Collaborative relative positioning method and system for portable mobile terminal

Abstract

The invention discloses a portable mobile terminal collaborative relative positioning method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the preprocessing of the multi-source data; wherein the multi-source data comprises an RTK differential value, a GNSS observed quantity and a Bluetooth observed quantity; gross error detection is carried out on the preprocessed multi-source data; constructing a fusion observation equation according to the RTK differential value after gross error detection, the GNSS observed quantity and the Bluetooth observed quantity, and obtaining an estimated value of a positioning parameter through a Kalman filtering algorithm according to the fusion observation equation; and carrying out relative position calculation according to the estimated value of the positioning parameter to obtain a relative positioning result of the portable mobile terminal. By adopting the technical scheme of the invention, the relative positioning with higher precision and moderate cost is realized on the portable mobile terminal so as to meet the high-precision relative positioning requirement under diversified application scenes.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless positioning and navigation technology, and in particular relates to a method and system for collaborative relative positioning of a portable mobile terminal. Background Art

[0002] Wearable mobile terminals are electronic devices that can be worn directly on the human body for information processing and transmission, usually including smart watches, smart glasses, health monitoring equipment, etc. Due to their portability, real-time and intelligent features, these terminals are widely used in health monitoring, sports tracking, outdoor navigation, emergency rescue and other fields. With the continuous expansion of application scenarios, the requirements for positioning accuracy of wearable mobile terminals are gradually increasing, especially in scenarios that require precise relative positions, such as athlete tracking, augmented reality, close social interaction and other scenarios.

[0003] In these scenarios, navigation systems that rely solely on absolute position can no longer meet the needs; wearable terminals urgently need to be able to provide centimeter-level accuracy relative positioning functions to achieve accurate interaction with surrounding devices, users or key objects. For example, in indoor environments or crowded scenes, traditional satellite positioning technology faces accuracy limitations due to signal obstruction and multipath effects, while relative positioning technologies such as Bluetooth and RTK can effectively provide short-range relative position data. Therefore, the precise relative positioning method based on wearable terminals is of great significance in terms of security, interactivity, data reliability, etc., and has become a key technology to improve user experience and application performance.

[0004] Bluetooth positioning technology is a method of using Bluetooth wireless communication signals to measure the relative position of devices. It mainly relies on parameters such as received signal strength, arrival time difference, and arrival angle to estimate the position. Bluetooth positioning has the characteristics of low power consumption, low cost, and small device size, making it widely used in short-distance indoor positioning and ranging scenarios. The current Bluetooth positioning technology mainly uses the following methods: Signal strength positioning (RSSI): This method estimates the relative distance between devices based on the correlation between the received signal strength indication (RSSI) and the distance. However, since the signal strength is easily affected by environmental factors, its positioning accuracy is usually low and prone to large errors; Time difference of arrival positioning (TDoA): The location is calculated by recording the arrival time difference of signals by multiple Bluetooth base stations. This method requires the cooperation and synchronization of multiple base stations, so the deployment cost is high, but it can achieve higher positioning accuracy in specific environments; Angle of arrival positioning (AoA): This method uses the arrival angle of the Bluetooth signal for positioning, which is suitable for the AoA feature in Bluetooth 5.1 technology. This method can achieve higher positioning accuracy in an environment with less multipath effect, but it has higher requirements for antenna array configuration.

[0005] Although Bluetooth positioning technology has certain application advantages in short-range environments, its positioning accuracy is greatly affected by environmental changes, and there is instability when positioning in dynamic environments or within a large range. Therefore, Bluetooth positioning technology is difficult to meet application scenarios with high requirements for high precision and dynamics.

[0006] RTK (Real-Time Kinematic) is a technology that uses GNSS to achieve high-precision positioning. Through the differential correction data transmitted between the base station and the mobile station, the satellite positioning error is corrected in real time, thereby achieving centimeter-level positioning accuracy. The RTK positioning system mainly relies on the collaborative work of the base station and the mobile station, and is widely used in scenarios that require high-precision dynamic positioning, such as engineering measurement, autonomous driving, precision agriculture, etc. Its technical characteristics are as follows: High precision: RTK's differential algorithm based on carrier phase observations can effectively eliminate common errors and significantly improve positioning accuracy. Generally, RTK can achieve centimeter-level relative positioning accuracy within the effective range around the base station; Real-time: The RTK system has a fast convergence speed and can achieve high-precision positioning in a short time, which is very suitable for scenarios that require real-time dynamic positioning; Relative positioning: RTK technology is mainly used for relative positioning, and the positioning error is eliminated by solving the relative position between the base station and the mobile station. Therefore, the position accuracy of the mobile station is highly dependent on the accuracy of the base station position. When the base station is fixed and the network coverage is stable, RTK can provide reliable high-precision relative positioning services.

[0007] However, RTK positioning has high requirements for the base station and GNSS signal environment. When the distance between the base station and the mobile station increases, or in an environment with severe signal shielding and multipath interference, the accuracy and stability of RTK positioning will be significantly affected. In addition, RTK equipment is expensive, and its application is subject to certain restrictions when the signal environment is complex. It is not suitable for positioning needs indoors or in areas with unstable signals.

[0008] Existing wearable mobile terminal positioning methods mostly rely on single positioning technologies such as Bluetooth or GNSS (such as RTK). Due to environmental influences or the distance between devices, it is difficult to simultaneously meet the needs of short-range high-precision and large-scale real-time positioning. Bluetooth signals are susceptible to interference in dynamic and complex environments (such as indoor scenes with obvious multipath effects), resulting in unstable positioning; RTK technology has significantly reduced accuracy in highly obstructed and weak signal environments, and cannot stably provide high-precision location data. Existing high-precision positioning systems mostly rely on fixed base stations and complex networks, resulting in high deployment costs for wearable mobile terminals and limited application scenarios. Summary of the invention

[0009] The technical problem to be solved by the present invention is to provide a method and system for collaborative relative positioning of a portable mobile terminal, so as to achieve higher-precision and low-cost relative positioning on a portable mobile terminal to meet the high-precision relative positioning requirements in various application scenarios.

[0010] To achieve the above object, the present invention adopts the following technical solution:

[0011] A portable mobile terminal collaborative relative positioning method, comprising:

[0012] Step S1, obtaining multi-source data and pre-processing the multi-source data; wherein the multi-source data includes: RTK differential value, GNSS observation value and Bluetooth observation value

[0013] Step S2, performing gross error detection on the preprocessed multi-source data;

[0014] Step S3, constructing a fusion observation equation according to the RTK differential value, GNSS observation value and Bluetooth observation value after gross error detection;

[0015] Step S4: according to the fusion observation equation, obtain the estimated value of the positioning parameter through the Kalman filter algorithm;

[0016] Step S5: perform relative position calculation based on the estimated values ​​of the positioning parameters to obtain a relative positioning result of the portable mobile terminal.

[0017] Preferably, in step S1, preprocessing the multi-source data includes removing noise and outliers in the signal.

[0018] Preferably, the estimated values ​​of the positioning parameters are dynamically adjusted according to the environmental signal strength and the motion state. When the device is in a high-speed motion or signal-limited environment, the estimated values ​​of the positioning parameters are adjusted in real time to adapt to environmental changes.

[0019] Preferably, a distributed base station deployment method is adopted to deploy RTK base stations within a predetermined range to provide differential data support.

[0020] The present invention also provides a portable mobile terminal cooperative relative positioning system, comprising:

[0021] The acquisition device is used to acquire multi-source data and pre-process the multi-source data; wherein the multi-source data includes: RTK differential value, GNSS observation value and Bluetooth observation value

[0022] A first processing device, used for performing gross error detection on the pre-processed multi-source data;

[0023] A fusion device, used for constructing a fusion observation equation according to the RTK differential value, GNSS observation value and Bluetooth observation value after gross error detection;

[0024] A second processing device is used to obtain an estimated value of the positioning parameter through a Kalman filter algorithm according to the fusion observation equation;

[0025] The solution device is used to perform relative position solution according to the estimated value of the positioning parameter to obtain the relative positioning result of the portable mobile terminal.

[0026] Preferably, the acquisition device preprocesses the multi-source data including removing noise and abnormal values ​​in the signal.

[0027] Preferably, the estimated values ​​of the positioning parameters are dynamically adjusted according to the environmental signal strength and the motion state. When the device is in a high-speed motion or signal-limited environment, the estimated values ​​of the positioning parameters are adjusted in real time to adapt to environmental changes.

[0028] Preferably, a distributed base station deployment method is adopted to deploy RTK base stations within a predetermined range to provide differential data support.

[0029] The present invention integrates the distance information of the Bluetooth signal with the RTK information to obtain high-precision relative position information of the wearable terminal, meet the application requirements of different scenarios, and realize centimeter-level precision positioning of the portable mobile terminal within a short distance, so that the portable mobile terminal can still obtain a relatively stable and reliable high-precision relative position under limited signal or complex environment conditions, so as to meet the application scenarios that require accurate relative position such as sports tracking and social interaction. In addition, the present invention reduces the dependence on fixed base stations, reduces deployment and hardware costs, and provides a more economical solution for the popularization and application of high-precision positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0031] Figure 1 This is a flow chart of a method for collaborative relative positioning of portable mobile terminals according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of 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 scope of protection of the present invention.

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] Embodiment 1:

[0035] The embodiment of the present invention provides a portable mobile terminal collaborative relative positioning method, which realizes the data processing fusion of Bluetooth communication technology and satellite navigation positioning technology at the observation value level. Compared with the traditional method of positioning the traditional portable mobile terminal only through GNSS, it adds GNSS observation information and effectively utilizes the observation value of Bluetooth communication, thereby improving the relative positioning precision and accuracy. Figure 1 As shown, a portable mobile terminal collaborative relative positioning method includes:

[0036] Step S1: Data preprocessing

[0037] Acquire multi-source data and pre-process the multi-source data to remove noise and outliers in the signal; the multi-source data includes: RTK differential values, GNSS observations and Bluetooth observations.

[0038] Step S2: Gross error detection

[0039] The gross error detection algorithm is used to check the quality of the preprocessed multi-source data; by setting thresholds or detecting abnormal changes, possible gross error observations are identified and eliminated to reduce the error sources in the data.

[0040] Step S3: Fusion of observation equations

[0041] The fusion observation equation is constructed based on the RTK differential value, GNSS observation and Bluetooth observation after gross error detection.

[0042] Set the RTK differential value to Δr RTK , the GNSS observation is r GNSS , the Bluetooth observation quantity is r BT

[0043] The fused observation equation is

[0044] y=Hx+v

[0045] Among them, y is the observation vector, which contains the observation values ​​of RTK, GNSS and Bluetooth; H is the observation matrix, which is related to the transformation of the observation; x is the state vector, which represents the positioning parameters to be estimated; v is the noise term, which represents the observation error.

[0046] Set the weight of the RTK differential value to w RTK , GNSS observation weight w GNSS , the Bluetooth observation weight is w BT , the fusion equation is as follows.

[0047]

[0048] Step S4: Kalman filtering

[0049] According to the fusion observation equation, the estimated value of the positioning parameter is obtained through the Kalman filter algorithm.

[0050] Fusion of the previous step fusion equation y k , including RTK differential values, GNSS observations and Bluetooth observations; these observations are directly substituted into the Kalman filter formula to estimate the positioning parameters; the estimated values ​​of the positioning parameters are obtained through Kalman filtering

[0051] The prediction step formula is as follows

[0052]

[0053] The update step formula is as follows

[0054] K k =P k|k-1 H T (HP k|k-1 H T +R k ) -1

[0055]

[0056] P k|k =(IK k H)P k|k-1

[0057] in, is the state estimation matrix, P k-1|k-1 is the covariance matrix, F k is the state transfer matrix, Q k is the process noise covariance.

[0058] Step S5: relative position calculation

[0059] The relative position is calculated based on the estimated values ​​of the positioning parameters to obtain the relative positioning result of the portable mobile terminal.

[0060] Relative position p rel is the current mobile terminal position P K|K For the difference in the reference point position p0, the calculation formula is as follows:

[0061] p rel =P K|K -p0

[0062] Among them, P K|K=[x k ,y k , z k ] T is the current estimated location of the mobile terminal, x k ,y k ,z k is the coordinate value of the mobile terminal in the current estimated position coordinate system, p0 = [x0, y0, z0] T is the known position of the reference point (reference point), and x0, y0, z0 are the coordinate values ​​of the mobile terminal in the current estimated position coordinate system.

[0063] The relative position solution result is the positioning result of the mobile terminal relative to the reference point p rel .

[0064] Furthermore, the embodiment of the present invention adopts an intelligent power consumption management strategy, automatically switching between Bluetooth and RTK modules according to the environment and positioning accuracy requirements, thereby reducing the overall power consumption of the system. In low-precision requirements or static environments, the low-power Bluetooth module is preferentially activated to achieve positioning, while in dynamic environments or high-precision requirements, it automatically switches to the RTK module to obtain higher-precision positioning data, effectively extending the battery life of the device.

[0065] Furthermore, the distributed base station deployment scheme allows the deployment of RTK base stations within a certain range to provide differential data support. The portable mobile terminal establishes a connection with the nearest base station via Bluetooth, receives differential signals, and achieves centimeter-level precision positioning without fixed base stations; it reduces dependence on fixed base stations, reduces system deployment costs, and broadens application scenarios.

[0066] Furthermore, the estimated values ​​of the positioning parameters are dynamically adjusted according to the environmental signal strength and motion status. When the device is in a high-speed motion or signal-limited environment, the estimated values ​​of the positioning parameters are adjusted in real time to adapt to environmental changes, thereby maintaining high-precision and stable relative positioning under dynamic conditions, which is suitable for the application requirements of portable mobile terminals.

[0067] The present invention combines the advantages of Bluetooth and RTK positioning technology to achieve high-precision, low-power, and anti-interference relative positioning, meeting the positioning needs in complex environments and effectively solving the shortcomings of existing technologies in terms of positioning accuracy, power consumption management, anti-interference, and deployment flexibility.

[0068] The embodiments of the present invention have the following beneficial effects:

[0069] (1) Realizing high-precision relative positioning: The embodiments of the present invention achieve centimeter-level relative positioning accuracy in a short-range environment by integrating Bluetooth and RTK. Compared with single Bluetooth or RTK technology, the present invention can provide higher positioning accuracy in complex environments and meet the needs of portable mobile terminals for high-precision relative positioning.

[0070] (2) Improving positioning stability and anti-interference capability: In scenarios with obvious signal shielding and multipath effects, the present invention can dynamically adjust positioning parameters and switch to a stable positioning mode, thereby improving the anti-interference and adaptability of the positioning system and ensuring positioning stability in dynamic and complex environments.

[0071] (3) Reduce power consumption and extend battery life: Through the low-power switching strategy, the present invention intelligently switches between Bluetooth and RTK modules according to the positioning accuracy requirements and environmental conditions, thereby reducing the overall power consumption of the system. Compared with traditional high-precision positioning technology, the present invention effectively extends the battery life of the wearable portable mobile terminal while achieving precise positioning, which is suitable for long-term operation requirements.

[0072] (4) Reduce dependence on reference stations and reduce deployment costs: The present invention introduces a distributed reference station deployment solution, which enables high-precision relative positioning without fixed reference stations, and is suitable for dynamic and large-scale application scenarios. Compared with the traditional RTK technology that relies on fixed base stations, the present invention reduces the dependence on reference stations, thereby reducing system deployment and maintenance costs and expanding application scenarios.

[0073] (5) Enhanced positioning performance in dynamic environments: The present invention automatically adjusts positioning parameters according to environmental changes and movement status, so that high-precision positioning services can still be provided during user movement, meeting the needs of portable mobile terminals in scenarios such as motion tracking and interactive experience. Compared with the prior art, the present invention significantly improves the positioning accuracy and stability of the system in dynamic scenarios.

[0074] To sum up, the embodiments of the present invention realize a high-precision, low-power, stable and reliable relative positioning solution on a portable mobile terminal through the fusion positioning technology of Bluetooth and RTK, effectively overcoming the shortcomings of the existing technology in terms of accuracy, stability, power consumption and deployment cost, and has significant technical advantages and broad application prospects.

[0075] Embodiment 2:

[0076] The embodiment of the present invention further provides a portable mobile terminal cooperative relative positioning device, comprising:

[0077] The acquisition device is used to acquire multi-source data and pre-process the multi-source data; wherein the multi-source data includes: RTK differential value, GNSS observation value and Bluetooth observation value

[0078] A first processing device, used for performing gross error detection on the pre-processed multi-source data;

[0079] A fusion device, used for constructing a fusion observation equation according to the RTK differential value, GNSS observation value and Bluetooth observation value after gross error detection;

[0080] A second processing device is used to obtain an estimated value of the positioning parameter through a Kalman filter algorithm according to the fusion observation equation;

[0081] The solution device is used to perform relative position solution according to the estimated value of the positioning parameter to obtain the relative positioning result of the portable mobile terminal.

[0082] As an implementation manner of the embodiment of the present invention, the acquisition device preprocesses the multi-source data including removing noise and abnormal values ​​in the signal.

[0083] As an implementation method of an embodiment of the present invention, the estimated value of the positioning parameter is dynamically adjusted according to the environmental signal strength and motion state. When the device is in a high-speed motion or signal-limited environment, the estimated value of the positioning parameter is adjusted in real time to adapt to environmental changes.

[0084] As an implementation method of the embodiment of the present invention, a distributed reference station deployment method is adopted to deploy RTK base stations within a predetermined range to provide differential data support.

[0085] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A portable mobile terminal collaborative relative positioning method, characterized in that: include: Step S1, obtaining multi-source data and pre-processing the multi-source data; wherein the multi-source data includes: RTK differential value, GNSS observation value and Bluetooth observation value Step S2, performing gross error detection on the preprocessed multi-source data; Step S3, constructing a fusion observation equation according to the RTK differential value, GNSS observation value and Bluetooth observation value after gross error detection; Step S4: according to the fusion observation equation, obtain the estimated value of the positioning parameter through the Kalman filter algorithm; Step S5: perform relative position calculation based on the estimated values ​​of the positioning parameters to obtain a relative positioning result of the portable mobile terminal.

2. The portable mobile terminal collaborative relative positioning method according to claim 1, characterized in that: In step S1, preprocessing the multi-source data includes removing noise and outliers in the signal.

3. The portable mobile terminal collaborative relative positioning method according to claim 2, characterized in that: The estimated value of the positioning parameter is dynamically adjusted according to the environmental signal strength and motion status. When the device is in a high-speed motion or signal-limited environment, the estimated value of the positioning parameter is adjusted in real time to adapt to environmental changes.

4. The portable mobile terminal collaborative relative positioning method according to claim 3, characterized in that: A distributed base station deployment method is adopted to deploy RTK base stations within a predetermined range to provide differential data support.

5. A portable mobile terminal cooperative relative positioning system, characterized in that: include: The acquisition device is used to acquire multi-source data and pre-process the multi-source data; wherein the multi-source data includes: RTK differential value, GNSS observation value and Bluetooth observation value A first processing device, used for performing gross error detection on the pre-processed multi-source data; A fusion device, used for constructing a fusion observation equation according to the RTK differential value, GNSS observation value and Bluetooth observation value after gross error detection; A second processing device is used to obtain an estimated value of the positioning parameter through a Kalman filter algorithm according to the fusion observation equation; The solution device is used to perform relative position solution according to the estimated value of the positioning parameter to obtain the relative positioning result of the portable mobile terminal.

6. The portable mobile terminal cooperative relative positioning system according to claim 5, characterized in that: The acquisition device pre-processes the multi-source data including removing noise and abnormal values ​​in the signal.

7. The portable mobile terminal cooperative relative positioning system according to claim 6, characterized in that: The estimated value of the positioning parameter is dynamically adjusted according to the environmental signal strength and motion status. When the device is in a high-speed motion or signal-limited environment, the estimated value of the positioning parameter is adjusted in real time to adapt to environmental changes. The portable mobile terminal cooperative relative positioning system according to claim 7, characterized in that: A distributed base station deployment method is adopted to deploy RTK base stations within a predetermined range to provide differential data support.

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