Indoor and outdoor integrated positioning method and system based on multi-sensor fusion

Abstract

The invention relates to an indoor and outdoor integrated positioning method and system based on multi-sensor fusion, and the method comprises the steps: collecting positioning signals of an indoor positioning system and an outdoor positioning system through a receiving end of current equipment, executing a network judgment process, and selecting an optimal positioning network; the network judgment process comprises the following steps: switching and selecting a positioning scheme according to the positioning signal quality of the indoor positioning system and the outdoor positioning system; when the signals of the indoor positioning system and the outdoor positioning system are lost, predicting the position change of the current equipment by adopting a pre-trained neural network model according to the acceleration information and the angular velocity information of the IMU reference system; and when entering the indoor and outdoor boundary area, carrying out fusion positioning based on the indoor positioning system and the outdoor positioning system. Compared with the prior art, the method has the advantages that the positioning precision and robustness are improved, indoor and outdoor seamless navigation is realized, and the situation that signals are completely lost can be handled.

Description

Technical Field

[0001] The present invention relates to the field of positioning technology, and in particular to an indoor and outdoor integrated positioning method and system based on multi-sensor fusion. Background Art

[0002] In modern society, navigation and location determination technology has become an indispensable part of people's daily life and industrial production. In the prior art, outdoor navigation systems mainly rely on GNSS (Global Navigation Satellite System). GNSS technology performs well in outdoor environments and can provide high positioning accuracy, but in indoor, underground or severe signal obstruction environments, GNSS signals will be significantly affected, resulting in a significant decrease in positioning accuracy. To cope with this situation, indoor positioning technology is widely used, such as WIFI, Bluetooth, UWB and other positioning technologies. However, the coverage of indoor positioning signals is limited and it is difficult to apply them in a large range of outdoor environments. In order to solve the limitations of indoor and outdoor positioning technologies, the prior art usually adopts a multi-sensor fusion method, such as fusing multiple indoor and outdoor positioning data to improve positioning accuracy and system robustness. However, the existing multi-sensor fusion method has the following defects:

[0003] Inconsistent positioning accuracy: When entering indoors from outdoors or going out from indoors, the difference in the characteristics of indoor and outdoor positioning signals causes sudden changes in positioning accuracy, affecting the continuity and reliability of the navigation system.

[0004] Lack of effective network switching mechanism: Existing multi-sensor fusion methods lack an effective network switching mechanism when dealing with positioning switching in different environments, which easily leads to ping-pong effect, further exacerbating the positioning error.

[0005] Inadequate signal loss handling: When both indoor and outdoor positioning signals are unavailable, existing technologies lack effective countermeasures, resulting in the system being unable to provide reliable location information support. Summary of the invention

[0006] The purpose of the present invention is to provide an indoor and outdoor integrated positioning method and system based on multi-sensor fusion in order to overcome the defects of the above-mentioned prior art such as inconsistent positioning accuracy, lack of effective network switching mechanism and insufficient signal loss processing.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] An indoor and outdoor integrated positioning method based on multi-sensor fusion is used in an indoor and outdoor environment with an indoor positioning system and an outdoor positioning system, wherein the outdoor positioning system includes a GNSS system and an IMU reference system, and the method includes the following steps:

[0009] The receiving end of the current device collects the positioning signals of the indoor positioning system and the outdoor positioning system, and executes the network judgment process to select the best positioning network;

[0010] The network decision process includes:

[0011] According to the positioning signal quality of the indoor positioning system and the outdoor positioning system, switch to select a positioning solution based on the indoor positioning system, a fusion positioning solution based on the indoor positioning system and the outdoor positioning system, and a positioning solution based on the outdoor positioning system;

[0012] When the signals of both the indoor positioning system and the outdoor positioning system are lost, the pre-trained neural network model is used to predict the position change of the current device based on the acceleration information and angular velocity information of the IMU reference system;

[0013] When the current position of the device enters the indoor and outdoor boundary area, fusion positioning is performed based on the indoor positioning system and the outdoor positioning system.

[0014] Furthermore, according to the positioning signal quality of the indoor positioning system and the outdoor positioning system, the specific process of switching and selecting the positioning solution includes:

[0015] When the number of available positioning satellites of the GNSS system is less than the preset minimum number threshold, the HDOP value is greater than the preset HDOP maximum value; and the RSSI value of the base station of the outdoor positioning system is less than the lower limit and greater than or equal to the preset optimal threshold, positioning is performed based on the indoor positioning system;

[0016] When the number of available positioning satellites of the GNSS system is between the preset minimum number threshold and the maximum number threshold, and the HDOP value is less than the HDOP maximum value; and the RSSI value of the base station of the indoor positioning system is less than the lower limit and greater than or equal to the preset optimal threshold, fusion positioning is performed based on the indoor positioning system and the outdoor positioning system;

[0017] When the available positioning satellites of the GNSS system are greater than the maximum number threshold, the HDOP value is less than the HDOP maximum value, and the base station RSSI value of the indoor positioning system is less than the lower limit number and less than the preset optimal threshold, the outdoor positioning system is selected for positioning.

[0018] Furthermore, in the process of switching and selecting the positioning scheme, a switching count is also performed. If the switching count number is greater than a preset switching threshold, the positioning scheme is reset and the switching count is cleared.

[0019] Furthermore, the training process of the neural network model includes:

[0020] The acceleration information and angular velocity information collected by the IMU reference system are used as the input of the neural network model. The position information obtained by switching the positioning scheme according to the positioning signal quality of the indoor positioning system and the outdoor positioning system is used as the output of the neural network model. A training data set is constructed to train the neural network model.

[0021] Furthermore, the neural network model is a BP neural network.

[0022] Furthermore, in the indoor and outdoor boundary area, a federal Kalman filter is used to perform fusion positioning based on the indoor positioning system and the outdoor positioning system.

[0023] Furthermore, the federal Kalman filter includes a first sub-filter, a second sub-filter and a main filter, wherein the first sub-filter performs filtering and fusion according to the positioning signals of the GNSS system and the IMU reference system, obtains a first positioning result and outputs it to the main filter; the second sub-filter performs filtering and fusion according to the positioning signals of the indoor positioning system and the IMU reference system, obtains a second positioning result and outputs it to the main filter;

[0024] The main filter performs weighted average fusion according to the positioning signal of the IMU reference system, the first positioning result and the second positioning result, and generates a first adjustment parameter and a second adjustment parameter, feeds the first adjustment parameter back to the first sub-filter for parameter optimization, and feeds the second adjustment parameter back to the second sub-filter for parameter optimization.

[0025] An indoor and outdoor integrated positioning system based on multi-sensor fusion, comprising an indoor positioning system, an outdoor positioning system and a main processor unit, wherein the outdoor positioning system comprises a GNSS system and an IMU reference system;

[0026] The main processor unit is used to execute the steps of the indoor and outdoor integrated positioning method based on multi-sensor fusion as described above.

[0027] Furthermore, the indoor positioning system is a Bluetooth AOA device, which is used to obtain direction information of relative position to achieve positioning in indoor environment.

[0028] Furthermore, the GNSS system includes a GNSS receiver for acquiring global navigation satellite system signals to achieve positioning in an outdoor environment;

[0029] The IMU reference system includes an accelerometer and a gyroscope to provide displacement and attitude change information when the global navigation satellite system signal is unstable or lost.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] (1) Improving positioning accuracy and robustness: During the positioning process, the present invention first determines the positioning signal quality of the indoor positioning system and the outdoor positioning system. After determining the number of available GNSS satellite signals, the GNSS horizontal precision factor HDOP value, the RSSI value of the indoor base station and other parameters, the optimal positioning network is selected to ensure the quality of the positioning signal.

[0032] It is also proposed to use a federated Kalman filter to fuse the positioning signals of the indoor positioning system and the outdoor positioning system. By utilizing the multi-sensor fusion capability of the federated Kalman filter, high-precision position determination can be achieved in the indoor and outdoor boundary areas, effectively avoiding the problem of decreased positioning accuracy caused by the performance differences of a single technology in different environments, and improving the robustness of the system.

[0033] (2) Realizing seamless indoor and outdoor navigation: The present invention proposes a positioning solution that integrates the indoor positioning system and the outdoor positioning system. This can achieve a smooth transition of positioning data when switching between indoor and outdoor environments, avoiding the problem of sudden changes in accuracy, realizing seamless indoor and outdoor navigation, and improving user experience.

[0034] (3) Dealing with the situation of complete signal loss: When all positioning signals are lost, the simulation experiment scheme proposed in the present invention of using BP neural network combined with historical data for position prediction can maintain the continuity of navigation services and solve the problem of position prediction when the signal is completely lost in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the overall process of an indoor and outdoor integrated positioning method based on multi-sensor fusion provided in an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of a network switching decision process provided in an embodiment of the present invention;

[0037] Figure 3 The figure is a schematic diagram of a data fusion process of a federated Kalman filter provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, 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 part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0039] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0040] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0041] In the description of the present invention, it should be noted that the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0042] Example 1

[0043] like Figure 1 As shown, this embodiment provides an indoor and outdoor integrated positioning method based on multi-sensor fusion, which is used in an indoor and outdoor environment with an indoor positioning system and an outdoor positioning system. The outdoor positioning system includes a GNSS system and an IMU reference system. The method includes the following steps:

[0044] The receiving end of the current device collects the positioning signals of the indoor positioning system and the outdoor positioning system, and executes the network judgment process to select the best positioning network;

[0045] The network judgment process includes:

[0046] According to the positioning signal quality of the indoor positioning system and the outdoor positioning system, switch to select a positioning solution based on the indoor positioning system, a fusion positioning solution based on the indoor positioning system and the outdoor positioning system, and a positioning solution based on the outdoor positioning system;

[0047] When the signals of both the indoor positioning system and the outdoor positioning system are lost, the pre-trained neural network model is used to predict the position change of the current device based on the acceleration information and angular velocity information of the IMU reference system;

[0048] When the current device position enters the indoor and outdoor boundary area, fusion positioning is performed based on the indoor positioning system and the outdoor positioning system.

[0049] This means that this solution divides the indoor and outdoor positioning process into the network switching stage, the signal loss positioning prediction, and the indoor and outdoor boundary areas for separate processing, which will be described in detail below.

[0050] 1. Network switching stage

[0051] In the network switching phase, the network switching problem is modeled as a multi-attribute decision-making problem, covering network switching discovery, network selection decision and elimination of the ping-pong effect; comprehensively considering parameters such as the number of available GNSS satellite signals, the GNSS horizontal precision factor HDOP value, and the RSSI value of the indoor base station to determine the current device positioning status. When the receiving end receives positioning signals from two heterogeneous networks at the same time, the network decision process is triggered to select the best positioning network.

[0052] The specific judgment process is:

[0053] When the number of available positioning satellites of the GNSS system is less than the preset minimum number threshold, the HDOP value is greater than the preset HDOP maximum value; and the RSSI value of the base station of the outdoor positioning system is less than the lower limit and greater than or equal to the preset optimal threshold, positioning is performed based on the indoor positioning system;

[0054] When the available positioning satellites of the GNSS system are between the preset minimum number threshold and the maximum number threshold, and the HDOP value is less than the maximum HDOP value; and the base station RSSI value of the indoor positioning system is less than the lower limit and greater than or equal to the preset optimal threshold, fusion positioning is performed based on the indoor positioning system and the outdoor positioning system;

[0055] When the number of available positioning satellites of the GNSS system is greater than the maximum number threshold, the HDOP value is less than the maximum HDOP value, and the base station RSSI value of the indoor positioning system is less than the lower limit and the number is less than the preset optimal threshold, the outdoor positioning system is selected for positioning.

[0056] When all the above conditions are not met, the preset default mode is used for positioning.

[0057] Preferably, Figure 2 As shown, in the process of switching and selecting the positioning scheme, a switching count is also performed. If the switching count number is greater than the preset switching threshold, the positioning scheme is reset, the default mode is used for positioning, and the switching count is cleared to realize a cyclic switching process.

[0058] 2. Positioning prediction of signal loss

[0059] The above neural network model may be a BP neural network or other network model, and is not limited thereto.

[0060] This embodiment proposes a positioning prediction method based on BP neural network, which is used to predict the position through historical data when both GNSS and indoor base station signals are lost to ensure the continuity of the system. When the tag can meet the above positioning conditions to achieve positioning, the motion mode of the positioned device is learned from the historical data through the neural network, the acceleration information and angular velocity information of the IMU are used as the input of the neural network model, and the position information solved by indoor and outdoor fusion positioning is used as the output of the neural network model to train the model. When the positioning signal is interrupted, the neural network is used to predict the position change of the device to ensure the stability and continuity of the position information.

[0061] 3. Indoor and outdoor areas

[0062] In the indoor and outdoor boundary areas, the Federated Kalman Filter (FKF) is used to perform fusion positioning based on the indoor positioning system and the outdoor positioning system. FKF can effectively combine the data of multiple sensors, make full use of the advantages of different technologies, and improve the positioning accuracy and system robustness. FKF optimizes the combination of information from multiple sensors by weighted averaging, thereby achieving a smooth transition of indoor and outdoor positioning data and avoiding sudden changes in accuracy. The parameter settings of FKF need to be adjusted according to the actual application scenario to ensure the best filtering effect.

[0063] Specifically, Figure 3 As shown, the federal Kalman filter includes a first sub-filter, a second sub-filter and a main filter. The first sub-filter performs filtering and fusion according to the positioning signals of the GNSS system and the IMU reference system to obtain a first positioning result which is output to the main filter; the second sub-filter performs filtering and fusion according to the positioning signals of the indoor positioning system and the IMU reference system to obtain a second positioning result which is output to the main filter;

[0064] The main filter performs weighted average fusion according to the positioning signal of the IMU reference system, the first positioning result and the second positioning result, and generates a first adjustment parameter and a second adjustment parameter, feeds the first adjustment parameter back to the first sub-filter for parameter optimization, and feeds the second adjustment parameter back to the second sub-filter for parameter optimization.

[0065] Example 2

[0066] This embodiment provides an indoor and outdoor integrated positioning system based on multi-sensor fusion, including an indoor positioning system, an outdoor positioning system and a main processor unit, and the outdoor positioning system includes a GNSS system and an IMU reference system;

[0067] The main processor unit is used to execute the steps of an indoor and outdoor integrated positioning method based on multi-sensor fusion as described in Example 1.

[0068] Specifically, the indoor positioning system is a Bluetooth AOA device, the GNSS system includes a GNSS receiver, and the IMU reference system includes an accelerometer and a gyroscope;

[0069] The main processor unit (MCU / MPU) is responsible for processing data from various sensors, executing fusion algorithms, processing the original positioning information and outputting the final positioning results. The main processor receives the raw data from each sensor and applies fusion algorithms to improve the accuracy and robustness of the overall positioning.

[0070] GNSS receiver: used to obtain global navigation satellite system (GNSS) signals and provide global coordinate information. GNSS receivers can provide high-precision location data in an open environment and provide basic location information for the overall fusion system.

[0071] Inertial Measurement Unit (IMU): An inertial measurement unit usually contains an accelerometer and a gyroscope, which is used to provide displacement and attitude change information in a short period of time when the GNSS signal is unstable or lost. After fusion, the IMU data can make up for the shortcomings of GNSS in an obstructed environment.

[0072] Bluetooth AOA devices: Bluetooth AOA devices are used to obtain directional information of relative position, which usually helps to accurately locate the short distance in indoor environments. Through the AOA information of multiple base stations, the system can perform multilateral positioning, thereby improving the positioning capability in areas where GNSS signals cannot cover.

[0073] The data obtained by the above-mentioned multiple navigation technologies are input into the main processor unit. The main processor unit determines that the current positioning is in a certain judgment state through the received base station data. When it is in the outdoor positioning state, the GNSS / IMU fusion positioning solution is adopted; when it is in the indoor and outdoor connection state, the main processor unit uses FKF to optimize the combination of GNSS / AOA / IMU information, thereby achieving a smooth transition of indoor and outdoor positioning data and avoiding sudden changes in accuracy. The parameter setting of FKF needs to be adjusted according to the actual application scenario to ensure the best filtering effect. When all positioning signals are lost, the BP neural network is used in combination with historical data for position prediction. The BP neural network needs to be trained so that it can predict future positions based on historical position data. During the training process, historical data from the positioning process is used.

[0074] It should be noted that the specific content and beneficial effects of the system of the present application can be found in the above-mentioned method embodiment, which will not be repeated here.

[0075] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. An indoor and outdoor integrated positioning method based on multi-sensor fusion, characterized in that: Used in an indoor or outdoor environment with an indoor positioning system and an outdoor positioning system, wherein the outdoor positioning system includes a GNSS system and an IMU reference system, the method includes the following steps: The receiving end of the current device collects the positioning signals of the indoor positioning system and the outdoor positioning system, and executes the network judgment process to select the best positioning network; The network decision process includes: According to the positioning signal quality of the indoor positioning system and the outdoor positioning system, switch to select a positioning solution based on the indoor positioning system, a fusion positioning solution based on the indoor positioning system and the outdoor positioning system, and a positioning solution based on the outdoor positioning system; When the signals of both the indoor positioning system and the outdoor positioning system are lost, the pre-trained neural network model is used to predict the position change of the current device based on the acceleration information and angular velocity information of the IMU reference system; When the position of the current device enters the indoor and outdoor boundary area, fusion positioning is performed based on the indoor positioning system and the outdoor positioning system.

2. The method for indoor and outdoor integrated positioning based on multi-sensor fusion according to claim 1, characterized in that: According to the positioning signal quality of the indoor positioning system and the outdoor positioning system, the specific process of switching and selecting the positioning solution includes: When the number of available positioning satellites of the GNSS system is less than the preset minimum number threshold, the HDOP value is greater than the preset HDOP maximum value; and the RSSI value of the base station of the outdoor positioning system is less than the lower limit and greater than or equal to the preset optimal threshold, positioning is performed based on the indoor positioning system; When the number of available positioning satellites of the GNSS system is between the preset minimum number threshold and the maximum number threshold, and the HDOP value is less than the HDOP maximum value; and the RSSI value of the base station of the indoor positioning system is less than the lower limit and greater than or equal to the preset optimal threshold, fusion positioning is performed based on the indoor positioning system and the outdoor positioning system; When the available positioning satellites of the GNSS system are greater than the maximum number threshold, the HDOP value is less than the HDOP maximum value, and the base station RSSI value of the indoor positioning system is less than the lower limit number and less than the preset optimal threshold, the outdoor positioning system is selected for positioning.

3. The method for indoor and outdoor integrated positioning based on multi-sensor fusion according to claim 2, characterized in that: In the process of switching and selecting the positioning scheme, a switching count is also performed. If the switching count number is greater than a preset switching threshold, the positioning scheme is reset and the switching count is cleared.

4. The method for indoor and outdoor integrated positioning based on multi-sensor fusion according to claim 1, characterized in that: The training process of the neural network model includes: The acceleration information and angular velocity information collected by the IMU reference system are used as the input of the neural network model. The position information obtained by switching the positioning scheme according to the positioning signal quality of the indoor positioning system and the outdoor positioning system is used as the output of the neural network model. A training data set is constructed to train the neural network model.

5. The method for indoor and outdoor integrated positioning based on multi-sensor fusion according to claim 1, characterized in that: The neural network model is a BP neural network.

6. The method for indoor and outdoor integrated positioning based on multi-sensor fusion according to claim 1, characterized in that: The method adopts a federal Kalman filter to perform fusion positioning based on an indoor positioning system and an outdoor positioning system.

7. The method for indoor and outdoor integrated positioning based on multi-sensor fusion according to claim 6, characterized in that: The federal Kalman filter includes a first sub-filter, a second sub-filter and a main filter, wherein the first sub-filter performs filtering fusion according to the positioning signals of the GNSS system and the IMU reference system to obtain a first positioning result and output it to the main filter; The second sub-filter performs filtering and fusion according to the positioning signals of the indoor positioning system and the IMU reference system to obtain a second positioning result and output it to the main filter; The main filter performs weighted average fusion according to the positioning signal of the IMU reference system, the first positioning result and the second positioning result, and generates a first adjustment parameter and a second adjustment parameter, feeds the first adjustment parameter back to the first sub-filter for parameter optimization, and feeds the second adjustment parameter back to the second sub-filter for parameter optimization.

8. An indoor and outdoor integrated positioning system based on multi-sensor fusion, characterized in that: It includes an indoor positioning system, an outdoor positioning system and a main processor unit, wherein the outdoor positioning system includes a GNSS system and an IMU reference system; The main processor unit is used to execute the steps of an indoor and outdoor integrated positioning method based on multi-sensor fusion as described in any one of claims 1-7.

9. The indoor and outdoor integrated positioning system based on multi-sensor fusion according to claim 8, characterized in that: The indoor positioning system is a Bluetooth AOA device, which is used to obtain direction information of relative position and realize positioning in indoor environment.

10. The indoor and outdoor integrated positioning system based on multi-sensor fusion according to claim 8, characterized in that: The GNSS system includes a GNSS receiver for acquiring global navigation satellite system signals to achieve positioning in an outdoor environment; The IMU reference system includes an accelerometer and a gyroscope to provide displacement and attitude change information when the global navigation satellite system signal is unstable or lost.

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