An indoor positioning method based on quantum magnetometer and wi-fi fusion

Abstract

The application discloses an indoor positioning method based on quantum magnetometer and Wi-Fi fusion, comprising the following steps: S1, obtaining Wi-Fi observation value, and mapping three-dimensional magnetic field data of the quantum magnetometer into a quantum state to complete feature matching and obtain magnetometer measurement value; S2, setting weight; S3, fusing the measurement value according to the weight to obtain actual measurement value; S4, calculating measurement residual and Kalman gain; S5, correcting the predicted state to obtain indoor positioning result. The application discloses an indoor positioning method based on quantum magnetometer and Wi-Fi fusion, which effectively combines the quantum magnetometer and Wi-Fi signal according to the weight, utilizes the advantages of both, realizes data complementation, significantly improves positioning accuracy and reliability, can realize high-precision and large-range indoor positioning, has quantum computing power landing application potential, and is expected to be applied to positioning data of quantum parallel computing; and adaptability and electromagnetic interference resistance are improved, and deployment cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of indoor positioning quantum magnetism and quantum sensing technology, specifically to an indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi. Background Technology

[0002] With the widespread adoption of smart devices and unmanned systems in the digital world, indoor positioning technology is increasingly being applied in key technology areas such as smart homes, logistics management, and augmented reality. Existing indoor positioning technologies mainly include infrared indoor positioning, visible light indoor positioning, radio frequency identification (RFID) indoor positioning, Wi-Fi indoor positioning, Bluetooth indoor positioning, geomagnetic indoor positioning, ultra-wideband indoor positioning, inertial navigation positioning, and more.

[0003] Wi-Fi indoor positioning technology primarily analyzes the signal strength of access points (APs) and uses the Received Signal Strength Indicator (RSSI) for location tracking. However, existing Wi-Fi indoor positioning technologies are often limited to meter-level accuracy due to multipath effects and signal attenuation, which cannot meet the needs of certain high-precision applications (such as indoor navigation and autonomous driving). Furthermore, Wi-Fi signals are easily interfered with by objects, walls, and other wireless signals, leading to positioning failures or significant reductions in accuracy. Summary of the Invention

[0004] To address the issue of insufficient accuracy in existing Wi-Fi indoor positioning technologies, this invention provides an indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] An indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi includes the following steps:

[0007] S1: Obtain the measurements from the quantum magnetometer and Wi-Fi at the current moment, respectively;

[0008] S2: Set the weights for the quantum magnetometer and Wi-Fi respectively;

[0009] S3: Based on the weights, fuse the measurements from the quantum magnetometer and Wi-Fi at the current moment to obtain the actual measurement value at the current moment;

[0010] S4: Calculate the measurement residual and Kalman gain based on the actual measured value and predicted state at the current moment;

[0011] Kalman gain is a weighting coefficient for balancing the predicted state of the system and the fused observations, used to determine the magnitude of the correction of the predicted state by the measurement residuals.

[0012] S5: Combine the measurement residual and Kalman gain to correct the predicted state at the current moment, and obtain the indoor positioning result at the current moment.

[0013] In the above scheme, by effectively combining quantum magnetometers and Wi-Fi signals according to weights, the advantages of both are utilized to achieve data complementarity, significantly improving positioning accuracy and reliability, enabling high-precision and wide-range indoor positioning; it also enhances adaptability and anti-electromagnetic interference capabilities, and reduces deployment costs.

[0014] Preferably, in step S1, the measurement value of the quantum magnetometer at the current moment is obtained through the following steps. :

[0015] A magnetic field feature library is pre-established, which stores standard magnetic field feature patterns at different locations;

[0016] The three-dimensional magnetic field vector collected by the quantum magnetometer is mapped onto three-dimensional Hilbert space to construct the measured magnetic field quantum state. Standard reference quantum states are constructed by corresponding standard magnetic field vectors at each location in the magnetic field feature library. The quantum state is defined as follows:

[0017]

[0018] in, These are the orthogonal basis vectors of the three-dimensional magnetic field. Let be the value of the three-dimensional magnetic field data collected by the quantum magnetometer at the current time k in the j direction. Let be the value of the standard magnetic field feature pattern at the i-th position in the magnetic field feature library in the j-direction;

[0019] Calculate the inner product of the actual quantum state and the standard quantum state in the coulomb:

[0020]

[0021] The Euclidean distance of the magnetic field, characterized by quantum state overlap, is then derived. :

[0022]

[0023] Traverse all position indices i in the magnetic field feature library to find the quantum state distance. The smallest index i is used as the basis for determining the measurement value of the quantum magnetometer at the current time. This is achieved by superimposing the spatial location information corresponding to index i with the timestamp of the current time k. .

[0024] Preferably, in step S1, the measurement value of Wi-Fi at the current moment is obtained through the following steps. :

[0025] A Wi-Fi fingerprint database is pre-established, which stores standard Wi-Fi signal strength characteristics of different locations;

[0026] Calculate the cosine similarity between the Wi-Fi signal strength data collected by Wi-Fi and the fingerprints in the Wi-Fi fingerprint database:

[0027]

[0028] in, Let k be the signal strength of the j-th access point collected by Wi-Fi at the current time k. The standard Wi-Fi signal strength of the j-th access point for the i-th fingerprint location in the Wi-Fi fingerprint database;

[0029] Searching for The largest value i is obtained by adding the timestamp of the current time k to the position information corresponding to i. .

[0030] Preferably, in step S2, the weights are set according to the following formula:

[0031]

[0032] in, For the weights of the quantum magnetometer, For Wi-Fi weights, For the signal-to-noise ratio of the quantum magnetometer, This represents the signal-to-noise ratio of Wi-Fi.

[0033] Preferably, in step S3, the actual measurement value at the current moment is obtained by fusing according to the following formula. :

[0034]

[0035] in, For the weights of the quantum magnetometer, This represents the measurement value of the quantum magnetometer at the current moment. For Wi-Fi weights, This is the Wi-Fi measurement at the current moment.

[0036] Preferably, based on the state at the previous moment. Predict the state at the current moment to obtain the predicted state at the current moment. The expression is:

[0037]

[0038] in, Here is the state transition matrix. To control the input matrix, To control the input vector, This is the process noise vector.

[0039] Preferably, in step S4, the measurement residual is calculated. The formula is:

[0040]

[0041] in, This is the actual measurement value at the current moment. This is the measurement matrix.

[0042] Preferably, in step S4, the Kalman gain is calculated based on the covariance information of the actual measured value and the predicted state at the current moment. The calculation formula is:

[0043]

[0044] in, Let be the covariance matrix of the predicted state at the current moment. For the measurement matrix, This is the covariance matrix of the actual measured values ​​at the current moment.

[0045] Preferably, the update formula for the covariance matrix of the predicted state at the current time is:

[0046]

[0047] in, Let I be the updated covariance matrix, and let I be the identity matrix.

[0048] Preferably, in step S5, the residual is measured... and Kalman gain Multiply and accumulate to the predicted state at the current time step Above, that is

[0049]

[0050] This allows for the correction of the predicted state at the current moment, resulting in... This serves as the indoor positioning result at the current moment.

[0051] Beneficial technical effects of the present invention:

[0052] This invention provides an indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi. By effectively combining quantum magnetometer and Wi-Fi signals according to weights, the advantages of both are utilized to achieve data complementarity, significantly improving positioning accuracy and reliability. It can achieve high-precision and wide-range indoor positioning; it also improves adaptability and anti-electromagnetic interference capabilities and reduces deployment costs. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the implementation steps of the technical solution of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments. However, the scope of protection of this invention is not limited to the specific embodiments described below.

[0055] Example 1

[0056] like Figure 1 As shown, an indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi includes the following steps:

[0057] S1: Obtain the measurements from the quantum magnetometer and Wi-Fi at the current moment, respectively;

[0058] S2: Set the weights for the quantum magnetometer and Wi-Fi respectively;

[0059] S3: Based on the weights, fuse the measurements from the quantum magnetometer and Wi-Fi at the current moment to obtain the actual measurement value at the current moment;

[0060] S4: Calculate the measurement residual and Kalman gain based on the actual measured value and predicted state at the current moment;

[0061] S5: Combine the measurement residual and Kalman gain to correct the predicted state at the current moment, and obtain the indoor positioning result at the current moment.

[0062] In practice, by effectively combining quantum magnetometers and Wi-Fi signals according to weights, the advantages of both are utilized to achieve data complementarity, significantly improving positioning accuracy and reliability. This enables high-precision and wide-range indoor positioning; it also enhances adaptability and resistance to electromagnetic interference, and reduces deployment costs.

[0063] Example 2

[0064] An indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi includes the following steps:

[0065] S1: Obtain the measurements from the quantum magnetometer and Wi-Fi at the current moment, respectively;

[0066] More specifically, in step S1, the measurement value of the quantum magnetometer at the current moment is obtained through the following steps. :

[0067] A magnetic field feature library is pre-established, which stores standard magnetic field feature patterns (magnetic field intensity distribution, gradient, etc.) at different locations.

[0068] The three-dimensional magnetic field vector collected by the quantum magnetometer is mapped onto three-dimensional Hilbert space to construct the measured magnetic field quantum state. Standard reference quantum states are constructed by corresponding standard magnetic field vectors at each location in the magnetic field feature library. The quantum state is defined as follows:

[0069]

[0070] in, These are the orthogonal basis vectors of the three-dimensional magnetic field. Let be the value of the three-dimensional magnetic field data collected by the quantum magnetometer at the current time k in the j direction. Let be the value of the standard magnetic field feature pattern at the i-th position in the magnetic field feature library in the j-direction;

[0071] Calculate the inner product of the actual quantum state and the standard quantum state in the coulomb:

[0072]

[0073] The Euclidean distance of the magnetic field, characterized by quantum state overlap, is then derived. :

[0074]

[0075] Traverse all position indices i in the magnetic field feature library to find the quantum state distance. The smallest index i is used as the basis for determining the measurement value of the quantum magnetometer at the current time. This is achieved by superimposing the spatial location information corresponding to index i with the timestamp of the current time k. .

[0076] More specifically, in step S1, the Wi-Fi measurement value at the current moment is obtained through the following steps. :

[0077] A Wi-Fi fingerprint database is pre-established, which stores standard Wi-Fi signal strength characteristics (vectors composed of RSSI values ​​of each access point) at different locations.

[0078] Calculate the cosine similarity between the Wi-Fi signal strength data collected by Wi-Fi and the fingerprints in the Wi-Fi fingerprint database:

[0079]

[0080] in, Let k be the signal strength of the j-th access point collected by Wi-Fi at the current time k. The standard Wi-Fi signal strength of the j-th access point for the i-th fingerprint location in the Wi-Fi fingerprint database;

[0081] Searching for The largest value i is obtained by adding the timestamp of the current time k to the position information corresponding to i. .

[0082] S2: Set the weights for the quantum magnetometer and Wi-Fi respectively;

[0083] More specifically, in step S2, the weights are set according to the following formula:

[0084]

[0085] in, For the weights of the quantum magnetometer, For Wi-Fi weights, For the signal-to-noise ratio of the quantum magnetometer, The signal-to-noise ratio of Wi-Fi;

[0086]

[0087] in, The effective power of the quantum magnetometer signal. The effective power of the quantum magnetometer noise. The effective power of the Wi-Fi signal. This represents the effective power of Wi-Fi noise.

[0088] In practice, the above formula enables dynamic weight allocation, which can reasonably adjust the influence of data in the fusion process according to the quality differences under different signal conditions, so as to achieve the best data fusion effect and improve positioning accuracy.

[0089] S3: Based on the weights, fuse the measurements from the quantum magnetometer and Wi-Fi at the current moment to obtain the actual measurement value at the current moment;

[0090] More specifically, in step S3, the actual measurement value at the current moment is obtained by fusing according to the following formula. :

[0091]

[0092] in, For the weights of the quantum magnetometer, This represents the measurement value of the quantum magnetometer at the current moment. For Wi-Fi weights, This is the Wi-Fi measurement at the current moment.

[0093] More specifically, based on the state at the previous moment Predict the state at the current moment to obtain the predicted state at the current moment. The expression is:

[0094]

[0095] in, Here is the state transition matrix. To control the input matrix, To control the input vector, This is the process noise vector.

[0096] A measurement model is established to closely link the current system state with actual measurement data. The measurement model is as follows:

[0097]

[0098] in, This is a measurement matrix used to map the state space to the measurement space, so that the system state can be correlated with the measurement data. The current state; To measure the noise matrix, it is usually assumed that it follows a Gaussian noise distribution to account for unavoidable noise interference factors during the measurement process.

[0099] S4: Calculate the measurement residual and Kalman gain based on the actual measured value and predicted state at the current moment;

[0100] More specifically, in step S4, the measurement residuals are calculated. The formula is:

[0101]

[0102] In the specific implementation process, by calculating the observation residuals, the deviation between the current predicted state and the actual measured state can be clearly identified, providing a basis for subsequent state updates.

[0103] More specifically, in step S4, the Kalman gain is calculated based on the covariance information of the actual measured value and the predicted state at the current moment. The calculation formula is:

[0104]

[0105] in, Let be the covariance matrix of the predicted state at the current moment. This is the covariance matrix of the actual measured values ​​at the current moment.

[0106] In practice, by calculating the Kalman gain, the weights of predicted information and actual measured information can be reasonably balanced when fusing measurement data, thereby optimizing the state estimation results.

[0107] More specifically, the formula for updating the covariance matrix of the predicted state at the current moment is:

[0108]

[0109] in, Let I be the updated covariance matrix, and let I be the identity matrix.

[0110] In practice, updating the covariance matrix allows for real-time adjustment of the uncertainty assessment of the state estimation, providing a more accurate reference for the next round of state prediction and updates.

[0111] S5: Combine the measurement residual and Kalman gain to correct the predicted state at the current moment, and obtain the indoor positioning result at the current moment.

[0112] More specifically, in step S5, by measuring the residual and Kalman gain Multiply and accumulate to the predicted state at the current time step Above, that is

[0113]

[0114] This allows for the correction of the predicted state at the current moment, resulting in... This serves as the indoor positioning result at the current moment.

[0115] Based on the disclosure and teachings of the foregoing specification, those skilled in the art can make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the invention should also fall within the protection scope of the claims of the present invention. Furthermore, although some specific terms are used in this specification, these terms are only for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. An indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi, characterized in that, Includes the following steps: S1: Obtain the measurements from the quantum magnetometer and Wi-Fi at the current moment, respectively; S2: Set the weights for the quantum magnetometer and Wi-Fi respectively; S3: Based on the weights, fuse the measurements from the quantum magnetometer and Wi-Fi at the current moment to obtain the actual measurement value at the current moment; S4: Calculate the measurement residual and Kalman gain based on the actual measured value and predicted state at the current moment; S5: Combine the measurement residual and Kalman gain to correct the predicted state at the current moment, and obtain the indoor positioning result at the current moment.

2. The indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi as described in claim 1, characterized in that, In step S1, the measurement value of the quantum magnetometer at the current moment is obtained through the following steps. : A magnetic field feature library is pre-established, which stores standard magnetic field feature patterns at different locations; The three-dimensional magnetic field vector collected by the quantum magnetometer is mapped onto three-dimensional Hilbert space to construct the measured magnetic field quantum state. Standard reference quantum states are constructed by corresponding standard magnetic field vectors at each location in the magnetic field feature library. The quantum state is defined as follows: in, These are the orthogonal basis vectors of the three-dimensional magnetic field. Let be the value of the three-dimensional magnetic field data collected by the quantum magnetometer at the current time k in the j direction. Let be the value of the standard magnetic field feature pattern at the i-th position in the magnetic field feature library in the j-direction; Calculate the inner product of the actual quantum state and the standard quantum state in the coulomb: The Euclidean distance of the magnetic field, characterized by quantum state overlap, is then derived. : Traverse all position indices i in the magnetic field feature library to find the quantum state distance. The smallest index i is used as the basis for determining the measurement value of the quantum magnetometer at the current time. This is achieved by superimposing the spatial location information corresponding to index i with the timestamp of the current time k. .

3. The indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi according to claim 1, characterized in that, In step S1, the Wi-Fi measurement value at the current moment is obtained through the following steps. : A Wi-Fi fingerprint database is pre-established, which stores standard Wi-Fi signal strength characteristics of different locations; Calculate the cosine similarity between the Wi-Fi signal strength data collected by Wi-Fi and the fingerprints in the Wi-Fi fingerprint database: in, Let k be the signal strength of the j-th access point collected by Wi-Fi at the current time k. The standard Wi-Fi signal strength of the j-th access point for the i-th fingerprint location in the Wi-Fi fingerprint database; Searching for The largest value i is obtained by adding the timestamp of the current time k to the position information corresponding to i. .

4. The indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi according to claim 1, characterized in that, In step S2, the weights are set according to the following formula: in, For the weights of the quantum magnetometer, For Wi-Fi weights, For the signal-to-noise ratio of the quantum magnetometer, This represents the signal-to-noise ratio of Wi-Fi.

5. The indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi according to claim 1, characterized in that, In step S3, the actual measurement value at the current moment is obtained by fusing according to the following formula. : in, For the weights of the quantum magnetometer, This represents the measurement value of the quantum magnetometer at the current moment. For Wi-Fi weights, This is the Wi-Fi measurement at the current moment.

6. The indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi according to claim 1, characterized in that, Based on the state at the previous moment Predict the state at the current moment to obtain the predicted state at the current moment. The expression is: in, Here is the state transition matrix. To control the input matrix, To control the input vector, This is the process noise vector.

7. The indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi according to claim 6, characterized in that, In step S4, the measurement residual is calculated. The formula is: in, This is the actual measurement value at the current moment. This is the measurement matrix.

8. The indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi according to claim 6, characterized in that, In step S4, the Kalman gain is calculated based on the covariance information of the actual measured value and the predicted state at the current moment. The calculation formula is: in, Let be the covariance matrix of the predicted state at the current moment. For the measurement matrix, This is the covariance matrix of the actual measured values ​​at the current moment.

9. The indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi according to claim 8, characterized in that, The formula for updating the covariance matrix of the predicted state at the current moment is: in, Let I be the updated covariance matrix, and let I be the identity matrix.

10. The indoor positioning method based on the fusion of quantum magnetometer and Wi-Fi according to claim 1, characterized in that, In step S5, by measuring the residual and Kalman gain Multiply and accumulate to the predicted state at the current time step Above, that is This allows for the correction of the predicted state at the current moment, resulting in... This serves as the indoor positioning result at the current moment.

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