A seamless indoor and outdoor positioning system and method for mobile phones

Abstract

The present invention discloses a seamless indoor and outdoor positioning system and method for mobile phones, including a data acquisition system, a data preprocessing system and a positioning solution system, wherein the data acquisition system collects and stores data in a data set; the data preprocessing system is a forgetful system, and the forgetful system is represented as data that can only store a period of time, and at the same time, the data preprocessing system includes an IMU data preprocessing system, a WIFI data preprocessing system, an image data preprocessing system and a GNSS data preprocessing system; the data in the data set passes through the data preprocessing system to obtain a state equation with constraints; finally, the positioning solution system obtains the positioning result and performs visualization processing. The present invention uses outdoor GNSS and inertial navigation devices for tight combination positioning, and selects WIFI and inertial devices for tight combination indoors to achieve seamless indoor and outdoor positioning. At the same time, in order to improve the adaptability to the environment, cameras are selected for auxiliary enhancement both indoors and outdoors.

Description

Technical Field

[0001] The present invention relates to the field of indoor positioning technology, and in particular to a seamless indoor and outdoor positioning system and method for a mobile phone. Background Art

[0002] The application of location-based services has attracted more and more attention to positioning technology, and at the same time, higher requirements have been put forward for the accuracy of positioning results. With the popularization and application of smart phones, mobile phones that provide a variety of location-based services will become the main carriers of high-precision positioning for the public in the future. Since navigation signals based on the Global Navigation Satellite System (GNSS) are difficult to receive indoors, they cannot be used for indoor positioning. In order to solve the problem of indoor positioning, a variety of technical solutions have been proposed, such as those based on WiFi, ultra-wideband, Bluetooth, inertial sensors, etc. Compared with other technologies, WiFi has received more attention due to its large audience and low price. Distance measurement methods such as ToA and TDoA have serious deviations in distance measurement results due to clock errors, so it is extremely unreasonable to use them for positioning. The RTT distance measurement method can provide more accurate distance measurement results by eliminating the influence of clock errors, but it cannot obtain good positioning results due to the existence of semi-system errors. Therefore, no reasonable solution has been proposed for the problem of high-precision seamless indoor and outdoor positioning. Summary of the invention

[0003] The object of the present invention is to provide a seamless indoor and outdoor positioning system and method for mobile phones to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a seamless indoor and outdoor positioning system for mobile phones, which includes a data acquisition system, a data preprocessing system and a positioning solution system;

[0005] Wherein, the data acquisition system collects and stores the data into a data set;

[0006] The data preprocessing system is designed as an amnesia system, which can store data for a predetermined length of time; the data preprocessing system includes four subsystems: an IMU data preprocessing system, a WIFI data preprocessing system, an image data preprocessing system and a GNSS data preprocessing system;

[0007] The positioning solution system is used to process the results from the data preprocessing system, obtain the state equation with constraints, solve the positioning results and perform visualization processing.

[0008] According to another aspect of the present invention, a method for seamless indoor and outdoor positioning of a mobile phone is provided, characterized in that it comprises the following steps:

[0009] Step 1: The data acquisition system collects and stores data into a data set, and the data acquisition system can retrieve database information and add it to the data set;

[0010] Step 2, the data acquisition system transmits the data set to the data preprocessing system, and the data preprocessing system includes an IMU data preprocessing system, a WIFI data preprocessing system, an image data preprocessing system and a GNSS data preprocessing system; the corresponding data of each system is processed in the data preprocessing system, and the output results of the data preprocessing system include the IMU integral conversion results of the data under each subsystem under the forgetfulness time, the error elimination and reflection projection results of the WIFI signal, the ionosphere, troposphere, and orbit correction results of the GNSS signal, the image reprojection results, and other data results that can be calculated by the positioning solution system;

[0011] Step 3: Finally, the data preprocessing system transmits all the data results to the positioning and solving system. The positioning and solving system processes the results, obtains the positioning results and performs visualization, and then sends the positioning results and related information to the data set.

[0012] Further, in step 1, the data set includes WIFI data, and the WIFI data specifically includes the BSSID address, RTT, RTT variance, RSSI, and timestamp of the router. The inertial navigation data includes inertial navigation raw data. Preferably, the inertial navigation raw data includes magnetic count values, gravity count values, acceleration count values, gyroscope values, and mobile phone hardware temperature values, and also includes preliminary calculations of acceleration, angular velocity, angle, timestamp, and other parameters obtained by temperature calibration, turntable calibration, filtering, etc. of the data, GNSS pseudo-moment information, signal transmission time, reception time, clock deviation, and calculated carrier phase, integrated Doppler related information, and other parameters used for GNSS calculation and correction, image information, timestamp information, feature sub-information, and matching sub-information provided by the camera, and also includes retrieving database data related to the current data from all previously calculated data in any intermediate links.

[0013] Furthermore, the IMU data preprocessing system converts the IMU information in the data set provided by the data set into acceleration information, angular velocity information, and time information, and also provides the integration result, residual result, covariance matrix, correction function Jacob ratio matrix, transfer function Jacob ratio matrix and other information under the forgetful system time;

[0014] Furthermore, the IMU data preprocessing system is the processing system that is triggered most by the system and also has the lowest priority. The data preprocessing system will be interrupted by the signal from the high-priority signal preprocessing system. Each time it is interrupted, the data preprocessing system will use the critical point information before and after the interruption as the scale, and linearly distribute the weights to the two processes before and after the interruption. At the same time, the inertial information at the interruption point is calculated based on the data, and the inertial information at the interruption point enters the IMU data preprocessing system to obtain the inertial information of the complete cycle;

[0015] Furthermore, multiple time scales are set, the fastest is the sampling time of IMU, followed by SLAM, then WIFI, and the slowest is GNSS; the data preprocessing system priority is: GNSS>WIFI>SLAM>IMU, where when the GNSS signal exists, the forgetful system time is determined by the GNSS time difference, and when the GNSS signal does not exist, the forgetful system time is determined by the WIFI time difference, both of which are linear relationships; multiple time scales, where for any two, the one with a faster sampling time is relative to the one with a slower sampling time, and are relatively called a fast scale and a slow scale;

[0016] By using time alignment, fast-scale data is preprocessed on the slow-time-scale dimension; the data information of each sensor on the slow scale is obtained; and then all data are processed on the slow scale.

[0017] The data preprocessing system priority is: GNSS>WIFI>SLAM>IMU, where when the GNSS signal exists, the forgetfulness system time is determined by the GNSS time difference, and when the GNSS signal does not exist, the forgetfulness system time is determined by the WIFI time difference, both of which are linear relationships;

[0018] Furthermore, the WIFI data preprocessing system includes operations such as monitoring of communication state changes of WIFI-RTT data, white noise processing, phase distortion noise processing, multipath effect processing, kernelized ridge regression, and reflection projection. The communication state change monitoring signal is transmitted to the positioning solution system, and the positioning solution system adds an estimated vector. Preferably, the estimated vector added at this time is the phase distortion noise of the mobile phone and all routers, and the estimated vector will replace the phase distortion noise value in the previous state after stabilization. The output of the WIFI data preprocessing system is the WIFI-RTT observation Jacobi matrix, residual and monitoring signal after noise reduction processing and reflection projection;

[0019] Furthermore, the image data preprocessing system performs visual calibration, marginalization, local parameterization, image equalization, FAST feature point extraction, elimination, calculation, LK optical flow tracking and other processes, obtains information such as image tracking feature points, reset signals, and reprojected matching points to participate in the position solving work of the positioning solving system;

[0020] Furthermore, the GNSS data preprocessing system corrects the pseudo-moment through data information, and then obtains the Jacobi equation and residual information of real-time PPP positioning to participate in the position solution work of the positioning solution system;

[0021] Furthermore, the positioning solution system estimates the speed and posture of all GNSS, WIFI, and camera signals at the time of occurrence, as well as the phase distortion noise of the mobile phone and router.

[0022] Beneficial effects:

[0023] The present invention uses a tight combination of outdoor GNSS and inertial navigation devices for positioning, and uses WIFI and inertial devices for tight combination indoors to achieve seamless indoor and outdoor positioning. At the same time, in order to improve adaptability to the environment, cameras are selected for auxiliary enhancement both indoors and outdoors. The present invention processes all estimable parameters contained in a time window in a time window. This is done because the error amortization between data is taken into account, and there is a strong correlation between the parameters to be estimated. Specifically, it overcomes the problem of non-uniqueness of the solution results of a single sensor, and uses the correlation of the data result position to obtain a unique solution. The present invention can perform segmented processing under a large forgetful time dimension, that is, a large forgetful system can allow multiple small forgetful systems to exist, perform preliminary calculations under each small forgetful system, and then use the results under the large forgetful system to smooth the results calculated under the small forgetful system. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a schematic diagram of a seamless indoor and outdoor positioning system for a mobile phone according to an embodiment of the present invention.

[0026] In the figure: data acquisition system 1, data preprocessing system 2, positioning solution system 3, IMU data preprocessing system 4, WIFI data preprocessing system 5, image data preprocessing system 6, GNSS data preprocessing system 7, data 8, results 9, data set 10, WIFI data 11, inertial navigation raw data 12, and corrected parameters 13. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of the present invention.

[0028] See also Figure 1 According to an embodiment of the present invention, a seamless indoor and outdoor positioning system for a mobile phone comprises a data acquisition system 1, a data preprocessing system 2 and a positioning solution system 3;

[0029] Wherein, the data acquisition system 1 acquires and stores data into a data set 10;

[0030] The data preprocessing system 2 is a forgetful system;

[0031] The forgetfulness system is represented as being able to store data 8 for a predetermined length of time only; the data preprocessing system 2 includes four subsystems: an IMU data preprocessing system 4, a WIFI data preprocessing system 5, an image data preprocessing system 6 and a GNSS data preprocessing system 7;

[0032] The positioning solution system 3 processes the result 9 from the data preprocessing system 2 to obtain a state equation with constraints, and performs visualization processing on the obtained positioning result.

[0033] According to another embodiment of the present invention, a method for seamless indoor and outdoor positioning of a mobile phone is provided, comprising the following steps:

[0034] Step 1: The data acquisition system collects and stores data into a data set, and the data acquisition system can retrieve database information and add it to the data set;

[0035] Step 2, the data acquisition system transmits the data set to the data preprocessing system, the data preprocessing system includes an IMU data preprocessing system, a WIFI data preprocessing system, an image data preprocessing system and a GNSS data preprocessing system; the corresponding data of each system is processed in the data preprocessing system, and the output results of the data preprocessing system include the IMU integral conversion results of the data under each subsystem under the forgetfulness time, the error elimination and reflection projection results of the WIFI signal, the ionosphere, troposphere, orbit correction results of the GNSS signal, the image reprojection results and other data results that can be calculated by the positioning solution system; among them, the error elimination and reflection projection results of the WIFI signal, the ionosphere, troposphere, orbit correction results of the GNSS signal, the image reprojection results and the like can adopt existing technologies;

[0036] Step 3: Finally, the data preprocessing system transmits all the data results to the positioning and solving system. The positioning and solving system processes the results, obtains the positioning results and performs visualization, and then sends the positioning results and related information to the data set.

[0037] According to another embodiment of the present invention, the data set 10 includes WIFI data 11, and the WIFI data 11 specifically includes the BSSID address, RTT, RTT variance, RSSI, and timestamp of the router. The inertial navigation data includes inertial navigation raw data. Preferably, the inertial navigation raw data 12 includes magnetic count values, gravity count values, acceleration count values, gyroscope values, and mobile phone hardware temperature values, and also includes preliminary calculations of acceleration, angular velocity, angle, timestamp, and other parameters obtained by temperature calibration, turntable calibration, filtering, etc. of the data, GNSS pseudo-moment information, signal transmission time, reception time, clock deviation, and calculated carrier phase, integrated Doppler related information, and other parameters used for GNSS calculation and correction 13, image information, timestamp information, feature sub-information, matching sub-information, etc. provided by the camera, and also includes retrieving database data related to the current data in all the data of any intermediate links calculated previously.

[0038] According to another embodiment of the present invention, the IMU data preprocessing system converts the IMU information in the data set provided by the data set into acceleration information, angular velocity information, and time information, and also provides the integration results under the time of the forgetful system, that is, the data is integrated within the time designed by the forgetful system, residual results, covariance matrix, correction function Jacobi matrix, transfer function Jacobi matrix and other information; the integration result is to connect the information between data in the spatial domain in the time dimension under the slow-scale time window, and integration results of different time units will appear. The overall purpose is to provide continuity between data. Short-term time integration can reduce the concern about the accumulation of IMU errors. At the same time, short-term reliable IMU information can provide spatial continuity of high-priority sensor information in the time dimension;

[0039] According to another embodiment of the present invention, the IMU data preprocessing system is the processing system that is triggered most by the system and also has the lowest priority. The data preprocessing system will be interrupted by the signal from the high-priority signal preprocessing system. Each time it is interrupted, the data preprocessing system will use the critical point information before and after the interruption as a scale, and linearly distribute the weights to the two processes before and after the interruption. At the same time, the inertial information at the interruption point is calculated based on the data, and the inertial information at the interruption point enters the IMU data preprocessing system to obtain the inertial information of a complete cycle;

[0040] According to another embodiment of the present invention, the data preprocessing system priority is: GNSS>WIFI>SLAM>IMU, wherein when the GNSS signal exists, the forgetfulness system time is determined by the GNSS time difference, and when the GNSS signal does not exist, the forgetfulness system time is determined by the WIFI time difference, both of which are linear relationships;

[0041] In the embodiment of the present invention, firstly, multiple time scales are set, the fastest one is IMU time, the sampling frequency is 100HZ, followed by SLAM, the sampling frequency is 10HZ, the WIFI sampling frequency is 5HZ, and the GNSS sampling frequency is 1HZ.

[0042] In the present invention, time alignment is used to adapt the fast scale to the slow scale. It can be regarded as preprocessing the fast scale data in the dimension of the slow time scale to obtain the data information of each sensor in the slow scale.

[0043] All data are then processed at the slow scale.

[0044] As mentioned above, the present invention performs comprehensive processing on data in a time window. This time window is adapted to the slow scale, and the slow scale is the sensor priority order of the present invention. The present invention processes all the estimable parameters contained in this time window in a time window. This is done because the error amortization between data and the strong correlation between the parameters to be estimated are taken into consideration. Specifically, the problem of non-unique solution results of a single sensor is overcome, and the correlation of the data result position is used to obtain a unique solution.

[0045] Therefore, the time length set by the forgetfulness system is related to the slow scale time, specifically, it is several times of the full slow scale moment. For example, the present invention can optionally adopt 15 times the highest slow scale. That is, the time range is about 15 seconds at most, that is, the forgetfulness time of the forgetfulness system of the present invention is 15 seconds, but for WIFI, this forgetfulness time is less than 2 seconds.

[0046] Therefore, segmented processing can be performed under a large forgetful time dimension, that is, a large forgetful system can allow the existence of multiple small forgetful systems, perform preliminary calculations under each small forgetful system, and then use the results under the large forgetful system to smooth the results calculated under the small forgetful systems.

[0047] According to another embodiment of the present invention, the WIFI data preprocessing system includes operations such as monitoring the communication state change of WIFI-RTT data, white noise processing, phase distortion noise processing, multipath effect processing, kernelized ridge regression, and reflection projection. The communication state change monitoring signal is transmitted to the positioning solution system, and the positioning solution system adds an estimated vector. Preferably, the estimated vector added at this time is the phase distortion noise of the mobile phone and all routers, and the estimated vector will replace the phase distortion noise value in the previous state after stabilization. The output of the WIFI data preprocessing system is the WIFI-RTT observation Jacobi matrix, residual and monitoring signal after noise reduction processing and reflection projection;

[0048] According to one embodiment of the present invention, the image data preprocessing system performs visual calibration, marginalization, local parameterization, image equalization, FAST feature point extraction, elimination, calculation, LK optical flow tracking and other processes, obtains image tracking feature points, reset signals, reprojected matching points and other information to participate in the position solving work of the positioning solving system;

[0049] According to an embodiment of the present invention, the GNSS data preprocessing system corrects the pseudo-moment through data information, and then obtains the Jacobi equation and residual information of real-time PPP positioning to participate in the position solution work of the positioning solution system;

[0050] According to one embodiment of the present invention, the positioning solution system needs to estimate the speed and posture of all GNSS, WIFI, and camera signals at the time of occurrence, as well as the phase distortion noise of the mobile phone and the router.

[0051] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for seamless indoor and outdoor positioning of mobile phones, using a seamless indoor and outdoor positioning system for mobile phones, the system includes a data acquisition system, a data preprocessing system and a positioning solution system; in, The data acquisition system collects and stores data into a data set; The data preprocessing system is designed as an amnesia system, and the amnesia system can store data for a predetermined length of time; The data preprocessing system includes four subsystems: IMU data preprocessing system, WIFI data preprocessing system, image data preprocessing system and GNSS data preprocessing system; The positioning solution system is used to process the results from the data preprocessing system, obtain the state equation with constraints, solve the positioning results and perform visualization processing, and is characterized by comprising the following steps: Step 1: The data acquisition system collects and stores data into a data set, and the data acquisition system can retrieve database information and add it to the data set; Step 2, the data acquisition system transmits the data set to the data preprocessing system, and the data preprocessing system includes an IMU data preprocessing system, a WIFI data preprocessing system, an image data preprocessing system and a GNSS data preprocessing system; the data is preprocessed in the data preprocessing system, and the output result of the data preprocessing system includes all data results that can be calculated by the positioning solution system: IMU integral conversion results of data under each subsystem under the forgetfulness time, error elimination and reflection projection results of WIFI signals, ionosphere, troposphere, orbit correction results of GNSS signals, and image reprojection results; Step 3: Finally, the data preprocessing system transmits all the data results to the positioning and solving system. The positioning and solving system processes the results, obtains the positioning results and performs visualization, and then sends the positioning results and related information to the data set.

2. A method for seamless indoor and outdoor positioning of a mobile phone according to claim 1, characterized in that: In the step 1, the data set includes WIFI data, and the WIFI data specifically includes the BSSID address, RTT, RTT variance, RSSI, and timestamp of the router. The inertial navigation data includes inertial navigation raw data, and the inertial navigation raw data includes magnetic count values, gravity count values, acceleration count values, gyroscope values, and mobile phone hardware temperature values, and also includes acceleration, angular velocity, angle, and timestamp parameters obtained by preliminary calculation of temperature calibration, turntable calibration, and filtering of the data, GNSS pseudo-moment information, signal transmission time, reception time, clock deviation, and calculated carrier phase, integrated Doppler related information for GNSS calculation and correction parameters, SLAM image information, timestamp information, feature sub-information, and matching sub-information provided by the camera, and also includes retrieving database data related to the current data in all previously calculated data of any intermediate links.

3. A method for seamless indoor and outdoor positioning of a mobile phone according to claim 2, characterized in that: The IMU data preprocessing system converts the IMU information in the data set provided by the data set into acceleration information, angular velocity information, and time information, and also provides the integration results, residual results, covariance matrix, correction function Jacobian matrix, and transfer function Jacobian matrix information under the forgetful system time.

4. A method for seamless indoor and outdoor positioning of a mobile phone according to claim 3, characterized in that: The IMU data preprocessing system is the processing system that is triggered most frequently by the system and also has the lowest priority. The data preprocessing system will be interrupted by signals from the high-priority signal preprocessing system. Each time it is interrupted, the data preprocessing system will use time as the scale based on the critical point information before and after the interruption, and linearly distribute the weights to the two processes before and after. At the same time, the inertial information at the interruption point is calculated based on the data, and the inertial information at the interruption point enters the IMU data preprocessing system to obtain the inertial information of a complete cycle.

5. A method for seamless indoor and outdoor positioning of a mobile phone according to claim 2, characterized in that: Multiple time scales are set, the fastest is the sampling time of IMU, followed by SLAM, then WIFI, and the slowest is GNSS; the priority of the data preprocessing system is: GNSS>WIFI>SLAM>IMU, where when the GNSS signal exists, the forgetful system time is determined by the GNSS time difference, and when the GNSS signal does not exist, the forgetful system time is determined by the WIFI time difference, which are all linear relationships; Multiple time scales, where for any two, the one with faster sampling time is relative to the one with slower sampling time, and the relative ones are called fast scale and slow scale; Using time alignment, fast-scale data is preprocessed on the slow-time-scale dimension; Get the data information of each sensor at a slow scale; All data are then processed at the slow scale.

6. A method for seamless indoor and outdoor positioning of a mobile phone according to claim 2, characterized in that: The WIFI data preprocessing system includes monitoring of communication state changes of WIFI-RTT data, white noise processing, phase distortion noise processing, multipath effect processing, kernelized ridge regression, and reflection projection operations. The communication state change monitoring signal is transmitted to the positioning solution system. The positioning solution system adds an estimated vector. At this time, the added estimated vector is the phase distortion noise of the mobile phone and all routers, and the estimated vector will replace the phase distortion noise value in the previous state after stabilization. The output of the WIFI data preprocessing system is the WIFI-RTT observation Jacobi matrix, residual and monitoring signal after noise reduction processing and reflection projection.

7. A method for seamless indoor and outdoor positioning of a mobile phone according to claim 2, characterized in that: The image data preprocessing system performs visual calibration, marginalization, local parameterization, image equalization, FAST feature point extraction, elimination, calculation, and LK optical flow tracking to obtain image tracking feature points, reset signals, and reprojected matching point information to participate in the position solving work of the positioning solving system.

8. A method for seamless indoor and outdoor positioning of a mobile phone according to claim 2, characterized in that: The GNSS data preprocessing system corrects the pseudo-moment through data information, and then obtains the Jacobi equation and residual information of real-time PPP positioning to participate in the position solution work of the positioning solution system.

9. A method for seamless indoor and outdoor positioning of a mobile phone according to claim 2, characterized in that: The positioning solution system needs to estimate the speed and posture of all GNSS, WIFI, and camera signals at the time of occurrence, as well as the phase distortion noise of the mobile phone and router.

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