving position, v and DSp,θSpMeasured to obtain, in the initial statePosition information DRx,DRxThe differential signal received at the new position is converted into the differential signal at the initial state through measurement:
2. The signal conversion method based on the Wi-Fi recognition system according to claim 1, wherein the modeling specifically comprises:
1.1) when the Wi-Fi signal reaches the signal receiving end after being reflected by a unit object, the method comprises the following steps: wherein: t is t1Is the propagation time of the signal from the unit object to the receiving end, f is the carrier frequency, a (f, t)1) For signal attenuation, Y (f, t)1) To receive a signal;
1.2) according to t1Time propagation distance D (t)1) And the law of inverse square law,wherein: a (f, 0) ═ x (f) is the signal at the object at the beginning, so that the received signal reduces to:and the channel frequency response is obtained as:namely an expression of signal state information in an ideal state vacuum;
1.3) because the signal of the Wi-Fi identification system is reflected by the whole arm of the human body and then reaches a receiving end instead of an object with unit length, the movement of the linear object which is regarded as the process of arm movement needs to be modeled: the arm moves linearly at a velocity v until it has moved for a time t2When the arm moves by a distance d ═ vt2(ii) a By modeling the palm, i.e. the unit object, we obtain: d2sinα=D1sinθ,D2cosα=D1cos θ -d, wherein: d is the arm at time t2Distance of movement, D1Is the distance from the palm to the receiving end at the beginning, D2Is at t2At the moment, the distance from the palm to the receiving end, theta is the included angle between the movement direction of the arm and the connecting line between the palm and the receiving end at the beginning, and alpha is the angle of the arm at t2The movement direction at any moment and the connecting line from the palm to the receiving end form an included angle; elimination of alpha to give Further obtainNamely the channel state equation of the palm motion;
1.4) superimposing the channel state information reflected by the whole armNamely, the channel frequency response equation of the whole arm motion is specifically: first, for t in the formula2And (3) carrying out derivation: further simplifying to:because the existing Wi-Fi-based motion recognition model mostly only utilizes amplitude and does not consider the influence of phase, the existing Wi-Fi-based motion recognition model only utilizes amplitude and does not consider the influence of phaseSo eliminating the effect of phase is followed by:
3. A system for implementing the method of claim 1 or 2, comprising: signal acquisition unit, signal conversion unit and signal action classification unit, wherein: the signal acquisition unit is responsible for collecting action signals reflected by a human body and transmitting the acquired signals into the signal conversion unit; the signal conversion unit outputs the converted signal to the signal action classification unit through signal differentiation, parameter solving through polynomial fitting and signal conversion, and the signal action classification unit carries out signal conversion and identification irrelevant to the position of equipment.
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