Indoor navigation positioning method and system combined with multi-source signals

Abstract

The invention provides an indoor navigation and positioning method and system combined with a multi-source signal, and relates to the technical field of communication, the method comprises the following steps: obtaining a group of signal intensity values of the multi-source signal, the multi-source signal comprising signals emitted by a plurality of signal sources, the group of signal intensity values comprises the signal intensity value of each signal source in the plurality of signal sources; the weight of each signal source in the multi-source signal is determined according to the set of signal intensity values and current environment characteristic data, a set of weight values is obtained, and the current environment characteristic data comprises whether there is a shielding object between the target terminal and each signal source, the type of the shielding object and the type of an area where the target terminal is located currently; and according to the group of weight values, fusing the multi-source signals according to a preset algorithm to determine a target positioning result of the target terminal. By implementing the technical scheme provided by the invention, the effect of improving the precision and stability of indoor navigation positioning is achieved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to an indoor navigation and positioning method and system combining multi-source signals. Background Art

[0002] As modern urban buildings become increasingly large and complex, traditional GPS outdoor positioning systems are unable to provide accurate and effective positioning services in indoor environments, which greatly limits the application potential of navigation and IoT technologies indoors. Solutions in related technologies usually use a single signal source, such as Wi-Fi signals or Bluetooth beacon positioning technology. However, a single signal source is easily affected by environmental factors, resulting in reduced accuracy, and has limited coverage. It performs poorly in indoor environments with dense obstructions or severe signal interference. That is, the indoor positioning accuracy of a single signal source is subject to environmental changes, especially in complex electromagnetic environments, where stability is poor. Therefore, it is particularly important to provide a stable indoor navigation and positioning method. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides an indoor navigation and positioning method and system combining multi-source signals.

[0004] In a first aspect, the present application provides an indoor navigation and positioning method combining multi-source signals, including: obtaining a set of signal strength values ​​of multi-source signals, wherein the multi-source signals include signals emitted by multiple signal sources, and a set of signal strength values ​​include the signal strength value of each signal source in the multiple signal sources; determining the weight of each signal source in the multi-source signal based on a set of signal strength values ​​and current environmental characteristic data to obtain a set of weight values, wherein the current environmental characteristic data includes whether there are obstructions between the target terminal and each signal source, the type of obstruction, and the type of area where the target terminal is currently located; and fusing the multi-source signals according to a preset algorithm based on a set of weight values ​​to determine the target positioning result of the target terminal.

[0005] By adopting the above technical solution, a set of signal strength values ​​of multi-source signals is obtained, including the signal strength values ​​of multiple signal sources, ensuring the diversity and comprehensiveness of data sources; the weight of each signal source is determined based on a set of signal strength values ​​and current environmental characteristic data, making signal processing more targeted and reducing the impact of environmental factors on positioning accuracy; multi-source signals are fused according to a preset algorithm, further improving the accuracy of positioning results, and being able to maintain high stability and reliability in complex electromagnetic environments. This technical solution improves the accuracy and stability of positioning by combining multi-source signals and using environmental characteristic data for weight adjustment; and by using environmental characteristic data for weight adjustment, it can effectively improve the accuracy and stability of indoor navigation positioning.

[0006] Optionally, the weights of each signal source in a multi-source signal are determined according to a set of signal strength values ​​and current environmental characteristic data to obtain a set of weight values, including: using a target prediction model to obtain a set of weight values ​​according to a set of signal strength values ​​and current environmental characteristic data, wherein the target prediction model is trained using a sample data packet set, and each sample data packet in the sample data packet set contains signal strength values ​​of each signal source corresponding to a location, environmental characteristic data corresponding to a location, an actual positioning result of a location, and a set of sample weight values ​​corresponding to the actual positioning result of a location.

[0007] By adopting the above technical scheme, the target prediction model is used to determine the weight of each signal source in the multi-source signal according to a set of signal strength values ​​and current environmental characteristic data, so that the weight distribution is more reasonable and the error caused by the influence of the environment on a single signal source is reduced; the target prediction model is trained based on a large number of sample data packet sets, and these sample data packets contain signal strengths, environmental characteristic data, actual positioning results and their corresponding sample weight values ​​at different locations, thereby improving the accuracy and robustness of the model; by performing weighted fusion processing on multi-source signals, the anti-interference ability and adaptability of the positioning system are effectively improved, and a high positioning accuracy can be maintained even in complex electromagnetic environments.

[0008] Optionally, the target positioning result of the target terminal is determined by fusing multi-source signals according to a preset algorithm based on a set of weight values, including one of the following: determining the target positioning result by triangulation based on a set of weight values ​​and a set of signal strength values; determining the target positioning result by Kalman filtering based on a set of weight values ​​and a set of signal strength values.

[0009] By adopting the above technical solutions, the triangulation method uses signal strength values ​​and weight values ​​to determine the position of the target terminal through geometric calculation, which is suitable for scenarios where the signal source position is known and the signal strength is related to the distance; the Kalman filtering method uses signal strength values ​​and weight values ​​to determine the position of the target terminal through state estimation and filtering algorithms, which is suitable for dynamic environments and can effectively handle noise and uncertainty. The weight values ​​can be dynamically adjusted to reduce the impact of noise and other interference, thereby further improving the stability and accuracy of the positioning results.

[0010] Optionally, the above method also includes: acquiring motion state data through an inertial measurement unit IMU, wherein the IMU is integrated in the target terminal, and the motion state data includes acceleration data and angular velocity data; predicting the moving direction of the target terminal according to the motion state data, and activating relevant area signal acquisition in advance to shorten the positioning delay.

[0011] By adopting the above technical solution, the motion state data is obtained through the inertial measurement unit (IMU), which can more accurately predict the moving direction of the target terminal, thereby activating the signal collection of the relevant area in advance during the movement process, reducing the positioning error caused by signal switching; predicting the moving direction of the target terminal based on the motion state data and activating the signal collection of the relevant area in advance can quickly obtain new signal strength values ​​when the target terminal enters a new area, avoiding delays caused by signal rescanning and improving user experience; combined with IMU data, the system can work more stably under different environmental conditions, especially in the case of unstable signals or more obstructions, through pre-judgment and advance preparation, to ensure the continuity and reliability of positioning results. This method further optimizes the response speed and accuracy of the positioning system by combining IMU data and multi-source signals.

[0012] Optionally, after determining the target positioning result of the target terminal according to a set of weight values ​​and a set of signal strength values ​​according to a preset algorithm, the method further includes: predicting the positioning result at the next moment according to the target positioning result and the motion state data.

[0013] By adopting the above technical solution, the combination of target positioning results and motion status data enables the system to predict the position at the next moment based on the dynamic characteristics of the terminal (such as acceleration and angular velocity) based on the known current position. This function not only reduces the error caused by signal delay, but also improves the response speed of the system; predicting the movement direction and activating signal acquisition in related areas in advance further shortens the delay time in the positioning process, ensuring high-precision continuous positioning even in fast-moving scenarios; overall, this technical solution significantly improves the stability and reliability of indoor navigation and positioning systems, especially suitable for complex electromagnetic environments and situations with dense obstructions.

[0014] Optionally, after fusing the multi-source signals according to a preset algorithm based on a set of weight values ​​to determine the target positioning result of the target terminal, the above method also includes: real-time monitoring of the signal quality of the multi-source signals, wherein the signal quality includes the signal strength value and noise level of each signal source in the multi-source signals; and optimizing the target positioning result according to the signal quality of the multi-source signals and a set of weight values ​​using an adaptive filtering algorithm to obtain an optimized positioning result.

[0015] By adopting the above technical solution, signal quality is one of the key factors for positioning accuracy. By real-time monitoring of the signal strength values ​​and noise levels of multi-source signals, changes in signal quality can be discovered in a timely manner, providing data support for subsequent optimization steps; the adaptive filtering algorithm can dynamically adjust the weight value according to changes in signal quality, thereby optimizing the fusion result. This step is a supplement and improvement to the original fusion algorithm, aiming to further improve positioning accuracy. It can monitor the signal quality of multi-source signals in real time to ensure that accurate signal strength values ​​and noise levels can be obtained in a timely manner under different environmental conditions; using the adaptive filtering algorithm to optimize the target positioning results can effectively reduce positioning errors caused by signal interference or environmental changes, and improve positioning accuracy and stability. This improvement enables indoor navigation positioning methods to maintain high accuracy and reliability in complex electromagnetic environments.

[0016] Optionally, the current environment characteristic data is obtained by the target terminal in one of the following ways: the target terminal preloads an indoor map of the target place to obtain the current environment characteristic data, wherein multiple signal sources are installed in the target place, and the indoor map includes the building layout of the target place, signal source locations and obstruction information; the target terminal perceives the environmental information in real time through built-in sensors to obtain the current environment characteristic data.

[0017] By adopting the above technical solution, two methods of obtaining current environmental feature data are proposed. Preloading the indoor map of the target place can pre-acquire detailed building layout, signal source location and obstruction information, so that these environmental characteristics can be considered in the positioning process, reducing signal attenuation and errors caused by obstructions, and improving positioning accuracy; built-in sensors perceive environmental information in real time, which can timely update current environmental feature data in a dynamic environment, ensuring that the positioning algorithm can quickly adapt to environmental changes and improve the real-time and robustness of positioning.

[0018] In the second aspect of the present application, there is also provided an indoor navigation and positioning system combining multi-source signals, which is located in a target terminal and includes: a multi-sensor fusion module for obtaining a set of signal strength values ​​of multi-source signals, wherein the multi-sensor fusion module integrates multiple signal receivers such as Wi-Fi, Bluetooth, ultra-wideband UWB and inertial measurement unit IMU, the multi-source signals include signals emitted by multiple signal sources, and a set of signal strength values ​​include the signal strength value of each signal source in the multiple signal sources; a first determination module for determining the weight of each signal source in the multi-source signal according to a set of signal strength values ​​and current environmental feature data to obtain a set of weight values, wherein the current environmental feature data includes whether there are obstructions between the target terminal and each signal source, the type of obstruction, and the type of area where the target terminal is currently located; a second determination module for fusing the multi-source signals according to a preset algorithm according to a set of weight values ​​to determine the target positioning result of the target terminal.

[0019] By adopting the above technical solution, the multi-sensor fusion module integrates multiple signal receivers (such as Wi-Fi, Bluetooth, ultra-wideband UWB and inertial measurement unit IMU), and can simultaneously obtain signal strength values ​​from different signal sources, thereby realizing the comprehensive use of multi-source signals and improving the robustness and anti-interference ability of the system; the first determination module dynamically adjusts the weight of each signal source according to a set of signal strength values ​​and current environmental feature data to ensure that the optimal signal combination can be selected under different environmental conditions, further improving the positioning accuracy, and dynamically adjusting the weight of each signal source by considering environmental characteristics (such as the type of obstruction, the type of area), so that the system can adapt to complex indoor environments; the second determination module uses a preset algorithm to fuse the multi-source signals, and finally determines the target positioning result of the target terminal, ensuring high-precision and low-latency positioning performance. The multi-source signals are fused using a preset algorithm (such as triangulation or Kalman filtering), which further improves the positioning accuracy of the target terminal, not only considering the change of signal strength, but also comprehensively analyzing the environmental characteristics, ensuring stable performance in different scenarios.

[0020] Optionally, the above system also includes: a micro-motion detection module, which is used to obtain motion status data using the IMU, and predict the moving direction of the target terminal based on the motion status data, and activate the relevant area signal acquisition in advance to shorten the positioning delay, wherein the target terminal is integrated with the IMU, and the motion status data includes acceleration data and angular velocity data.

[0021] In a third aspect of the present application, an electronic device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the program, any of the above method steps is implemented.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the above method steps is performed.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: 1. By combining multi-source signals and using environmental feature data for weight adjustment, the accuracy and stability of indoor navigation positioning can be effectively improved; 2. By combining IMU data and multi-source signals, the response speed and accuracy of the positioning system are further optimized; 3. Predict the moving direction and activate the signal collection in the relevant area in advance, further shortening the delay time in the positioning process and ensuring high-precision continuous positioning even in fast-moving scenarios; 4. The preset algorithm is used to fuse multi-source signals to further improve the positioning accuracy of the target terminal. It not only takes into account the changes in signal strength, but also comprehensively analyzes the environmental characteristics to ensure stable performance in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a flow chart of an indoor navigation and positioning method combining multi-source signals provided in an embodiment of the present application; Figure 2 It is a structural block diagram of an indoor navigation and positioning system combining multi-source signals provided in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application.

[0025] Description of reference numerals: 300 - electronic device; 301 - processor; 302 - communication bus; 303 - user interface; 304 - network interface; 305 - memory. DETAILED DESCRIPTION

[0026] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0028] In the description of the embodiments of the present application, the term "plurality" means two or more. In addition, 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 indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] The following is combined with Figure 1-Figure 3 The embodiments of the present application are described.

[0030] This application provides an indoor navigation and positioning method combining multi-source signals, referring to Figure 1 , Figure 1: is a flow chart of an indoor navigation and positioning method combining multi-source signals provided in an embodiment of the present application, the method comprising: Step S101, obtaining a set of signal strength values ​​of a multi-source signal, wherein the multi-source signal includes signals transmitted by a plurality of signal sources, and the set of signal strength values ​​includes a signal strength value of each signal source in the plurality of signal sources; Step S102, determining the weight of each signal source in the multi-source signal according to a set of signal strength values ​​and current environment characteristic data to obtain a set of weight values, wherein the current environment characteristic data includes whether there is an obstruction between the target terminal and each signal source, the type of the obstruction, and the type of area where the target terminal is currently located; Step S103 , fusing the multi-source signals according to a set of weight values ​​and a preset algorithm to determine a target positioning result of the target terminal.

[0031] Through the above steps, a set of signal strength values ​​of multi-source signals are obtained, including the signal strength values ​​of multiple signal sources, ensuring the diversity and comprehensiveness of data sources; the weight of each signal source is determined according to a set of signal strength values ​​and current environmental characteristic data, making signal processing more targeted and reducing the impact of environmental factors on positioning accuracy; multi-source signals are fused according to a preset algorithm, further improving the accuracy of positioning results, and being able to maintain high stability and reliability in a complex electromagnetic environment. This embodiment improves the accuracy and stability of positioning by combining multi-source signals and using environmental characteristic data for weight adjustment; and using environmental characteristic data for weight adjustment can effectively improve the accuracy and stability of indoor navigation positioning.

[0032] Utilize the signals transmitted by multiple signal sources (such as Wi-Fi, Bluetooth, ultra-wideband, etc.) to obtain the signal strength value received by the target terminal; dynamically adjust the weight of each signal source according to environmental characteristic data such as the occlusion between the target terminal and the signal source, the type of occlusion, and the area type. Signal sources with fewer occlusions and better signal propagation conditions will be given higher weights; use a preset algorithm (such as weighted averaging, triangulation, or Kalman filtering, etc.) to fuse the multi-source signals and obtain the final positioning result of the target terminal. In the related art, a single signal source is used, which is susceptible to environmental influences and leads to a decrease in accuracy. This embodiment takes advantage of multi-source signals (such as Wi-Fi, Bluetooth, etc.), takes into account the influence of factors such as possible obstructions in the actual environment on signal transmission, and uses environmental feature data for weight adjustment. The signal source weight is dynamically allocated according to environmental features (such as obstruction type, area type). The obstruction type can include obstructions of different materials such as walls, glass, and metal. The area type (or spatial layout) can be a corridor, room, hall, etc. This method can effectively solve the limitations of a single signal source in a complex environment. The dynamic weight allocation can adjust the contribution of the signal source according to environmental changes, thereby enhancing the stability of the positioning system in a complex environment, thereby improving the positioning accuracy; for example, in an area with metal obstructions, the weight of the Wi-Fi signal may be reduced, while the weight of the UWB signal may be increased. The combination of multi-source signals can cover a wider area and meet the positioning needs of large buildings or complex structures. The present invention overcomes the problem that a single signal source is susceptible to environmental influences by combining data from multiple signal sources and taking into account specific environmental factors, and provides a more stable and reliable indoor positioning service.

[0033] In an optional embodiment, the weights of each signal source in a multi-source signal are determined according to a set of signal strength values ​​and current environmental characteristic data to obtain a set of weight values, including: using a target prediction model to obtain a set of weight values ​​according to a set of signal strength values ​​and current environmental characteristic data, wherein the target prediction model is trained using a sample data packet set, and each sample data packet in the sample data packet set contains signal strength values ​​of each signal source corresponding to a location, environmental characteristic data corresponding to a location, an actual positioning result of a location, and a set of sample weight values ​​corresponding to the actual positioning result of a location.

[0034] In the above embodiment, the target prediction model is used to determine the weight of each signal source in the multi-source signal according to a set of signal strength values ​​and current environmental characteristic data, so that the weight distribution is more reasonable and the error caused by the environmental influence on a single signal source is reduced; the target prediction model is trained based on a large number of sample data packet sets, and these sample data packets contain signal strengths, environmental characteristic data, actual positioning results and their corresponding sample weight values ​​at different locations, thereby improving the accuracy and robustness of the model; by performing weighted fusion processing on multi-source signals, the anti-interference ability and adaptability of the positioning system are effectively improved, and a high positioning accuracy can be maintained even in a complex electromagnetic environment.

[0035] The input of the target prediction model includes signal strength value and current environmental feature data. The output of the target prediction model is a set of weight values ​​used for signal fusion. The target prediction model is trained using a set of sample data packets, which contain signal strength values, environmental features, actual positioning results, and corresponding sample weight values. The sample data packet set is trained using a machine learning algorithm (such as a neural network, regression model, etc.) to obtain a target prediction model that can predict weights. This method dynamically adjusts weights through a machine learning model to adapt to complex environmental changes and improve positioning accuracy and stability. The specific steps of this embodiment include: data preparation, collecting a set of sample data packets containing signal strength values, environmental characteristics, actual positioning results and weight values. For example, measuring the receiving strength (RSSI) of Wi-Fi, Bluetooth, and UWB signals at different locations (such as corridors, shops, elevators, etc.) in large indoor places (such as shopping malls), recording the environmental information of each measurement point, such as the distance from the signal source, whether there are any obstructions, and recording the real position coordinates of each measurement point; of course, the collected data needs to be cleaned and standardized, such as removing noise data and normalization; model training, using these sample data packets to train the target prediction model so that it can dynamically output weight values ​​according to the input signal strength and environmental characteristics; weight allocation, in the actual positioning process, according to the current signal strength and environmental characteristics, using the trained model to dynamically allocate weights; signal fusion and positioning, fusion of multi-source signals according to the allocated weights, and finally determining the positioning result of the target terminal. Traditional indoor positioning technology relies on a single signal source and is easily affected by environmental interference, resulting in reduced positioning accuracy and unstable performance in dynamic environments (such as signal strength fluctuations and changes in obstructions); this embodiment dynamically adjusts the weights through the target prediction model to adapt to environmental changes and improve positioning accuracy; dynamic weight distribution is used to reduce environmental interference and improve positioning stability; the machine learning model can learn complex environmental characteristics and enhance system adaptability. The target prediction model can dynamically adjust the signal weight according to environmental characteristics, so that more reliable signal sources in different environments are given higher weights. For example, in open areas, Wi-Fi and Bluetooth signals may be given higher weights; and in areas blocked by walls, UWB signals may be given higher weights. The target prediction model is usually trained and optimized based on historical data and environmental factors, and can more accurately predict the weight distribution of each signal source under different environmental conditions. These weight values ​​reflect the contribution of each signal source in the positioning process and are key parameters for subsequent positioning calculations.

[0036] In an optional embodiment, a target positioning result of a target terminal is determined by fusing multi-source signals according to a preset algorithm based on a set of weight values, including one of the following: determining the target positioning result by triangulation based on a set of weight values ​​and a set of signal strength values; determining the target positioning result by Kalman filtering based on a set of weight values ​​and a set of signal strength values.

[0037] In the above embodiments, the triangulation method uses signal strength values ​​and weight values ​​to determine the position of the target terminal through geometric calculation, which is suitable for scenarios where the signal source position is known and the signal strength is related to the distance; the Kalman filtering method uses signal strength values ​​and weight values ​​to determine the position of the target terminal through state estimation and filtering algorithms, which is suitable for dynamic environments and can effectively handle noise and uncertainty. The weight values ​​can be dynamically adjusted to reduce the impact of noise and other interference, thereby further improving the stability and accuracy of the positioning results.

[0038] Triangulation is a classic positioning method that estimates the target position based on the distance information of multiple reference points. In this scenario, each signal source can be regarded as a reference point, and its signal strength is converted into a distance estimate, which is optimized in combination with the weight value; Kalman filtering is a recursive algorithm that can handle the state estimation problem of linear dynamic systems. In positioning, it can be used to fuse information from different signal sources and update the target position based on the estimate at the previous moment and the observation at the current moment. The Kalman filtering method filters the signal strength value and its weight, and gradually updates the estimate of the target terminal position, thereby reducing the impact of noise and error and improving the stability and accuracy of the positioning results. By providing a variety of fusion algorithms to choose from, the system can adapt to different environmental conditions, improving the flexibility and robustness of the system.

[0039] The principle of triangulation: Assume that the user's position is (x, y), the positions of the signal sources are (x1, y1), (x2, y2) and (x3, y3), and the distances between the user and these signal sources are d1, d2 and d3 respectively. The user's position (x, y) can be solved by the following set of equations: Assume that the positions of the signal sources are: (x1, y1) = (0, 0), (x2, y2) = (10, 0), (x3, y3) = (5, 10), and the measured RSSI values ​​are: RSSI1 = -50dBm, RSSI2 = -55dBm, RSSI3 = -45dBm. Assume that the logarithmic distance model is used to estimate the distance: Where A is the RSSI value at the reference distance, and n is the path loss index. Assuming A = -30dBm, n = 2.5, then: d1 = 0.158m, d2 = 0.1m, d3 = 0.251m. Substituting these distances into the equation system and solving the equation system, we can get the user's position (x, y).

[0040] In triangulation, signal weight values ​​can be used to adjust the contribution of each signal source in the positioning calculation. Specifically, we can use weighted least squares to solve the user's position. For example, suppose we have three signal sources at (x1, y1), (x2, y2), and (x3, y3), the distances between the user and these signal sources are d1, d2, and d3, and the signal weights are w1, w2, and w3. We can construct the following weighted least squares problem: min(w1((x-x1) 2 +(y-y1) 2 -d1 2 ) 2 +w2((x-x2) 2 +(y-y2) 2 -d2 2 ) 2 +w3((x-x3) 2 +(y-y3) 2 -d3 2 ) 2 ), by solving this weighted least squares problem, we can get the user's position (x, y). Signal sources with higher weight values ​​will be given greater weight in the positioning calculation, thereby reducing the impact of noise and interference.

[0041] Kalman filtering is a recursive algorithm used to estimate the state of a linear dynamic system. It minimizes the variance of the estimation error by combining the dynamic model of the system with the observed data. In indoor positioning, Kalman filtering can be used to fuse multi-source signal data, reduce the impact of noise and interference, and improve positioning accuracy.

[0042] Assume that the user moves in an indoor environment, his position can be represented by a two-dimensional vector x = [x, y] T The user's motion model can be expressed as: k =Fx k-1 +u k +w k , where: x k is the user's position at time step k, F is the state transition matrix, representing the linear change of the user's position, u k is the control input, such as the user's movement speed and direction, w k is the process noise, assumed to be Gaussian.

[0043] Assuming that the user's location can be measured by multiple signal sources (such as Wi-Fi, Bluetooth, and UWB), the observation model of each signal source can be expressed as: k =Hx k +v k , where: z k is the observation data at time step k, such as signal strength (RSSI); H is the observation matrix, which maps user locations to observation data; v k is the observation noise, assumed to be Gaussian.

[0044] Kalman filtering consists of two main steps: prediction and update.

[0045] Prediction steps: Predicting user location: Prediction error covariance: P k|k-1 =FP k-1|k-1 F T +Q, Where Q is the process noise covariance matrix; Update steps: Calculate the Kalman gain: K k =P k|k-1 H T (HP k|k-1 H T +R) -1 , Where R is the observation noise covariance matrix; Update user location: Update error covariance: P k|k =(IK k H)P k|k-1 , Where I is the identity matrix.

[0046] Adjust the filter parameters according to the signal weights: In the update step, the observation data z k It is usually provided by multiple signal sources. According to the signal strength prediction model, we can get the weight wi of each signal source. These weights can be used to adjust the observation matrix H and the observation noise covariance matrix R.

[0047] The observation matrix H maps the user position to the observation data. If the weight of each signal source is different, we can adjust H to a weighted observation matrix. For example, taking two signal sources as an example, if the weight of signal source 1 is w1 and the weight of signal source 2 is w2, the adjusted observation matrix can be expressed as: The observation noise covariance matrix R represents the uncertainty of the observation data. Based on the signal weights, we can adjust R to reflect the reliability of different signal sources. For example, if the weight of signal source 1 is w1 and the weight of signal source 2 is w2, the adjusted observation noise covariance matrix can be expressed as: Through the Kalman filter algorithm, combined with the predicted signal weight and multi-source signal data, the user's estimated position can be dynamically adjusted to reduce the impact of noise and interference and improve the accuracy and stability of positioning. This method is not only suitable for large shopping malls, but can also be applied to other indoor scenes such as hospitals, airports, underground parking lots, etc.

[0048] In an optional embodiment, the above method also includes: acquiring motion state data through an inertial measurement unit IMU, wherein the IMU is integrated in the target terminal, and the motion state data includes acceleration data and angular velocity data; predicting the moving direction of the target terminal according to the motion state data, and activating relevant area signal acquisition in advance to shorten the positioning delay.

[0049] In the above embodiment, by acquiring motion state data through an inertial measurement unit (IMU), the moving direction of the target terminal can be predicted more accurately, thereby activating signal acquisition in related areas in advance during the movement process, reducing positioning errors caused by signal switching; by predicting the moving direction of the target terminal based on the motion state data and activating signal acquisition in related areas in advance, a new signal strength value can be quickly obtained when the target terminal enters a new area, avoiding delays caused by signal rescanning and improving user experience; combined with IMU data, the system can work more stably under different environmental conditions, especially in cases where the signal is unstable or there are many obstructions, through prejudgment and advance preparation, the continuity and reliability of the positioning results can be ensured. This method further optimizes the response speed and accuracy of the positioning system by combining IMU data and multi-source signals.

[0050] The acceleration and angular velocity data are obtained by IMU to analyze the motion state of the target terminal, predict the moving direction of the target terminal according to the IMU data, and activate the signal acquisition of the relevant area in advance according to the predicted result, so as to shorten the positioning delay. That is, by using the acceleration and angular velocity data provided by IMU, combined with the motion state of the target terminal, according to the predicted moving direction, the system can activate the signal source acquisition of the area that the target terminal is about to enter in advance, so as to ensure that the latest signal strength data can be obtained immediately when the target terminal arrives at the new area, thereby shortening the positioning delay and optimizing the response speed and accuracy of the indoor positioning system. In the traditional method, when the target terminal moves, there is a delay in signal acquisition and positioning calculation, which affects the user experience, and it is difficult to quickly respond to the movement of the target terminal in a dynamic environment, resulting in a decrease in positioning accuracy. This embodiment activates the signal acquisition of the relevant area in advance, shortens the positioning delay, improves the user experience, combines IMU data and multi-source signals, quickly responds to the movement of the target terminal, improves the positioning accuracy, activates the signal acquisition in advance according to the predicted result, optimizes the resource utilization, and improves the system efficiency. For application scenarios that require high precision and low latency (such as indoor navigation, intelligent warehouse management, etc.), the present invention can provide a smoother and more accurate positioning service.

[0051] In an optional embodiment, after determining the target positioning result of the target terminal according to a set of weight values ​​and a set of signal strength values ​​according to a preset algorithm, the method further includes: predicting the positioning result at the next moment according to the target positioning result and the motion state data.

[0052] In the above embodiment, the combined use of target positioning results and motion status data enables the system to predict the position at the next moment based on the dynamic characteristics of the terminal (such as acceleration and angular velocity) on the basis of the known current position. This function not only reduces the error caused by signal delay, but also improves the response speed of the system; predicting the moving direction and activating the signal acquisition of the relevant area in advance further shortens the delay time in the positioning process, ensuring high-precision continuous positioning even in fast-moving scenarios; overall, this embodiment significantly improves the stability and reliability of the indoor navigation and positioning system, and is particularly suitable for situations in complex electromagnetic environments and where dense obstructions exist.

[0053] The target positioning result provides accurate information of the current position, while the motion state data reflects the motion trend of the target terminal. It is reasonable to combine the two for prediction because they together constitute the key information required to predict the future position; based on the current position and motion state data, the possible position of the target terminal at the next moment can be estimated. This prediction capability is essential for providing real-time navigation guidance and path planning. For example, the fused historical trajectory information is analyzed using time series analysis, machine learning models (such as recurrent neural networks RNN, long short-term memory networks LSTM, etc.) or trajectory prediction algorithms to predict the possible position of the target terminal at the next moment; the prediction results are output to the navigation system or other related applications to provide real-time navigation guidance and path planning. This embodiment enables the navigation system to respond in advance and provide coherent navigation guidance by predicting the positioning results at the next moment; accurate prediction results and real-time navigation guidance enable users to reach their destination more easily, improving user satisfaction and trust; based on the prediction results, the navigation system can plan the path more effectively to avoid unnecessary detours or delays. By combining IMU data and dynamic prediction algorithms, it can better adapt to the rapid movement of the target terminal and improve the performance of the system in a dynamic environment.

[0054] In an optional embodiment, after fusing multi-source signals according to a preset algorithm based on a set of weight values ​​to determine the target positioning result of the target terminal, the above method also includes: real-time monitoring of the signal quality of the multi-source signals, wherein the signal quality includes the signal strength value and noise level of each signal source in the multi-source signal; and optimizing the target positioning result according to the signal quality of the multi-source signals and a set of weight values ​​using an adaptive filtering algorithm to obtain an optimized positioning result.

[0055] In the above embodiment, signal quality is one of the key factors for positioning accuracy. By real-time monitoring of the signal strength values ​​and noise levels of multi-source signals, changes in signal quality can be discovered in a timely manner, providing data support for subsequent optimization steps; the adaptive filtering algorithm can dynamically adjust the weight value according to changes in signal quality, thereby optimizing the fusion result. This step is a supplement and improvement to the original fusion algorithm, aiming to further improve positioning accuracy. The signal quality of multi-source signals can be monitored in real time to ensure that accurate signal strength values ​​and noise levels can be obtained in a timely manner under different environmental conditions; the use of an adaptive filtering algorithm to optimize the target positioning results can effectively reduce positioning errors caused by signal interference or environmental changes, and improve positioning accuracy and stability. This improvement enables the indoor navigation positioning method to maintain high accuracy and reliability in complex electromagnetic environments.

[0056] By real-time monitoring of the signal strength values ​​and noise levels of multi-source signals, the changes in signal quality are evaluated. The signal strength value reflects the strength of the signal, while the noise level reflects the degree of interference in the signal. According to the changes in signal quality, the weight value is dynamically adjusted using the adaptive filtering algorithm. When the signal quality of a certain signal source decreases, the algorithm will reduce its weight value to reduce its impact on the fusion result; on the contrary, when the signal quality improves, the algorithm will increase its weight value to make full use of high-quality signals. After determining the positioning result of the target terminal, the signal quality of multi-source signals is monitored in real time, including the signal strength value and noise level of multi-source signals. According to the signal quality and weight value, the adaptive filtering algorithm is used to optimize the initial positioning result. For example, the Kalman filter can dynamically adjust the filtering parameters according to the current signal quality and historical data, thereby improving the accuracy and stability of the positioning result. The adaptive filtering algorithm can effectively suppress noise and signal interference, improve the stability of the system in complex environments, optimize the positioning result through the adaptive filtering algorithm, improve the positioning accuracy, dynamically adjust the filtering parameters, adapt to the changes in signal quality, and improve the stability of the system.

[0057] Taking the navigation and positioning in a large shopping mall as an example, a user uses a smartphone for navigation in a large shopping mall. Wi-Fi hotspots, Bluetooth beacons and UWB base stations are deployed in the shopping mall. The user enters from the entrance and aims to find a specific store. The multi-source signal strengths obtained include: Wi-Fi signal strength is -50dBm, Bluetooth signal strength is -60dBm, and UWB signal embedded end is -40dBm. The aforementioned target prediction model predicts the weights of the signal sources based on historical data and current environmental feature data as Wi-Fi: Bluetooth: UWB = 0.4:0.3:0.3. Note that Here is just an example. Then, according to the weight and signal strength of each signal source, the user's position coordinates are preliminarily calculated to be (X=10.5, Y=15.2); the adaptive filtering algorithm dynamically adjusts the filter parameters according to the signal quality and weight. For example, the noise level of the Wi-Fi signal is high, and the filter reduces its weight to 0.3. The noise level of the UWB signal is low, and the filter increases its weight to 0.5. The noise level of the Bluetooth signal is medium, and the filter sets its weight to 0.2. The optimized positioning result: the user's position coordinates are updated to (X=10.3, Y=15.1), and the positioning accuracy is improved.

[0058] In an optional embodiment, the current environment characteristic data is obtained by the target terminal in one of the following ways: the target terminal preloads an indoor map of the target location to obtain the current environment characteristic data, wherein multiple signal sources are installed in the target location, and the indoor map includes the building layout of the target location, signal source locations and obstruction information; the target terminal perceives the environmental information in real time through built-in sensors to obtain the current environment characteristic data.

[0059] In the above embodiment, two methods of obtaining current environmental feature data are proposed. Preloading the indoor map of the target location can pre-acquire detailed building layout, signal source location and obstruction information, so that these environmental characteristics can be taken into account in the positioning process, reducing signal attenuation and errors caused by obstructions, and improving positioning accuracy; built-in sensors perceive environmental information in real time, and can timely update current environmental feature data in a dynamic environment, ensuring that the positioning algorithm can quickly adapt to environmental changes and improve the real-time and robustness of positioning.

[0060] Preloading indoor maps is suitable for situations where the target location environment is known and the map information is accurate. By preloading indoor maps, the target terminal can obtain key data such as the building layout, signal source location, and obstruction information of the target location, providing strong support for subsequent positioning calculations; real-time perception of environmental information is suitable for situations where the environment changes greatly or the map information is inaccurate. Built-in sensors (such as cameras, lidar, etc.) are used to perceive environmental information in real time, such as the location, shape, size, etc. of objects, and convert this information into environmental feature data. This data can be used to update the indoor environment model or be integrated with preloaded indoor map data to improve positioning accuracy. The target terminal can dynamically obtain the feature data of the current environment to adapt to changes in the environment. By preloading indoor maps or perceiving environmental information in real time, the system can more accurately understand the current environmental characteristics, thereby improving positioning accuracy, and the system can provide more stable and accurate indoor navigation services.

[0061] The present application also provides an indoor navigation and positioning system combining multi-source signals, located in a target terminal, such as Figure 2 As shown, Figure 2 : is a structural block diagram of an indoor navigation and positioning system combining multi-source signals provided in an embodiment of the present application, the system comprising: A multi-sensor fusion module 201 is used to obtain a set of signal strength values ​​of multi-source signals, wherein the multi-sensor fusion module integrates multiple signal receivers such as Wi-Fi, Bluetooth, ultra-wideband UWB and inertial measurement unit IMU, the multi-source signal includes signals emitted by multiple signal sources, and the set of signal strength values ​​includes the signal strength value of each signal source in the multiple signal sources; A first determination module 202 is used to determine the weight of each signal source in the multi-source signal according to a set of signal strength values ​​and current environmental characteristic data to obtain a set of weight values, wherein the current environmental characteristic data includes whether there is an obstruction between the target terminal and each signal source, the type of the obstruction, and the type of area where the target terminal is currently located; The second determination module 203 is used to fuse the multi-source signals according to a set of weight values ​​and a preset algorithm to determine the target positioning result of the target terminal.

[0062] By adopting the above system, the multi-sensor fusion module 201 integrates multiple signal receivers (such as Wi-Fi, Bluetooth, ultra-wideband UWB and inertial measurement unit IMU), and can simultaneously obtain signal strength values ​​from different signal sources, thereby realizing the comprehensive use of multi-source signals and improving the robustness and anti-interference ability of the system; the first determination module 202 dynamically adjusts the weight of each signal source according to a set of signal strength values ​​and current environmental feature data, ensuring that the optimal signal combination can be selected under different environmental conditions, further improving the positioning accuracy, and dynamically adjusting the weight of each signal source by considering environmental characteristics (such as the type of obstruction, the type of area), so that the system can adapt to complex indoor environments; the second determination module 203 uses a preset algorithm to fuse the multi-source signals, and finally determines the target positioning result of the target terminal, ensuring high-precision and low-latency positioning performance. Using a preset algorithm (such as triangulation or Kalman filtering) to fuse the multi-source signals further improves the positioning accuracy of the target terminal, not only considering the change of signal strength, but also comprehensively analyzing the environmental characteristics, ensuring stable performance in different scenarios.

[0063] In the above embodiment, the multi-sensor fusion module 201 integrates multiple signal receivers to collect signal strength values ​​from different signal sources, which include wireless communication signals such as Wi-Fi, Bluetooth, UWB, and motion state data (acceleration and angular velocity) provided by IMU. These data provide a basis for subsequent positioning calculations; the first determination module 202 calculates the weight of each signal source based on the collected signal strength values ​​and current environmental feature data (such as obstruction type, area type, etc.) using a target prediction model or other algorithms, which helps to dynamically adjust the importance of each signal source in a complex environment to improve positioning accuracy; the second determination module 203 uses a preset algorithm (such as triangulation, Kalman filtering, etc.) to fuse the weighted signal source information and finally determine the position of the target terminal. This multi-source signal fusion method can overcome the limitations of a single signal source to a certain extent and provide more accurate positioning results.

[0064] In an optional embodiment, the above system also includes: a micro-motion detection module, which is used to obtain motion status data using the IMU, and predict the moving direction of the target terminal based on the motion status data, and activate the relevant area signal acquisition in advance to shorten the positioning delay, wherein the target terminal is integrated with the IMU, and the motion status data includes acceleration data and angular velocity data.

[0065] In the above embodiments, the motion state data (such as acceleration data and angular velocity data) obtained by the IMU can be used to predict the moving direction of the target terminal, thereby activating the signal collection of the relevant area in advance, significantly shortening the positioning delay; based on the prediction result, the system can intelligently activate the signal collection of the relevant area. For example, if it is predicted that the target terminal will move in a certain direction, the system can activate the signal source and receiver in that direction in advance, so as to quickly obtain the signal data required for positioning when the target terminal arrives; by predicting the moving direction of the target terminal and activating the signal collection of the relevant area in advance, the positioning delay is significantly shortened and the response speed of the system is improved. For application scenarios that require high precision and low latency (such as indoor navigation, intelligent warehouse management, etc.), the present invention can provide smoother and more accurate positioning services.

[0066] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0067] The present application also provides a computer-readable storage medium, in which instructions are stored. When the instructions are executed, any one of the method steps described above is executed.

[0068] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0069] The present application also discloses an electronic device. Figure 3 As shown, Figure 3 The electronic device 300 may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , at least one network interface 304 , and a memory 305 .

[0070] The communication bus 302 is used to realize the connection and communication between these components.

[0071] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0072] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0073] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts of the entire electronic device (such as a server), and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0074] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of an indoor navigation positioning method combining multi-source signals.

[0075] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call an application program of an indoor navigation and positioning method combining multi-source signals stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0076] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] In the several implementation modes provided in this application, it should be understood that the disclosed device or system can be implemented in other ways. For example, the device or system embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0080] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0081] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure.

[0082] This application is intended to cover any modifications, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the technical field not recorded in the present disclosure.

Claims

1. An indoor navigation and positioning method combining multi-source signals, characterized in that: include: Acquire a set of signal strength values ​​of multi-source signals, wherein the multi-source signals include signals transmitted by multiple signal sources, and the set of signal strength values ​​includes the signal strength value of each signal source in the multiple signal sources; Determine the weight of each signal source in the multi-source signal according to the set of signal strength values ​​and current environment characteristic data to obtain a set of weight values, wherein the current environment characteristic data includes whether there is an obstruction between the target terminal and each signal source, the type of the obstruction, and the type of area where the target terminal is currently located; The multi-source signals are fused according to the set of weight values ​​and a preset algorithm to determine a target positioning result of the target terminal.

2. The method according to claim 1, characterized in that Determining the weight of each signal source in the multi-source signal according to the set of signal strength values ​​and the current environment characteristic data to obtain a set of weight values, including: The target prediction model is used to obtain the set of weight values ​​according to the set of signal strength values ​​and the current environmental characteristic data, wherein the target prediction model is trained using a sample data packet set, and each sample data packet in the sample data packet set contains the signal strength value of each signal source corresponding to a location, the environmental characteristic data corresponding to the location, the actual positioning result of the location, and a set of sample weight values ​​corresponding to the actual positioning result of the location.

3. The method according to claim 1, characterized in that The method of fusing the multi-source signals according to the set of weight values ​​and a preset algorithm to determine a target positioning result of the target terminal includes one of the following: Determine the target positioning result by triangulation according to the set of weight values ​​and the set of signal strength values; The target positioning result is determined by a Kalman filtering method according to the set of weight values ​​and the set of signal strength values.

4. The method according to claim 1, characterized in that: The method further comprises: Acquiring motion state data through an inertial measurement unit IMU, wherein the target terminal is integrated with the IMU, and the motion state data includes acceleration data and angular velocity data; The moving direction of the target terminal is predicted according to the motion state data, and signal collection in the relevant area is activated in advance to shorten the positioning delay.

5. The method according to claim 4, characterized in that After determining the target positioning result of the target terminal according to the set of weight values ​​and the set of signal strength values ​​according to a preset algorithm, the method further includes: The positioning result at the next moment is predicted according to the target positioning result and the motion state data.

6. The method according to claim 1, characterized in that After fusing the multi-source signals according to the set of weight values ​​in accordance with a preset algorithm to determine a target positioning result of the target terminal, the method further includes: Monitoring the signal quality of the multi-source signal in real time, wherein the signal quality includes the signal strength value and noise level of each signal source in the multi-source signal; The target positioning result is optimized according to the signal quality of the multi-source signals and the set of weight values ​​by using an adaptive filtering algorithm to obtain an optimized positioning result.

7. The method according to claim 1, characterized in that The current environment feature data is obtained by the target terminal in one of the following ways: The target terminal preloads an indoor map of the target location to obtain the current environment feature data, wherein the multiple signal sources are installed in the target location, and the indoor map includes the building layout, signal source location and shielding information of the target location; The target terminal senses environmental information in real time through built-in sensors to obtain the current environmental feature data.

8. An indoor navigation and positioning system combining multi-source signals, located in a target terminal, characterized in that: include: A multi-sensor fusion module, used to obtain a set of signal strength values ​​of multi-source signals, wherein the multi-sensor fusion module integrates multiple signal receivers such as Wi-Fi, Bluetooth, ultra-wideband UWB and inertial measurement unit IMU, the multi-source signal includes signals transmitted by multiple signal sources, and the set of signal strength values ​​includes the signal strength value of each signal source in the multiple signal sources; A first determination module is used to determine the weight of each signal source in the multi-source signal according to the set of signal strength values ​​and current environmental characteristic data to obtain a set of weight values, wherein the current environmental characteristic data includes whether there is an obstruction between the target terminal and each signal source, the type of the obstruction, and the type of area where the target terminal is currently located; The second determination module is used to fuse the multi-source signals according to the set of weight values ​​according to a preset algorithm to determine the target positioning result of the target terminal.

9. The system according to claim 8, characterized in that The system further comprises: The micro-motion detection module is used to obtain motion state data using the IMU, predict the moving direction of the target terminal based on the motion state data, and activate the relevant area signal acquisition in advance to shorten the positioning delay, wherein the IMU is integrated in the target terminal, and the motion state data includes acceleration data and angular velocity data.

10. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

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