CN113156220A - Radio wave sensing method and system - Google Patents
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Abstract
The invention discloses a radio wave sensing method and a system, wherein the radio wave sensing method comprises the following steps: acquiring a transmitting or/and receiving beam forming guide matrix of which the transmitting end meets the set requirement; taking the transmission or/and reception beam forming guide matrix meeting the set requirement as a key guide matrix, and carrying out wireless microwave channel measurement according to the key guide matrix; and performing machine learning or/and inference or/and decision of wireless sensing by using the measured wireless microwave channel information. The radio wave sensing method and the radio wave sensing system can improve the accuracy and the sensitivity of radio wave sensing.
Description
Technical Field
The present invention relates to a radio wave sensing method, and more particularly, to a radio wave sensing method and system based on beam forming.
Background
Existing radio wave sensing technologies (e.g., WiFi sensing, millimeter wave sensing, etc.) utilize a wireless receiving device to obtain information related to a channel, such as received signal strength or wireless channel response, and then utilize a machine learning method to analyze the obtained information for learning and inference of a sensing task.
In practice, the radio wave sensing technology has the following two main drawbacks: (1) the radiowave sensing technology is limited by the limited transmitting power, and the receiving and sensing capability thereof is greatly reduced in the environment of large channel attenuation or partition walls. (2) In a rich scattering (rich scattering) environment, channel information is easily interfered by a change of a spatial environment configuration to cause a false judgment of a sensing system in making a statistical inference.
In view of the above, there is a need to design a new radio wave sensing method to overcome at least some of the above-mentioned disadvantages of the existing radio wave sensing methods.
Disclosure of Invention
The invention provides a radio wave sensing method and system, which can improve the accuracy and sensitivity of radio wave sensing.
In order to solve the technical problem, according to one aspect of the present invention, the following technical solutions are adopted:
a radio wave sensing method, the radio wave sensing method comprising:
acquiring a transmitting or/and receiving beam forming guide matrix of which the transmitting end meets the set requirement;
taking the transmission or/and reception beam forming guide matrix meeting the set requirement as a key guide matrix, and carrying out wireless microwave channel measurement according to the key guide matrix;
and performing machine learning or/and inference or/and decision of wireless sensing by using the measured wireless microwave channel information.
As an embodiment of the present invention, when the transmit or/and receive beamforming steering matrix is trained to the optimal state, the optimal transmit or/and receive beamforming steering matrix and the machine learning system parameters are fixed for performing the wireless sensing inference or decision-making; the optimal state is determined according to an optimized cost function.
As an embodiment of the present invention, a transmit or/and receive beamforming steering matrix meeting the set requirements is obtained at the transmitting end or/and the receiving end.
According to another aspect of the invention, the following technical scheme is adopted: a radio wave sensing method, the radio wave sensing method comprising:
based on the defined loss function, a weight function is adjusted that optimizes the transmit or/and receive beamforming steering matrix and the sensing system.
As an embodiment of the present invention, the transmitting or/and receiving beamforming steering matrix weights and the machine learning of wireless sensing are performed sequentially or simultaneously;
if the sequence is carried out, the first step is that when the transmitting or/and receiving beam forming guide matrix is trained to a set state, the transmitting or/and receiving beam forming guide matrix meeting the set requirement is fixed; secondly, performing machine learning training of the wireless sensing system based on the transmitting or/and receiving beam forming guide matrix obtained in the first step to enable the system to obtain optimized sensing system parameters; the optimized transmitting or/and receiving beam forming guide matrix and the sensing system parameters obtained by machine learning are synthesized and used for carrying out inference or decision of wireless sensing;
if the wireless sensing system is simultaneously operated, training and machine learning of parameters of a transmitting or/and receiving beam forming guide matrix and the wireless sensing system are simultaneously operated, and the result is used for carrying out inference or decision of wireless sensing;
when the system is trained to a set state, the transmit or/and receive beamforming steering matrix and the machine learning system parameters meeting the set requirements are fixed for wireless sensing inference or decision-making.
According to another aspect of the invention, the following technical scheme is adopted: a radio wave sensing system, the radio wave sensing system comprising:
a guide data acquisition module for acquiring a transmit or/and receive beamforming guide matrix of which the transmitting end meets the set requirement;
the wireless microwave channel measurement module is used for taking the transmitting or/and receiving beam forming guide matrix meeting the set requirement as a key guide matrix and carrying out wireless microwave channel measurement according to the key guide matrix;
the wireless sensing module is used for utilizing the wireless microwave channel information measured by the wireless microwave channel measuring module to carry out machine learning or/and inference or/and decision of wireless sensing.
As an embodiment of the present invention, the system further comprises a data fixing module for fixing the optimal transmit or/and receive beamforming steering matrix and the wireless sensing system weight when the system is trained to the optimal state, and using the fixed weights for wireless sensing inference or decision-making; the optimal state is determined according to an optimized cost function.
As an embodiment of the present invention, a transmit or/and receive beamforming steering matrix is obtained, where the transmit end or/and the receive end meet a set requirement.
According to another aspect of the invention, the following technical scheme is adopted: a radio wave sensing system adjusts an optimized transmit or/and receive beamforming steering matrix, and a weight function of the sensing system, based on a defined loss function.
As an embodiment of the present invention, the system includes:
a beamforming matrix weight training module for performing transmit or/and receive beamforming steering matrix weight training;
the machine learning module is used for performing machine learning of wireless sensing;
a flow control module for controlling the transmission or/and reception beam forming guide matrix weight training and the wireless sensing machine learning to be performed sequentially or simultaneously;
in the case of sequential operation, the first step is to fix the transmit or/and receive beamforming steering matrix meeting the setting requirements when the transmit or/and receive beamforming steering matrix is trained to the setting state; secondly, performing machine learning training of the wireless sensing system based on the transmitting or/and receiving beam forming guide matrix obtained in the first step to enable the system to obtain optimized sensing system parameters; the optimized transmitting or/and receiving beam forming guide matrix and the sensing system parameters obtained by machine learning are synthesized and used for carrying out inference or decision of wireless sensing;
in the case of simultaneous operation, training and machine learning of transmission or/and reception beam forming guide matrix and wireless sensing system parameters are performed simultaneously, and the results are used for inference or decision of wireless sensing;
when the system is trained to a set state, the wireless sensing system parameters obtained by the transmission or/and reception beam forming guide matrix and machine learning which meet the set requirements are fixed for wireless sensing deduction or decision making.
The invention has the beneficial effects that: the radio wave sensing method and the radio wave sensing system can improve the accuracy and the sensitivity of radio wave sensing.
Drawings
Fig. 1-1 is a schematic diagram of the received energy gain obtained at the receiving end without using the beamforming technique.
Fig. 1-2 are schematic diagrams illustrating the received energy gain obtained at the receiving end by using the beamforming technique.
Fig. 2-1 is a schematic diagram of multipath mitigation effects that may be achieved without using beamforming techniques.
Fig. 2-2 is a schematic diagram of multipath mitigation effects that may be achieved using beamforming techniques.
Fig. 3-1 is a diagram illustrating wireless sensing of the obtained channel information at the receiving end using beamforming at the transmitting end according to an embodiment of the present invention.
Fig. 3-2 is a schematic diagram illustrating an embodiment of using transmit end beamforming, where a receive end feeds back obtained channel information to a transmit end via a feedback path, and then performs wireless sensing at the transmit end.
Fig. 3-3 are diagrams illustrating an embodiment of using receive end beamforming and performing wireless sensing on the obtained channel information at the receive end.
Fig. 3-4 are schematic diagrams illustrating the use of beam forming at the receiving end according to an embodiment of the present invention, in which the receiving end feeds back the obtained channel information to the transmitting end via a feedback path, and then performs wireless sensing at the transmitting end.
Fig. 3-5 are diagrams illustrating wireless sensing at a receiving end using beamforming at the transmitting end and the receiving end and the obtained channel information according to an embodiment of the present invention.
Fig. 3-6 are schematic diagrams illustrating beamforming at the transmitting end and the receiving end, wherein the receiving end feeds back the obtained channel information to the transmitting end via a feedback path and performs wireless sensing at the transmitting end according to an embodiment of the present invention.
Fig. 4 is a flowchart illustrating a two-phase beamforming wireless sensing method according to an embodiment of the invention.
FIG. 5 is a flowchart illustrating a method for phased beamforming wireless sensing according to an embodiment of the present invention.
FIG. 6 is a schematic diagram of a radio wave sensing system according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
For a further understanding of the invention, reference will now be made to the preferred embodiments of the invention by way of example, and it is to be understood that the description is intended to further illustrate features and advantages of the invention, and not to limit the scope of the claims.
The description in this section is for several exemplary embodiments only, and the present invention is not limited only to the scope of the embodiments described. It is within the scope of the present disclosure and protection that the same or similar prior art means and some features of the embodiments may be interchanged.
The steps in the embodiments in the specification are only expressed for convenience of description, and the implementation manner of the present application is not limited by the order of implementation of the steps.
The invention utilizes the beam forming technology to strengthen the radio wave sensing capability; beamforming techniques are widely used in advanced wireless communication systems, such as Wireless Local Area Network (WLAN) techniques like IEEE 802.11n/IEEE 802.11ac/IEEE 802.11ax, and mobile communication systems like 4G/5G, which have been defined separately. The principle of the beam forming technology is to form a beam in a specific direction in space by using a plurality of antennas, so as to improve the received energy of a receiving end in the direction.
Based on the beam forming technique, the wireless radio wave sensing device can have the following two main performance enhancements: (1) receive end energy gain (power gain), as shown in fig. 1-2 (a schematic diagram of the receive energy gain obtained at the receive end without using beamforming technique is shown in fig. 1-1); (2) multipath mitigation (multipath mitigation) as shown in fig. 2-2 (a schematic diagram of the multipath mitigation that can be brought about without using beamforming techniques is shown in fig. 2-1).
The above two advantages correspond to just two major problems encountered by radio wave sensing technology in sensing. The present invention utilizes existing beamforming techniques to address weaknesses in existing radio wave sensing techniques. The following describes the architecture implementation, optimization, and execution, respectively.
In architecture, the beamforming system can be classified into a system with beamforming capability for transmitting end beamforming, receiving end beamforming, and transmitting/receiving according to the socket of the beamforming system. The acquisition of the sensing data can be divided into two ways of direct acquisition and acquisition through a feedback path. The beamforming radiowave sensing system set forth in the present invention includes the above three types of beamforming architectures. The specific structural diagram is shown in 3-1 to 3-6.
In the implementation, the present invention includes two main types of implementation processes of the beamforming sensing system, which are respectively: (1) a two-stage treatment process; (2) a combined treatment process.
FIG. 4 is a schematic diagram of a first two-stage processing procedure; referring to fig. 4, in the processing procedure, the first stage determines the beamforming steering matrix of the transmitting and receiving ends; in the second stage, based on the selected steering matrix, the learning, inference or decision of the sensing system is performed. The entire optimization and execution process may be iterated multiple times, i.e., stage one → stage two → ….
In an embodiment of the present invention, the radio wave sensing method includes: acquiring a transmission guide matrix of which the transmission end meets the set requirement; taking the transmission guide matrix meeting the set requirement as a key transmission guide matrix, and measuring the wireless microwave channel according to the key transmission guide matrix; and performing machine learning or/and inference or/and decision of wireless sensing by using the measured wireless microwave channel information.
In an embodiment of the present invention, when the system is trained to the optimal state, the optimal beamforming matrix and the machine learning system parameters are fixed for performing wireless sensing inference or decision-making; the optimal state is determined according to an optimized cost function. In one embodiment, a transmit steering matrix is obtained at the transmitting end that meets the set requirements.
FIG. 4 is a flow chart of a method for sensing a beamformed radio wave according to an embodiment of the present invention; the radio wave sensing method includes:
step S1, the transmitting end uses the existing Array Signal Processing (Array Signal Processing) or the existing communication protocol (e.g., the multiplexing beacon in IEEE 802.11 n) to obtain the transmitting end' S optimal transmitting pilot matrix. The best definition may be the maximum received energy, or the minimum received error rate.
Step S2, the transmit steering matrix is fixed to the optimal matrix obtained in step one, and the wireless microwave channel measurement is performed.
Step S3, learning, inference or decision of wireless sensing is performed by using the measured wireless microwave channel information.
The second architecture is a combined process, which adjusts the weight functions of the optimal beamforming steering matrix and the sensing system based on the defined loss function. The optimization and execution process may also be performed in multiple iterations.
FIG. 5 is a flow chart of a method for sensing a beamformed radio wave according to an embodiment of the present invention; referring to fig. 5, in the present embodiment, the transmit-side beamforming steering matrix weights and the machine learning of the wireless sensing are performed simultaneously. When the system is trained to be optimal (the optimal definition depends on its optimized cost function), the next best beamforming steering matrix and machine learning system parameters are fixed for wireless sensing inference or decision-making.
FIG. 6 is a schematic diagram of a radio wave sensing system according to an embodiment of the present invention; referring to fig. 6, the radio wave sensing system includes: the device comprises a guide data acquisition module 1, a wireless microwave channel measurement module 2 and a wireless sensing module 3. The guide data acquisition module 1 is used for acquiring a transmission or/and reception beam forming guide matrix of which the transmission end and/or the reception end meets the set requirement; the wireless microwave channel measuring module 2 is used for taking the transmitting or/and receiving beam forming guide matrix meeting the set requirement as a key guide matrix and carrying out wireless microwave channel measurement according to the key guide matrix; the wireless sensing module 3 is used for performing machine learning or/and inference or/and decision of wireless sensing by using the wireless microwave channel information measured by the wireless microwave channel measuring module.
In an embodiment of the present invention, the system may further include a data fixing module for fixing the optimal beamforming steering matrix and the wireless sensing system weight when the system is trained to the optimal state, and using the fixed optimal beamforming steering matrix and the wireless sensing system weight for performing wireless sensing inference or decision-making; the optimal state is determined according to an optimized cost function.
In an embodiment of the present invention, a transmit or/and receive beamforming steering matrix is obtained for which the transmitting end meets the set requirements.
In an embodiment of the present invention, the optimized beamforming steering matrix and the weight function of the sensing system are adjusted based on the defined loss function. In one embodiment, the system comprises: the device comprises a beam forming guide matrix weight training module, a machine learning module and a flow control module. The beam forming guide matrix weight training module is used for carrying out transmission or/and receiving end beam forming guide matrix weight training; the machine learning module is used for performing machine learning of wireless sensing; the flow control module is used for controlling the transmission or/and receiving end beam forming guiding matrix weight training and the wireless sensing machine learning to be carried out, and controlling the transmission or/and receiving end beam forming guiding matrix weight training and the wireless sensing machine learning to be carried out sequentially or simultaneously.
In the case of sequential operation, the first step is to fix the beamforming steering matrix meeting the setting requirement when the beamforming steering matrix is trained to the setting state; secondly, performing machine learning training of the wireless sensing system based on the beam forming guide matrix obtained in the first step to enable the system to obtain optimized sensing system parameters; and synthesizing the optimized beam forming guide matrix and the sensing system parameters obtained by machine learning, and using the sensing system parameters to carry out inference or decision of wireless sensing. Under the condition of simultaneous operation, the training and machine learning of the beam forming guide matrix and the wireless sensing system parameters are simultaneously performed, and the results are used for carrying out the inference or decision of wireless sensing. When the system is trained to a set state, fixing the beam forming guide matrix meeting the set requirement and the wireless sensing system parameters obtained by machine learning for wireless sensing inference or decision making.
In summary, the radio wave sensing method and system provided by the invention can improve the accuracy and sensitivity of radio wave sensing.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The description and applications of the invention herein are illustrative and are not intended to limit the scope of the invention to the embodiments described above. Effects or advantages referred to in the embodiments may not be reflected in the embodiments due to interference of various factors, and the description of the effects or advantages is not intended to limit the embodiments. Variations and modifications of the embodiments disclosed herein are possible, and alternative and equivalent various components of the embodiments will be apparent to those skilled in the art. It will be clear to those skilled in the art that the present invention may be embodied in other forms, structures, arrangements, proportions, and with other components, materials, and parts, without departing from the spirit or essential characteristics thereof. Other variations and modifications of the embodiments disclosed herein may be made without departing from the scope and spirit of the invention.
Claims (10)
1. A radio wave sensing method, characterized in that the radio wave sensing method comprises:
acquiring a transmitting or/and receiving beam forming guide matrix of which the transmitting end meets the set requirement;
taking the transmission or/and reception beam forming guide matrix meeting the set requirement as a key guide matrix, and carrying out wireless microwave channel measurement according to the key guide matrix;
and performing machine learning or/and inference or/and decision of wireless sensing by using the measured wireless microwave channel information.
2. The radio wave sensing method according to claim 1, characterized in that:
when the system is trained to the optimal state, fixing the optimal transmit or/and receive beam forming guide matrix and the wireless sensing system weight for wireless sensing inference or decision making; the optimal state is determined according to an optimized cost function.
3. The radio wave sensing method according to claim 1, characterized in that:
and obtaining the transmitting or/and receiving beam forming guide matrix of which the transmitting end or/and the receiving end meet the set requirement.
4. A radio wave sensing method, characterized in that the radio wave sensing method comprises:
based on the defined loss function, the optimal transmit or/and receive beamforming steering matrix, and the weight function of the sensing system, are adjusted.
5. The radio wave sensing method according to claim 4, characterized in that:
the training of the weight of the transmitting or/and receiving beam forming guide matrix and the machine learning of the wireless sensing are carried out sequentially or simultaneously;
if the sequence is carried out, the first step is that when the transmitting or/and receiving beam forming guide matrix is trained to a set state, the transmitting or/and receiving beam forming guide matrix meeting the set requirement is fixed; secondly, performing machine learning training of the wireless sensing system based on the transmitting or/and receiving beam forming guide matrix obtained in the first step to enable the system to obtain optimized sensing system parameters; the optimized transmitting or/and receiving beam forming guide matrix and the sensing system parameters obtained by machine learning are synthesized and used for carrying out inference or decision of wireless sensing;
if the wireless sensing system is simultaneously operated, training and machine learning of parameters of a transmitting or/and receiving beam forming guide matrix and the wireless sensing system are simultaneously operated, and the result is used for carrying out inference or decision of wireless sensing;
when the system is trained to a set state, the wireless sensing system parameters obtained by the transmission or/and reception beam forming guide matrix and machine learning which meet the set requirements are fixed for wireless sensing inference or decision-making.
6. A radio wave sensing system, characterized in that the radio wave sensing system comprises:
a guide data acquisition module for acquiring a transmit or/and receive beamforming guide matrix of which the transmitting end meets the set requirement;
the wireless microwave channel measurement module is used for taking the transmitting or/and receiving beam forming guide matrix meeting the set requirement as a key guide matrix and carrying out wireless microwave channel measurement according to the key guide matrix;
the wireless sensing module is used for utilizing the wireless microwave channel information measured by the wireless microwave channel measuring module to carry out machine learning or/and inference or/and decision of wireless sensing.
7. The radio wave sensing system according to claim 6, wherein:
the system further includes a data fixing module for fixing the optimal transmit or/and receive beamforming steering matrix and the wireless sensing system weights for wireless sensing inference or decision making when the system is trained to an optimal state; the optimal state is determined according to an optimized cost function.
8. The radio wave sensing system according to claim 6, wherein:
and obtaining the transmitting or/and receiving beam forming guide matrix of which the transmitting end meets the set requirement.
9. A radio wave sensing system characterized by: based on the defined loss function, the optimal transmit or/and receive beamforming steering matrix, and the weight function of the sensing system, are adjusted.
10. The radio wave sensing system according to claim 9, wherein:
the system comprises:
a beamforming matrix weight training module for performing transmit or/and receive beamforming steering matrix weight training;
the machine learning module is used for performing machine learning of wireless sensing;
a flow control module for controlling the transmission or/and reception beam forming guide matrix weight training and the wireless sensing machine learning to be performed sequentially or simultaneously;
in the case of sequential operation, the first step is to fix the transmit or/and receive beamforming steering matrix meeting the setting requirements when the transmit or/and receive beamforming steering matrix is trained to the setting state; secondly, performing machine learning training of the wireless sensing system based on the transmitting or/and receiving beam forming guide matrix obtained in the first step to enable the system to obtain optimized sensing system parameters; the optimized transmitting or/and receiving beam forming guide matrix and the sensing system parameters obtained by machine learning are synthesized and used for carrying out inference or decision of wireless sensing;
in the case of simultaneous operation, training and machine learning of transmission or/and reception beam forming guide matrix and wireless sensing system parameters are performed simultaneously, and the results are used for inference or decision of wireless sensing;
when the system is trained to a set state, the wireless sensing system parameters obtained by the transmission or/and reception beam forming guide matrix and machine learning which meet the set requirements are fixed for wireless sensing deduction or decision making.
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Application publication date: 20210723 |