Method and device for estimating direction of arrival of WiFi signal based on rotating double antennas

Abstract

The invention discloses a WiFi signal direction of arrival estimation method and device based on rotary double antennas. The method comprises the following steps: 1) data acquisition and preprocessing; 2) constructing a virtual antenna array; 3) introducing a rotation relation to simplify a correlation model: further simplifying the correlation model in combination with spatial characteristics of a rotation antenna of a rotation device, and finally converting the correlation model into a linear equation to solve a direction of arrival; 4) removing an angle of arrival result influenced by multiple paths; and 5) device application: deploying the device in an actual indoor positioning scene for application. According to the method provided by the invention, the virtual circular antenna array is simulated by rotating the double antennas, so that the double physical antennas have the three-dimensional angle measurement capability, the rotation characteristics of the double antennas are introduced into the correlation model, the direction of arrival calculation is successfully converted into a linear equation set for rapid solution, and compared with a traditional direction of arrival estimation method, the method provided by the invention has the advantages that the calculation efficiency is greatly improved. According to the method, a higher direction-of-arrival resolving speed is obtained with similar angle measurement precision, and experiments show that the resolving speed of the method is increased by 10-20 times, so that the problems that an existing double-antenna angle measurement device cannot automatically estimate the three-dimensional direction of arrival and time consumption is large are solved.

Description

Technical Field

[0001] The present invention relates to the field of wireless positioning technology, and in particular to a method and device for estimating the arrival angle of a WiFi signal based on a rotating dual antenna. Background Art

[0002] In the field of mobile communications, people's demand for location-based services (LBS) is growing. With the popularization of wireless local area networks, WiFi-based indoor positioning systems are an emerging research hotspot that caters to this demand. Obtaining the AoA (angle of arrival) information of the incoming wave in an indoor environment can provide key positioning parameters for the indoor positioning system, thereby achieving high-precision positioning in an indoor environment. At the same time, compared with the current RSSI-based positioning technology, the AoA-based positioning technology does not require the construction of a location fingerprint library, saving a lot of manpower costs. At the same time, the AoA-based positioning technology is a passive positioning technology that can easily realize the network-side positioning of the base station without the user having to install any redundant software. Therefore, how to accurately estimate the arrival angle of the incoming wave direction is the key to solving the indoor positioning problem.

[0003] Traditional AoA estimation algorithms such as the MUSIC algorithm and the ESPRIT algorithm mainly use the orthogonality of the signal subspace and the noise subspace of a dedicated multi-antenna array device to achieve angle estimation. However, traditional angle estimation algorithms also have obvious defects. Dedicated large-scale array antennas must be used to accurately estimate AoA information, which sets up obstacles for the application and promotion of AoA estimation in indoor areas.

[0004] At present, most mobile communication systems use smart antenna technology, so the service base station will be able to provide more accurate radio wave arrival angle information and provide location services based on network-side positioning. For example, the current AoA positioning system based on the LTE network uses the MIMO precoding mechanism to achieve AoA acquisition. In indoor applications, there is currently an Ubicarse system, which uses the SAR (Synthetic Aperture Radar) design concept in the WiFi environment to simulate a large antenna array by rotating the receiving end antenna to achieve angle estimation. In addition, another current system, the ArrayPhaser system, realizes the design of a system with a multi-antenna array structure by cascading WiFi access points. In addition, the DirectionFinding system uses WiFi dual antennas and the principle of interferometer direction finding to achieve arrival angle measurement. Although the above systems can accurately estimate arrival, they all have defects to varying degrees. The implementation of the Ubicarse system requires the user to manually rotate the antenna of the receiving device, and the time consumption of AoA settlement based on spectrum search is high. The ArrayPhaser system needs to fully consider the synchronization errors of different devices in the cascade design, which poses a great challenge to the maintenance of the later system. The DirectionFinding system uses two antennas to perform angle estimation, which cannot estimate the three-dimensional arrival angle and cannot effectively estimate the multipath signals in the environment. At the same time, the estimated angle resolution is poor. The above problems will limit the promotion of AoA positioning system in the field of indoor positioning. Summary of the invention

[0005] In order to overcome the shortcomings of existing identification methods, the present invention proposes a method and device for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna, so as to solve the technical problems that the physical antenna array of the dual antenna in the existing estimation method has no three-dimensional angle measurement capability and the AoA solution based on spectrum search takes a long time.

[0006] The method proposed in the present invention introduces virtual antenna technology in the field of wireless positioning technology, and designs a device for rotating dual-antenna simulating virtual antenna array, thereby successfully improving the dual-antenna angle measurement capability and angle measurement time efficiency. Experiments show that the method has three-dimensional angle measurement capability and the angle measurement rate is increased by 10-20 times, which solves the limitations of the existing AoA positioning system in the field of indoor positioning.

[0007] The technical solution adopted by the present invention to solve the technical problem is:

[0008] A method for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna, the method comprising the following steps:

[0009] Step S1: Data collection and preprocessing: Construct a Wi-Fi signal detector, collect channel status information of the transmitting antenna and the dual receiving antennas, and extract the phase difference of the signals collected by the dual antennas.

[0010] Furthermore, the data collection and preprocessing described in step S1 includes the following processes:

[0011] Step S11: Wi-Fi signals are collected from the air by using the USRP-B210 device, and the channel state information of the collected WiFi signals is estimated. The collected dual-antenna WiFi signal channel state information is modeled as:

[0012]

[0013] in is the channel state measurement value between transmitting antenna 1 and receiving antenna 2; is the distance between transmitting antenna 1 and receiving antenna 2 at t k The straight-line distance at the time; f d (·) function is the energy attenuation function, which characterizes the attenuation degree of the signal and only affects the amplitude but not the phase; λ is the signal wavelength; Yes k Random phase error at time; η 1 and η 2 are the inherent phase differences of receiving antenna 1 and receiving antenna 2 respectively; and is Gaussian noise with mean 0;

[0014] Step S12: Obtain the dual antenna signal phase difference by conjugate multiplication.

[0015]

[0016] in(·) * The operation method represents taking the conjugate of a complex number, is the difference in distance between transmitting antenna 1 and receiving antenna 1 and receiving antenna 2; Δη 12 is the inherent phase difference of the two antennas; it can be noted that in this step, the random phase error is eliminated by conjugate multiplication.

[0017] Step S2: constructing a virtual antenna array: using a rotating device to reciprocately rotate the dual antennas and measure the rotation angle of the dual antennas to simulate a circular virtual antenna array, and constructing a correlation model between AoA and the channel state information of the WiFi signal received by the rotating dual antennas and the rotation angle based on the circular virtual antenna array;

[0018] Assuming the signal is a plane wave, it can be approximated by the following formula:

[0019]

[0020] Where r is the distance between receiving antenna 1 and receiving antenna 2 fixed by the antenna base; Δγ is the direction from receiving antenna 1 to receiving antenna 2 (defined as the dual receiving antenna direction rx 12 ) and the angle between AoA; It's rx 12 The azimuth of It's rx 12 is the elevation angle of AoA, Φ is the azimuth angle of AoA, and θ is the elevation angle of AoA;

[0021] This establishes the dual receiving antenna direction rx 12 The relationship between AoA and phase difference is as follows:

[0022]

[0023] Therefore, a correlation model between AoA and the channel state information of WiFi signals received by the rotating dual antennas and the rotation angle of the dual antennas was constructed.

[0024] Step S3: Introduce the rotation relationship to simplify the association model: Combine the spatial characteristics of the rotating device rotating antenna, further simplify the association model, and finally transform it into a linear equation, construct a linear equation system and use QR decomposition to solve AoA;

[0025] First, rewrite the association model and replace the key parameters with more concise parameters:

[0026]

[0027] in

[0028]

[0029] In the above formula, A includes the AoA pitch angle θ, and Φ is directly the AoA azimuth angle C includes the inherent phase difference between the two antennas;

[0030] Using the characteristics of trigonometric functions, the correlation model can be further decomposed into

[0031]

[0032] where p 1 , p 2 , p 3 , p 4 is the parameter to be determined, and the phase difference There is a linear relationship between them; at this point, p can be solved by solving the linear equation 1 , p 2 , p 3 , p 4, but the spatial characteristics of the dual antennas are rotated by the rotating device, that is, the rotation of the dual antennas occurs in the same plane. Substituting this value directly into the above formula, the four-parameter estimation problem is further simplified to a three-parameter estimation problem:

[0033]

[0034] where p 1 , p 2 , p 3 is the parameter to be determined, and is still related to the phase difference There is a linear relationship between them; in order to solve these three parameters, it is necessary to collect the phase difference values ​​collected under multiple different dual receiving antenna directions and the corresponding antenna directions:

[0035]

[0036] Construct a system of linear equations, write them in matrix form and solve them using QR decomposition:

[0037]

[0038] Find the parameter vector After that, the parameters have the following relationships with A, Φ, and C respectively:

[0039]

[0040] The AoA pitch angle θ is calculated by the following formula:

[0041]

[0042] The AoA azimuth angle is calculated by the following formula:

[0043]

[0044] This method can also solve the inherent phase difference Δη between the two antennas 12 =-p 3 .

[0045] Step S4: Remove the AoA result affected by multipath: By calculating the error relationship between the phase difference and the cosine function defined by the solved A, Φ, and C, an error factor can be calculated. The error factor indicates the degree of influence of multipath on the AoA estimation result. The error relationship is:

[0046]

[0047] The error factor is:

[0048]

[0049] An error factor threshold is set. By excluding AoA measurement results with error factors greater than the threshold, AoA angle measurement results that are seriously affected by multipath can be excluded.

[0050] Step S5: Device application: deploy two or more devices in an indoor positioning scenario, process the data collected in the actual indoor positioning scenario, and obtain the AoA measured by multiple deployed devices based on steps S1-S4, and then perform the following operations:

[0051] Step S51: Modeling the AoAs of multiple devices as spatial rays using known device deployment spatial relationships;

[0052] Step S52: using the modeled spatial rays, the three-dimensional spatial position of the target device is solved by cross positioning.

[0053] A second aspect of the present invention relates to a WiFi signal angle of arrival estimation device based on a rotating dual antenna, comprising a rotating device, a memory and one or more processors, wherein the memory stores an executable code, and when the one or more processors execute the executable code, they are used to implement a WiFi signal angle of arrival estimation method based on a rotating dual antenna according to the present invention.

[0054] A third aspect of the present invention relates to a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements a method for estimating the angle of arrival of a WiFi signal based on rotating dual antennas of the present invention.

[0055] The technical concept of the present invention is to propose a virtual circular antenna array simulation method based on rotating dual antennas, and at the same time design an efficient three-dimensional AoA estimation method based on the virtual circular antenna array, and use the signal phase characteristics generated by the virtual circular antenna array to construct an error factor to combat the multipath effect, so that it can use dual antennas to achieve fast three-dimensional angle measurement in indoor positioning scenarios and use multiple deployed devices for three-dimensional positioning, and the positioning results are robust to multipath effects.

[0056] The beneficial effects of the present invention are mainly manifested in: by rotating the dual antennas to simulate the virtual circular antenna array, the dual physical antennas have the ability to measure three-dimensional angles, and the rotation characteristics of the dual antennas are introduced into the correlation model, and the AoA angle solution is successfully converted into a set of linear equations for rapid solution. Compared with the traditional arrival angle estimation method, a higher AoA solution speed is achieved with similar angle measurement accuracy. Experiments have shown that the solution speed of the method is increased by 10-20 times, which solves the problem that the existing dual antenna angle measurement device cannot automatically estimate the three-dimensional arrival angle and consumes a lot of time. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 is a flow chart of the method of the present invention.

[0058] Figure 2 It is a structural schematic diagram of the device of the present invention. DETAILED DESCRIPTION

[0059] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0060] Example 1

[0061] First combine Figure 1 The following is a method for estimating the angle of arrival of a WiFi signal according to an embodiment of the present invention. The method comprises the following steps:

[0062] Step S1: Data collection and preprocessing: Construct a Wi-Fi signal detector, collect channel status information of the transmitting antenna and the dual receiving antennas, and extract the phase difference of the signals collected by the dual antennas.

[0063] Furthermore, the data collection and preprocessing in step S1 includes the following processes:

[0064] Step S11: Wi-Fi signals are collected from the air by using the USRP-B210 device, and the channel state information of the collected WiFi signals is estimated. The collected dual-antenna WiFi signal channel state information is modeled as:

[0065]

[0066] in is the channel state measurement value between transmitting antenna 1 and receiving antenna 2; is the distance between transmitting antenna 1 and receiving antenna 2 at t k The straight-line distance at the time; f d (·) function is the energy attenuation function, which characterizes the attenuation degree of the signal and only affects the amplitude but not the phase; λ is the signal wavelength; Yes k Random phase error at time; η 1 and η 2 are the inherent phase differences of receiving antenna 1 and receiving antenna 2 respectively; and is Gaussian noise with mean 0;

[0067] Step S12: Obtain the dual antenna signal phase difference by conjugate multiplication.

[0068]

[0069] in(·) *The operation method represents taking the conjugate of a complex number, is the difference in distance between transmitting antenna 1 and receiving antenna 1 and receiving antenna 2; Δη 12 is the inherent phase difference of the two antennas; it can be noted that in this step, the random phase error is eliminated by conjugate multiplication.

[0070] Specifically, the embodiment based on step S1 can be briefly introduced as follows:

[0071] A USRP-B210 device was used, the acquisition center frequency was set to 2457 MHz, the bandwidth was set to 20 MHz, the sampling rate was set to 20 MHz, and the dual-antenna channel state information was estimated at a frequency of 200 Hz. A total of 108,000 Wi-Fi signal data were collected, and the dual-antenna signal phase difference was extracted for each data according to the method in step S12 to facilitate the subsequent construction of the association model.

[0072] Step S2: Construct a virtual antenna array: Figure 2 The rotating device reciprocates to rotate the dual antennas and measures the rotation angle of the dual antennas to simulate a circular virtual antenna array, and a correlation model between AoA and the channel state information of the WiFi signal received by the rotating dual antennas and the rotation angle is constructed based on the circular virtual antenna array;

[0073] Assuming the signal is a plane wave, it can be approximated by the following formula:

[0074]

[0075] Where r is the distance between receiving antenna 1 and receiving antenna 2 fixed by the antenna base; Δγ is the direction from receiving antenna 1 to receiving antenna 2 (defined as the dual receiving antenna direction rx 12 ) and the angle between AoA; It's rx 12 The azimuth of It's rx 12 is the elevation angle of AoA, Φ is the azimuth angle of AoA, and θ is the elevation angle of AoA;

[0076] This establishes the dual receiving antenna direction rx 12 The relationship between AoA and phase difference is as follows:

[0077]

[0078] Therefore, a correlation model between AoA and the channel state information of WiFi signals received by the rotating dual antennas and the rotation angle of the dual antennas was constructed.

[0079] Specifically, the embodiment based on step S2 can be briefly introduced as follows:

[0080] The antenna spacing between the two antennas is set to 2.6 mm. The rotating device is placed vertically to the ground to ensure that the direction of the two antennas is horizontal to the ground. The antenna base is reciprocated at a speed of 200 RPM with a rotation angle of 360° per round. At the same time, the two antennas are driven to reciprocate and combine with the signal phase difference measured in step S1 to simulate the circular antenna array.

[0081] Step S3: Introduce the rotation relationship to simplify the association model: Combine the spatial characteristics of the rotating device rotating antenna, further simplify the association model, and finally transform it into a linear equation, construct a linear equation system and use QR decomposition to solve AoA;

[0082] First, rewrite the association model and replace the key parameters with more concise parameters:

[0083]

[0084] in

[0085]

[0086] In the above formula, A includes the AoA pitch angle θ, and Φ is directly the AoA azimuth angle C includes the inherent phase difference between the two antennas;

[0087] Using the characteristics of trigonometric functions, the correlation model can be further decomposed into

[0088]

[0089] where p 1 , p 2 , p 3 , p 4 is the parameter to be determined, and the phase difference There is a linear relationship between them; at this point, p can be solved by solving the linear equation 1 , p 2 , p 3 , p 4 , but the spatial characteristics of the dual antennas are rotated by the rotating device, that is, the rotation of the dual antennas occurs in the same plane. Substituting this value directly into the above formula, the four-parameter estimation problem is further simplified to a three-parameter estimation problem:

[0090]

[0091] where p 1 , p 2 , p 3 is the parameter to be determined, and is still related to the phase difference There is a linear relationship between them; in order to solve these three parameters, it is necessary to collect the phase difference values ​​collected under multiple different dual receiving antenna directions and the corresponding antenna directions:

[0092]

[0093] Construct a system of linear equations, write them in matrix form and solve them using QR decomposition:

[0094]

[0095] Find the parameter vector After that, the parameters have the following relationships with A, Φ, and C respectively:

[0096]

[0097] The AoA pitch angle θ is calculated by the following formula:

[0098]

[0099] The AoA azimuth angle is calculated by the following formula:

[0100]

[0101] This method can also solve the inherent phase difference Δη between the two antennas 12 =-p 3 .

[0102] Step S4: Remove the AoA result affected by multipath: By calculating the error relationship between the phase difference and the cosine function defined by the solved A, Φ, and C, an error factor can be calculated. The error factor indicates the degree of influence of multipath on the AoA estimation result. The error relationship is:

[0103]

[0104] The error factor is:

[0105]

[0106] An error factor threshold is set. By excluding AoA measurement results with error factors greater than the threshold, AoA angle measurement results that are seriously affected by multipath can be excluded.

[0107] Specifically, the embodiment based on step S4 can be briefly introduced as follows:

[0108] Set the threshold to 0.2, E max If the result is higher than the threshold, the angle measurement result is discarded; if it is lower than the threshold, the angle measurement result is accepted.

[0109] Step S5: Device application: deploy two or more devices in an indoor positioning scenario, process the data collected in the actual indoor positioning scenario, and obtain the AoA measured by multiple deployed devices based on steps S1-S4, and then perform the following operations:

[0110] Step S51: Modeling the AoAs of multiple devices as spatial rays using known device deployment spatial relationships;

[0111] Step S52: using the modeled spatial rays, the three-dimensional spatial position of the target device is solved by cross positioning.

[0112] Specifically, the embodiment based on step S6 can be briefly introduced as follows:

[0113] Based on the embodiment of step S6, an experiment is set up to set two devices on both sides of an 8*5m room with a spacing of 8m. The coordinate system set in advance is used to obtain the coordinates of the target device, and the AoA measured by the two devices is solved at the same time and based on the modeling as space rays, the estimated position of the target device is obtained by cross positioning. The median positioning error obtained in the final experiment is 0.43m, which is close to the existing method. At the same time, the experiment is compared with the method setting in the existing literature. The results show that the angle measurement and solution time of the method of the present invention is about 10-20 times faster than that of the existing literature method.

[0114] Example 2

[0115] The present embodiment relates to a WiFi signal angle of arrival estimation device based on rotating dual antennas, including a rotating device, a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement a WiFi signal angle of arrival estimation method based on rotating dual antennas of the present invention.

[0116] Example 3

[0117] The present embodiment relates to a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, a method for estimating the angle of arrival of a WiFi signal based on rotating dual antennas of the present invention is implemented.

[0118] The contents described in the embodiments of this specification are merely an enumeration of the implementation forms of the inventive concept. The protection scope of the present invention should not be regarded as limited to the specific forms described in the embodiments. The protection scope of the present invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A method for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna, comprising the following steps: Step S1: Data collection and preprocessing: construct a Wi-Fi signal detector, collect channel state information of the transmitting antenna and the dual receiving antennas, and extract the phase difference of the signals collected by the dual antennas; Step S2: constructing a virtual antenna array: using a rotating device to reciprocately rotate the dual antennas and measure the rotation angle of the dual antennas to simulate a circular virtual antenna array, and constructing a correlation model between AoA and the channel state information of the WiFi signal received by the rotating dual antennas and the rotation angle based on the circular virtual antenna array; Step S3: Introduce the rotation relationship to simplify the association model: Combine the spatial characteristics of the rotating device rotating antenna, further simplify the association model, and finally transform it into a linear equation, construct a linear equation system and use QR decomposition to solve AoA; Step S4: Remove the AoA result affected by multipath: By calculating the error relationship between the phase difference and the cosine function defined by the obtained A, Φ, and C, an error factor is calculated. The error factor indicates the degree of influence of multipath on the AoA estimation result. The error relationship is: The error factor is: An error factor threshold is set. By excluding AoA measurement results with error factors greater than the threshold, AoA angle measurement results that are seriously affected by multipath can be excluded. Step S5: Device application: deploy two or more devices in an indoor positioning scenario, process the data collected in the actual indoor positioning scenario, and obtain the AoA measured by multiple deployed devices based on steps S1-S4, and then perform the following operations: Step S51: Modeling the AoAs of multiple devices as spatial rays using known device deployment spatial relationships; Step S52: using the modeled spatial rays, the three-dimensional spatial position of the target device is solved by cross positioning.

2. The method for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna according to claim 1, characterized in that: The data collection and preprocessing described in step S1 includes: Step S11: Wi-Fi signals are collected from the air by using the USRP-B210 device, and the channel state information of the collected WiFi signals is estimated. The collected dual-antenna WiFi signal channel state information is modeled as: in is the channel state measurement value between transmitting antenna 1 and receiving antenna 2; is the distance between transmitting antenna 1 and receiving antenna 2 at t k The straight-line distance at the time; f d (·) function is the energy attenuation function, which characterizes the attenuation degree of the signal and only affects the amplitude but not the phase; λ is the signal wavelength; Yes k The random phase error at the moment; η1 and η2 are the inherent phase differences between receiving antenna 1 and receiving antenna 2 respectively; and is Gaussian noise with mean 0; Step S12: Obtain the dual antenna signal phase difference by conjugate multiplication. in(·) * The operation method represents taking the conjugate of a complex number, is the difference in distance between transmitting antenna 1 and receiving antenna 1 and receiving antenna 2; Δη 12 is the inherent phase difference between the two antennas; random phase errors are eliminated by conjugate multiplication.

3. The method for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna as claimed in claim 1, characterized in that: Step S2 specifically includes: Assuming the signal is a plane wave, it can be approximated by the following formula: Where r is the distance between receiving antenna 1 and receiving antenna 2 fixed by the antenna base; Δγ is the direction from receiving antenna 1 to receiving antenna 2 (defined as the dual receiving antenna direction rx 12 ) and the angle between AoA; It's rx 12 The azimuth of It's rx 12 is the elevation angle of AoA, Φ is the azimuth angle of AoA, and θ is the elevation angle of AoA; This establishes the dual receiving antenna direction rx 12 The relationship between AoA and phase difference is as follows: Therefore, a correlation model between AoA and the channel state information of WiFi signals received by the rotating dual antennas and the rotation angle of the dual antennas was constructed.

4. The method for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna according to claim 1, characterized in that: Step S3 specifically includes: First, rewrite the association model and replace the key parameters with more concise parameters: in In the above formula, A includes the AoA pitch angle θ, and Φ is directly the AoA azimuth angle C includes the inherent phase difference between the two antennas; Using the characteristics of trigonometric functions, the correlation model can be further decomposed into Among them, p1, p2, p3, and p4 are the parameters to be determined, and the phase difference There is a linear relationship between them; at this time, p1, p2, p3, and p4 can be solved by solving linear equations, but the spatial characteristics of the dual antennas are rotated by the rotating device, that is, the rotation of the dual antennas occurs in the same plane. Substituting this value directly into the above formula, the four-parameter estimation problem is further simplified to a three-parameter estimation problem: Among them, p1, p2, and p3 are the parameters to be determined, and they are still related to the phase difference. There is a linear relationship between them; in order to solve these three parameters, it is necessary to collect the phase difference values ​​collected under multiple different dual receiving antenna directions and the corresponding antenna directions: Construct a system of linear equations, write them in matrix form and solve them using QR decomposition: Find the parameter vector After that, the parameters have the following relationships with A, Φ, and C respectively: The AoA pitch angle θ is calculated by the following formula: The AoA azimuth angle is calculated by the following formula: This method can also solve the inherent phase difference Δη between the two antennas 12 =-p3.

5. A WiFi signal arrival angle estimation device based on rotating dual antennas, comprising a rotating device, a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the WiFi signal arrival angle estimation method based on rotating dual antennas according to one of claims 1 to 4.

6. A computer-readable storage medium having a program stored thereon, wherein when the program is executed by a processor, the method for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna according to any one of claims 1 to 4 is implemented.

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