A method and device for estimating the angle of arrival of WiFi signals based on rotating dual antennas

Abstract

A method and device for estimating the angle of arrival of WiFi signals based on rotating dual antennas, comprising: 1) data acquisition and preprocessing; 2) constructing a virtual antenna array; 3) introducing a rotational relationship to simplify the association model: combining the spatial characteristics of the rotating antennas of the rotating device to further simplify the association model and ultimately convert it into a linear equation to solve the angle of arrival; 4) removing the angle of arrival results affected by multipath; and 5) device application: deploying the device in an actual indoor positioning scenario. The proposed method simulates a virtual circular antenna array by rotating dual antennas, enabling the dual physical antennas to have three-dimensional angle measurement capabilities. By incorporating the rotational characteristics of the dual antennas into the association model, the method successfully converts the angle of arrival solution into a system of linear equations for rapid solution. Compared to traditional angle of arrival estimation methods, the proposed method achieves a higher angle of arrival solution speed with similar angle measurement accuracy. Experiments show that the proposed method increases the solution speed by 10-20 times, addressing the problems of existing dual-antenna angle measurement devices, such as the inability to automatically estimate three-dimensional angle of arrival and the high time consumption.

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 angle of arrival of a WiFi signal based on rotating dual antennas. Background Art

[0002] In the field of mobile communications, demand for location-based services (LBS) is growing. With the widespread adoption of wireless local area networks (WLANs), WiFi-based indoor positioning systems are an emerging research hotspot that caters to this demand. Obtaining the AoA (angle of arrival) information of incoming waves in indoor environments provides key positioning parameters for indoor positioning systems, enabling high-precision positioning in indoor environments. Furthermore, compared to current RSSI-based positioning technologies, AoA-based positioning technologies do not require the construction of a location fingerprint database, saving significant labor costs. Furthermore, AoA-based positioning is a passive positioning technology that can easily achieve network-side positioning of base stations without requiring users to install any additional software. Therefore, accurately estimating the angle of arrival of incoming waves is crucial to solving indoor positioning problems.

[0003] Traditional AoA estimation algorithms, such as MUSIC and ESPRIT, primarily rely on specialized multi-antenna arrays to exploit the orthogonality of the signal and noise subspaces to achieve angle estimation. However, these traditional angle estimation algorithms also have significant drawbacks. Accurate AoA estimation requires the use of specialized large-scale array antennas, which hinders the widespread application of AoA estimation indoors.

[0004] Currently, most mobile communication systems utilize smart antenna technology, enabling service base stations to provide relatively accurate radio wave angle of arrival (AoA) information and network-based location services. For example, current AoA positioning systems based on LTE networks utilize MIMO precoding mechanisms to achieve AoA. For indoor applications, the Ubicarse system employs SAR (Synthetic Aperture Radar) design principles in Wi-Fi environments, rotating the receiving antenna to simulate a large antenna array for angle estimation. Another current system, the ArrayPhaser system, utilizes cascaded Wi-Fi access points to implement multi-antenna array systems. Furthermore, the DirectionFinding system utilizes dual Wi-Fi antennas and interferometer direction finding to measure AoA. While these systems can accurately estimate arrival, they all have varying degrees of limitations. The Ubicarse system requires users to manually rotate the receiving device antenna, and AoA calculation based on spectrum search is time-consuming. The ArrayPhaser system requires full consideration of synchronization errors between devices in its cascaded design, posing significant challenges for ongoing system maintenance. The Direction Finding system uses two antennas for angle estimation, but cannot estimate the three-dimensional angle of arrival and cannot effectively estimate the multipath signals in the environment. Furthermore, the estimated angle resolution is poor. These problems limit the promotion of AoA positioning systems in the field of indoor positioning. Summary of the Invention

[0005] To overcome the shortcomings of existing identification methods, the present invention proposes a method and device for estimating the angle of arrival of WiFi signals based on rotating dual antennas. This method is used to address the technical problems in existing estimation methods, such as the lack of three-dimensional angle measurement capability of the physical antenna array of the dual antennas and the long time required to solve the AoA solution based on spectral search.

[0006] The method proposed in the present invention successfully improves the dual-antenna angle measurement capability and angle measurement time efficiency by introducing virtual antenna technology into the field of wireless positioning technology and designing a device for simulating a virtual antenna array with rotating dual antennas. 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 its 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 in step S1 includes the following steps:

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

[0012]

[0013] in is the channel state measurement value between transmit antenna 1 and receive antenna 2; is the distance between transmitting antenna 1 and receiving antenna 2 at t k The straight-line distance at the moment; f d The (·) function is the energy attenuation function that characterizes the attenuation degree of the signal, which only affects the amplitude but not the phase; λ is the signal wavelength; It is t 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;

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

[0015]

[0016] in(·) * The operation method represents taking the conjugate of the complex number, is the distance difference between transmitting antenna 1 and receiving antenna 1 and receiving antenna 2; Δη 12 is the inherent phase difference between 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 reciprocate the dual antennas and measure the rotation angle of the dual antennas to simulate a circular virtual antenna array. Based on the circular virtual antenna array, a correlation model is constructed between the AoA and the channel state information of the WiFi signal received by the rotating dual antennas and the rotation angle.

[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 pitch angle of AoA, Φ is the azimuth angle of AoA, and θ is the pitch angle of AoA;

[0021] This establishes the dual receiving antenna direction rx 12 The relationship between AoA and phase difference is substituted into the above equation:

[0022]

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

[0024] Step S3: Introducing the rotation relationship to simplify the correlation model: Taking into account the spatial characteristics of the rotating antenna of the rotating device, the correlation model is further simplified and finally converted into a linear equation. A linear equation system is constructed and the AoA is solved using QR decomposition;

[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] 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:

[0033]

[0034] 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:

[0035]

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

[0037]

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

[0039]

[0040] The AoA pitch angle θ is solved according to the following formula

[0041]

[0042] The AoA azimuth angle is solved according to the following formula

[0043]

[0044] This method can also solve the inherent phase difference Δη between the two antennas 12 =-p3.

[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 obtained 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 severely 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 device for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna, comprising a rotating device, a memory, and one or more processors. The memory stores executable code, and when the one or more processors execute the executable code, they are used to implement a method for estimating the angle of arrival of a WiFi signal 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. When the program is executed by a processor, the method for estimating the angle of arrival of a WiFi signal based on rotating dual antennas of the present invention is implemented.

[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. The signal phase characteristics generated by the virtual circular antenna array are used 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 a virtual circular antenna array, the dual physical antennas are given three-dimensional angle measurement capabilities, and the rotation characteristics of the dual antennas are introduced into the correlation model, successfully converting the AoA angle solution into a set of linear equations for rapid solution. Compared with traditional arrival angle estimation methods, 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, solving the problems of existing dual-antenna angle measurement devices that cannot automatically estimate three-dimensional arrival angles and consume a lot of time. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It 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 solutions and advantages of the present invention more clear, the technical solutions 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 the embodiments.

[0060] Example 1

[0061] First combine Figure 1 A method for estimating the angle of arrival of a WiFi signal according to one embodiment of the present invention is described. 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 steps:

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

[0065]

[0066] in is the channel state measurement value between transmit antenna 1 and receive antenna 2; is the distance between transmitting antenna 1 and receiving antenna 2 at t k The straight-line distance at the moment; f d The (·) function is the energy attenuation function that characterizes the attenuation degree of the signal, which only affects the amplitude but not the phase; λ is the signal wavelength; It is t 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;

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

[0068]

[0069] in(·) * The operation method represents taking the conjugate of the complex number, is the distance difference between transmitting antenna 1 and receiving antenna 1 and receiving antenna 2; Δη 12 is the inherent phase difference between 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 described as follows:

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

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

[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 pitch angle of AoA, Φ is the azimuth angle of AoA, and θ is the pitch angle of AoA;

[0076] This establishes the dual receiving antenna direction rx 12 The relationship between AoA and phase difference is substituted into the above equation:

[0077]

[0078] Therefore, a correlation model between AoA, the channel state information of the WiFi signal 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 described 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 rotated back and forth at a speed of 200 RPM with a rotation angle of 360° per round. At the same time, the two antennas are driven to rotate back and forth and the phase difference of the signal measured in step S1 is combined to simulate a circular antenna array.

[0081] Step S3: Introducing the rotation relationship to simplify the correlation model: Taking into account the spatial characteristics of the rotating antenna of the rotating device, the correlation model is further simplified and finally converted into a linear equation. A linear equation system is constructed and the AoA is solved using QR decomposition;

[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] 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:

[0090]

[0091] 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:

[0092]

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

[0094]

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

[0096]

[0097] The AoA pitch angle θ is solved according to the following formula

[0098]

[0099] The AoA azimuth angle is solved according to the following formula

[0100]

[0101] This method can also solve the inherent phase difference Δη between the two antennas 12 =-p3.

[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 obtained 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 severely affected by multipath can be excluded.

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

[0113] In an experiment based on step S6, two devices were set up on either side of an 8x5m room, 8m apart. The coordinates of the target device were obtained using a pre-set coordinate system. The AoA measured by the two devices was simultaneously calculated and modeled as spatial rays. Cross-localization was used to obtain the estimated target device position. The median positioning error achieved in the experiment was 0.43m, similar to existing methods. Experimental comparisons with existing literature showed that the proposed method reduces the angle measurement and resolution time by approximately 10-20 times.

[0114] Example 2

[0115] This embodiment relates to a device for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna, including a rotating device, a memory, and one or more processors. The memory stores executable code. When the one or more processors execute the executable code, they are used to implement a method for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna according to the present invention.

[0116] Example 3

[0117] This embodiment relates to a computer-readable storage medium having a program stored thereon. When the program is executed by a processor, the 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 scope of protection of the present invention should not be regarded as limited to the specific forms described in the embodiments. The scope of protection 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 status information of the transmitting antenna and 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 reciprocate the dual antennas and measure the rotation angle of the dual antennas to simulate a circular virtual antenna array. Based on the circular virtual antenna array, a correlation model is constructed between the AoA and the channel state information of the WiFi signal received by the rotating dual antennas and the rotation angle. Step S3: Introducing the rotation relationship to simplify the correlation model: Taking into account the spatial characteristics of the rotating antenna of the rotating device, the correlation model is further simplified and finally converted into a linear equation. A linear equation system is constructed and the AoA is solved using QR decomposition; Step S4: Remove the AoA result affected by multipath: Calculate an error factor by calculating the error relationship between the phase difference and the cosine function defined by the obtained A, Φ, and C. This error factor indicates the degree of influence of multipath on the AoA estimation result. The error relationship is: in, A contains the AoA elevation angle θ, r is the distance between receiving antenna 1 and receiving antenna 2 fixed by the antenna base, λ is the signal wavelength; Φ is the AoA azimuth angle C contains the inherent phase difference Δη between the two antennas 12 ; is the phase difference of dual antenna signals, is the direction rx from receiving antenna 1 to receiving antenna 2 12 azimuth; The error factor is: Set an error factor threshold. By excluding AoA measurement results with error factors greater than the threshold, AoA angle measurement results that are severely 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, wherein: The data collection and preprocessing described in step S1 includes: Step S11: Wi-Fi signals are collected from the air using the USRP-B210 device, and the channel state information of the collected Wi-Fi signals is estimated. The collected dual-antenna Wi-Fi signal channel state information is modeled as follows: in is the channel state measurement value between transmit antenna 1 and receive antenna 2; is the distance between transmitting antenna 1 and receiving antenna 2 at t k The straight-line distance at the moment; f d The (·) function is the energy attenuation function that characterizes the attenuation degree of the signal, which only affects the amplitude but not the phase; λ is the signal wavelength; It is t 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 the complex number, is the distance difference 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 according to claim 1, wherein: 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 angle between the direction from receiving antenna 1 to receiving antenna 2 and the AoA, and the direction from receiving antenna 1 to receiving antenna 2 is defined as the dual receiving antenna direction rx 12 ; It's rx 12 The azimuth of It's rx 12 is the pitch angle of AoA, Φ is the azimuth angle of AoA, and θ is the pitch angle of AoA; This establishes the dual receiving antenna direction rx 12 The relationship between AoA and phase difference is substituted into the above equation: Therefore, a correlation model between AoA, the channel state information of the WiFi signal 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 3, wherein: Step S3 specifically includes: First, rewrite the association model and replace the key parameters with more concise parameters: 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 it in matrix form and solve it using QR decomposition: Obtain parameter vector After that, the parameters have the following relationships with A, Φ, and C respectively: The AoA pitch angle θ is solved according to the following formula The AoA azimuth angle is solved according to the following formula This method can also solve the inherent phase difference Δη between the two antennas 12 =-p3.

5. A device for estimating the angle of arrival of a WiFi signal based on a rotating dual antenna, 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 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.

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 WiFi signals based on rotating dual antennas according to any one of claims 1 to 4 is implemented.

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