A hybrid indoor positioning method and system based on Kalman filtering

Abstract

This application relates to a hybrid indoor positioning method and system based on Kalman filtering, belonging to the field of indoor positioning technology, to solve the problems of poor positioning accuracy and energy consumption balance and weak positioning stability in dynamic scenes in existing indoor scene positioning technologies. The method includes: synchronously acquiring Bluetooth Low Energy signal reception strength, ultra-wideband signal reception time, and motion state data of the object being positioned; constructing an extended Kalman filter positioning model containing two-dimensional position and velocity components; dynamically adjusting the ultra-wideband signal acquisition frequency based on the movement speed and trajectory curvature; predicting the state vector and covariance matrix through a time update stage; then combining the received signal strength and ultra-wideband arrival time difference to form a measurement vector; correcting the predicted value through a measurement update stage; and outputting the real-time position. This application achieves an optimized balance between positioning accuracy and energy consumption, and improves positioning stability in dynamic scenes.

Description

Technical Field

[0001] This invention belongs to the field of indoor positioning technology, specifically relating to a hybrid indoor positioning method and system based on Kalman filtering. Background Technology

[0002] Currently, indoor positioning scenarios are increasingly demanding higher positioning accuracy and longer battery life. Traditional hybrid positioning technologies often combine Bluetooth Low Energy (BLE) and ultra-wideband (UWB) signals for positioning, collecting parameters from both types of signals and using filtering algorithms to calculate the location. However, in practical applications, the UWB signal acquisition frequency is typically fixed, failing to flexibly adjust to the motion state of the target object. Furthermore, the filtering algorithms used in signal data fusion lack targeted optimization. In traditional solutions, the fixed-frequency UWB signal acquisition mode cannot adapt flexibly to changes in the target object's motion state, resulting in insufficient positioning accuracy. Maintaining high-frequency acquisition even during smooth movement or stationary conditions leads to energy waste. Simultaneously, the simple filtering algorithms fail to fully integrate the advantages of both types of signals, making the fused positioning results susceptible to environmental interference. In scenarios with dynamically changing target object motion states, the stability is insufficient, making it difficult to meet the practical application requirements for both high accuracy and low energy consumption in positioning.

[0003] Existing technologies have the following drawbacks: poor balance between positioning accuracy and energy consumption in indoor positioning scenarios, and weak positioning stability in dynamic scenarios. These are the shortcomings of existing technologies.

[0004] In view of this, it is very necessary to provide a hybrid indoor positioning method and system based on Kalman filtering to solve the above-mentioned defects in the prior art. Summary of the Invention

[0005] To address the technical problems of poor balance between positioning accuracy and energy consumption in indoor positioning scenarios and weak positioning stability in dynamic scenarios in existing technologies, this invention provides a hybrid indoor positioning method and system based on Kalman filtering to solve the above-mentioned technical problems.

[0006] In a first aspect, the present invention provides a hybrid indoor positioning method based on Kalman filtering, comprising:

[0007] Step S1: Collect the received signal strength of Bluetooth Low Energy signal and the reception time of UWB signal, and simultaneously collect the motion state data of the located object;

[0008] The time difference of arrival (TDOA) of an ultra-wideband (UWB) signal is calculated based on the difference in its reception time. The mathematical expression is as follows:

[0009] ;

[0010] in, Let be the arrival time difference between the i-th receiving point and the j-th receiving point. Let be the ultra-wideband signal reception time at the i-th receiving point. Let be the ultra-wideband signal reception time at the j-th receiving point;

[0011] The motion state data includes the moving speed and trajectory curvature. The moving speed is calculated by integrating the acceleration data of the positioning object, and the trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object.

[0012] By adopting the above technical solution, the calculation method of the time difference of arrival of ultra-wideband signals and the specific composition and acquisition logic of motion state data are clarified, providing accurate and standardized data support for subsequent frequency adjustment and positioning calculation.

[0013] Step S2: Construct a positioning model based on the extended Kalman filter algorithm, and use the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector;

[0014] State vector Represented as: Where x and y are the two-dimensional position coordinates of the positioning tag. Let x be the velocity components corresponding to x and y, and k be the time step number.

[0015] By adopting the above technical solution, the specific composition of the state vector is defined, providing a clear calculation benchmark for state prediction and correction.

[0016] Step S3: Dynamically adjust the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature;

[0017] Dynamically adjusting the acquisition frequency of the ultra-wideband signal includes increasing the acquisition frequency of the ultra-wideband signal when the moving speed exceeds a preset threshold or the trajectory curvature exceeds a preset threshold. Preset threshold and trajectory curvature When the preset threshold is reached, the sampling frequency of the ultra-wideband signal is reduced.

[0018] By adopting the above technical solution, the dynamic adjustment rules of the ultra-wideband signal acquisition frequency are refined to achieve precise matching between motion state and acquisition frequency. While ensuring positioning accuracy in complex motion scenarios, the system minimizes ineffective energy consumption and optimizes the balance between accuracy and energy consumption.

[0019] Step S4: In the time update stage, introduce the state transition matrix and the process noise covariance matrix to predict the current state vector and the state covariance matrix;

[0020] The mathematical expression for the time update phase is:

[0021] ,

[0022] ;

[0023] in, To predict the state vector, This is the optimal state vector of the previous iteration cycle. and They are respectively and The corresponding state covariance matrix, Here is the state transition matrix. Let be the process noise covariance matrix.

[0024] By adopting the above technical solution, the calculation process of the time update stage is standardized by quantitative mathematical expressions, which enables the accurate derivation of the predicted state vector and covariance matrix, providing a reliable basis for subsequent measurement updates and improving the prediction accuracy of positioning results.

[0025] Step S5: The received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time are combined to form a measurement vector. The extended Kalman filter equation is introduced in the measurement update stage to correct the predicted state vector and output the real-time position of the positioning object.

[0026] The measurement update phase introduces the extended Kalman filter equation to correct the predicted state vector, including:

[0027] ,

[0028] ,

[0029] ;

[0030] in, Here is the Kalman gain matrix. To predict the state vector, for The corresponding state covariance matrix, The linearization matrix of the measurement function, This is the corrected optimal state vector. For measurement function, To measure the noise covariance matrix, For measurement vectors, It is the identity matrix;

[0031] When only the received signal strength data is acquired, ;

[0032] When simultaneously acquiring received signal strength data and time difference of arrival data acquired periodically based on the ultra-wideband signal reception time, Where n is the number of Bluetooth Low Energy module anchor points, and m is the number of time difference of arrival measurement groups. This is the received signal strength data for the nth Bluetooth Low Energy module anchor point. This is the arrival time difference measurement data for the m-th group.

[0033] The theoretical value of the received signal strength is calculated using the logarithmic distance path loss model. The mathematical expression is as follows:

[0034] ;

[0035] in, For reference distance The received power at that location, For path loss power, To predict the distance between the location and the nth Bluetooth Low Energy module anchor point.

[0036] The real-time position of the output positioning object is represented as follows: ,in , represents the real-time position coordinates of the object along the x-axis. This refers to the real-time position coordinates of the object along the y-axis. , Corresponding to the optimal state vector The values ​​of the x and y components.

[0037] By adopting the above technical solution, the complete equation of the extended Kalman filter and the dual-mode composition rules of the measurement vector are clarified in the measurement update stage. Combined with the logarithmic distance path loss model, the theoretical value of the received signal strength is accurately calculated. This makes the fusion correction of Bluetooth Low Energy signal and ultra-wideband signal data more in line with the characteristics of actual scenarios. At the same time, the standardized coordinate representation of the positioning results is clarified, which improves the accuracy and stability of the positioning results in dynamic scenarios, avoids output ambiguity, provides a standardized basis for the subsequent application of positioning data, and strengthens the scientificity, reliability and practicality of the positioning method.

[0038] Secondly, the technical solution of the present invention also provides a hybrid indoor positioning system based on Kalman filtering, including a data acquisition module, a model building module, a frequency adjustment module, a state prediction module, and a state correction module;

[0039] The data acquisition module collects the received signal strength of Bluetooth Low Energy signals and the reception time of Ultra Wideband signals, and simultaneously collects the motion status data of the located object.

[0040] Among them, motion state data includes movement speed and trajectory curvature, and the arrival time difference of ultra-wideband signals is calculated based on the difference in reception time of ultra-wideband signals;

[0041] The model building module constructs a positioning model based on the extended Kalman filter algorithm, using the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector;

[0042] The frequency adjustment module dynamically adjusts the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature.

[0043] The dynamic adjustment of the ultra-wideband signal acquisition frequency includes: increasing the acquisition frequency of the ultra-wideband signal when the moving speed is greater than a preset threshold or the trajectory curvature is greater than a preset threshold, and adjusting the moving speed accordingly. Preset threshold and trajectory curvature When the preset threshold is reached, the sampling frequency of the ultra-wideband signal is reduced.

[0044] The state prediction module introduces the state transition matrix and process noise covariance matrix during the time update phase to predict the current state vector and state covariance matrix.

[0045] The state correction module combines the received signal strength and the time difference of arrival obtained periodically based on the ultra-wideband signal reception time to form a measurement vector. Through the measurement update stage, the extended Kalman filter equation is introduced to correct the predicted state vector and output the real-time position of the positioning object.

[0046] The beneficial effects of this invention are as follows: This invention provides a hybrid indoor positioning method and system based on Kalman filtering. By simultaneously acquiring Bluetooth Low Energy (BLE) signals, ultra-wideband (UWB) signals, and motion state data of the target object, it constructs an extended Kalman filter positioning model that integrates BLE and UWB signals. This model dynamically adapts to the UWB signal acquisition frequency and accurately corrects the positioning state. Based on the combined judgment results of movement speed and trajectory curvature, it dynamically adjusts the UWB signal acquisition frequency, solving the problem of poor positioning accuracy and energy consumption balance. The received signal strength of the BLE signal and the arrival time difference of the UWB signal are used to form a measurement vector. This vector is then used for time update prediction and measurement update correction via extended Kalman filtering to output the real-time position. Combined with a logarithmic distance path loss model to optimize the data fusion logic, this improves positioning stability in dynamic scenarios and meets the practical application requirements of indoor positioning.

[0047] Furthermore, the design principle of this invention is reliable, the structure is simple, and it has a very wide range of application prospects. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of a hybrid indoor positioning method based on Kalman filtering provided by the present invention.

[0050] Figure 2 This is a schematic diagram of a hybrid indoor positioning system based on Kalman filtering provided by the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0053] Example 1:

[0054] like Figure 1 As shown, this embodiment of the invention provides a hybrid indoor positioning method based on Kalman filtering, including the following steps:

[0055] Step S1: Collect the received signal strength of Bluetooth Low Energy signal and the reception time of UWB signal, and simultaneously collect the motion state data of the located object;

[0056] Step S2: Construct a positioning model based on the extended Kalman filter algorithm, and use the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector;

[0057] Step S3: Dynamically adjust the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature;

[0058] Step S4: In the time update stage, introduce the state transition matrix and the process noise covariance matrix to predict the current state vector and the state covariance matrix;

[0059] Step S5: The received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time are combined to form a measurement vector. The extended Kalman filter equation is introduced in the measurement update stage to correct the predicted state vector and output the real-time position of the positioning object.

[0060] Example 2:

[0061] like Figure 1 As shown, this embodiment of the invention provides a hybrid indoor positioning method based on Kalman filtering, including the following steps:

[0062] Step S1: Collect the received signal strength of Bluetooth Low Energy signal and the reception time of UWB signal, and simultaneously collect the motion state data of the located object;

[0063] Step S2: Construct a positioning model based on the extended Kalman filter algorithm, and use the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector;

[0064] Step S3: Dynamically adjust the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature;

[0065] Step S4: In the time update stage, introduce the state transition matrix and the process noise covariance matrix to predict the current state vector and the state covariance matrix;

[0066] Step S5: The received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time are combined to form a measurement vector. The extended Kalman filter equation is introduced in the measurement update stage to correct the predicted state vector and output the real-time position of the positioning object.

[0067] In step S1, the received signal strength of Bluetooth Low Energy signal and the received time of Ultra Wideband signal are collected, and the motion state data of the positioning object are collected simultaneously.

[0068] The time difference of arrival (TDOA) of an ultra-wideband (UWB) signal is calculated based on the difference in its reception time. The mathematical expression is as follows:

[0069] ;

[0070] in, Let be the arrival time difference between the i-th receiving point and the j-th receiving point. Let be the ultra-wideband signal reception time at the i-th receiving point. Let be the ultra-wideband signal reception time at the j-th receiving point;

[0071] The motion state data includes the moving speed and trajectory curvature. The moving speed is calculated by integrating the acceleration data of the positioning object, and the trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object.

[0072] In one embodiment, a positioning tag integrating a Bluetooth Low Energy (BLE) module, an Ultra Wideband (UWB) module, and a motion sensor is used. The positioning tag adopts a low-power design. The BLE module uses the CC2650 LaunchPad chip, supports the 2.4GHz band, and broadcasts data packets at a frequency of 10Hz to ensure a high update rate of positioning results. The UWB module uses the DecaWave DW1000 chip, with an initial transmission frequency set to 0.25Hz, which can be dynamically adjusted later. The motion sensor uses the ADXL345 triaxial accelerometer, with a sampling frequency consistent with the BLE module (10Hz), synchronously collecting acceleration data of the positioning object. A positioning network is formed by 6 BLE anchor points and 6 UWB anchor points. The anchor points are evenly deployed on the indoor ceiling, with a spacing controlled at 5-8 meters to ensure no dead zones in signal coverage. After the positioning tag is activated, it synchronously collects motion status data of the positioned object and simultaneously collects two types of signal parameters at a preset frequency: The BLE module broadcasts data packets at a frequency of 10Hz, and the BLE anchor point collects the received signal strength of the Bluetooth Low Energy signal when receiving the data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication; The UWB module sends signals at a preset frequency of 0.25Hz-10Hz, and the UWB anchor point accurately records the signal reception time, collecting the reception time of the ultra-wideband signal with a time measurement accuracy of 0.2ns.

[0073] In one embodiment, the BLE anchor uses Texas Instruments' CC2540 USB evaluation kit to acquire the received signal strength of the Bluetooth Low Energy signal when receiving BLE data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication. The UWB anchor is built based on the DW1000 chip, with a time measurement accuracy of 0.2ns. When receiving UWB data packets, it acquires the reception time of the ultra-wideband signal and uploads the reception timestamp data to the system controller.

[0074] Acceleration data of the positioning object is acquired using a triaxial accelerometer at a frequency of 10Hz. The movement speed is calculated by integrating the acquired acceleration data, with the integration time step consistent with the acceleration acquisition frequency (e.g., 0.1s). The trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object. The position coordinates of the five most recent moments are selected, and a quadratic polynomial is used to fit the trajectory curve. The curvature is then calculated using the curvature formula, and the curvature at the midpoint of the fitted curve is taken as the current trajectory curvature.

[0075] The Time Difference of Arrival (TDOA) of the UWB signal is calculated based on the difference in reception time of the UWB signal. One UWB anchor point is selected as the reference anchor point (e.g., the first one). Then, the TDOA data for the m-th group is... That is, m=5 sets of TDOA measurement data, which effectively avoids time difference redundancy between multiple anchor points.

[0076] Example 3:

[0077] like Figure 1 As shown, this embodiment of the invention provides a hybrid indoor positioning method based on Kalman filtering, including the following steps:

[0078] Step S1: Collect the received signal strength of Bluetooth Low Energy signal and the reception time of UWB signal, and simultaneously collect the motion state data of the located object;

[0079] Step S2: Construct a positioning model based on the extended Kalman filter algorithm, and use the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector;

[0080] Step S3: Dynamically adjust the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature;

[0081] Step S4: In the time update stage, introduce the state transition matrix and the process noise covariance matrix to predict the current state vector and the state covariance matrix;

[0082] Step S5: The received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time are combined to form a measurement vector. The extended Kalman filter equation is introduced in the measurement update stage to correct the predicted state vector and output the real-time position of the positioning object.

[0083] In step S1, the received signal strength of Bluetooth Low Energy signal and the received time of Ultra Wideband signal are collected, and the motion state data of the positioning object are collected simultaneously.

[0084] The time difference of arrival (TDOA) of an ultra-wideband (UWB) signal is calculated based on the difference in its reception time. The mathematical expression is as follows:

[0085] ;

[0086] in, Let be the arrival time difference between the i-th receiving point and the j-th receiving point. Let be the ultra-wideband signal reception time at the i-th receiving point. Let be the ultra-wideband signal reception time at the j-th receiving point;

[0087] The motion state data includes the moving speed and trajectory curvature. The moving speed is calculated by integrating the acceleration data of the positioning object, and the trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object.

[0088] In one embodiment, a positioning tag integrating a Bluetooth Low Energy (BLE) module, an Ultra Wideband (UWB) module, and a motion sensor is used. The positioning tag adopts a low-power design. The BLE module uses the CC2650 LaunchPad chip, supports the 2.4GHz band, and broadcasts data packets at a frequency of 10Hz to ensure a high update rate of positioning results. The UWB module uses the DecaWave DW1000 chip, with an initial transmission frequency set to 0.25Hz, which can be dynamically adjusted later. The motion sensor uses the ADXL345 triaxial accelerometer, with a sampling frequency consistent with the BLE module (10Hz), synchronously collecting acceleration data of the positioning object. A positioning network is formed by 6 BLE anchor points and 6 UWB anchor points. The anchor points are evenly deployed on the indoor ceiling, with a spacing controlled at 5-8 meters to ensure no dead zones in signal coverage. After the positioning tag is activated, it synchronously collects motion status data of the positioned object and simultaneously collects two types of signal parameters at a preset frequency: The BLE module broadcasts data packets at a frequency of 10Hz, and the BLE anchor point collects the received signal strength of the Bluetooth Low Energy signal when receiving the data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication; The UWB module sends signals at a preset frequency of 0.25Hz-10Hz, and the UWB anchor point accurately records the signal reception time, collecting the reception time of the ultra-wideband signal with a time measurement accuracy of 0.2ns.

[0089] In one embodiment, the BLE anchor uses Texas Instruments' CC2540 USB evaluation kit to acquire the received signal strength of the Bluetooth Low Energy signal when receiving BLE data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication. The UWB anchor is built based on the DW1000 chip, with a time measurement accuracy of 0.2ns. When receiving UWB data packets, it acquires the reception time of the ultra-wideband signal and uploads the reception timestamp data to the system controller.

[0090] Acceleration data of the positioning object is acquired using a triaxial accelerometer at a frequency of 10Hz. The movement speed is calculated by integrating the acquired acceleration data, with the integration time step consistent with the acceleration acquisition frequency (e.g., 0.1s). The trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object. The position coordinates of the five most recent moments are selected, and a quadratic polynomial is used to fit the trajectory curve. The curvature is then calculated using the curvature formula, and the curvature at the midpoint of the fitted curve is taken as the current trajectory curvature.

[0091] The Time Difference of Arrival (TDOA) of the UWB signal is calculated based on the difference in reception time of the UWB signal. One UWB anchor point is selected as the reference anchor point (e.g., the first one). Then, the TDOA data for the m-th group is... That is, m=5 sets of TDOA measurement data, which effectively avoids time difference redundancy between multiple anchor points.

[0092] In step S2, a positioning model is constructed based on the extended Kalman filter algorithm, and the two-dimensional position coordinates and corresponding velocity components of the positioning tag are used as the state vector.

[0093] State vector Represented as: Where x and y are the two-dimensional position coordinates of the positioning tag. Let x be the velocity components corresponding to x and y, and k be the time step number.

[0094] Example 4:

[0095] like Figure 1 As shown, this embodiment of the invention provides a hybrid indoor positioning method based on Kalman filtering, including the following steps:

[0096] Step S1: Collect the received signal strength of Bluetooth Low Energy signal and the reception time of UWB signal, and simultaneously collect the motion state data of the located object;

[0097] Step S2: Construct a positioning model based on the extended Kalman filter algorithm, and use the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector;

[0098] Step S3: Dynamically adjust the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature;

[0099] Step S4: In the time update stage, introduce the state transition matrix and the process noise covariance matrix to predict the current state vector and the state covariance matrix;

[0100] Step S5: The received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time are combined to form a measurement vector. The extended Kalman filter equation is introduced in the measurement update stage to correct the predicted state vector and output the real-time position of the positioning object.

[0101] In step S1, the received signal strength of Bluetooth Low Energy signal and the received time of Ultra Wideband signal are collected, and the motion state data of the positioning object are collected simultaneously.

[0102] The time difference of arrival (TDOA) of an ultra-wideband (UWB) signal is calculated based on the difference in its reception time. The mathematical expression is as follows:

[0103] ;

[0104] in, Let be the arrival time difference between the i-th receiving point and the j-th receiving point. Let be the ultra-wideband signal reception time at the i-th receiving point. Let be the ultra-wideband signal reception time at the j-th receiving point;

[0105] The motion state data includes the moving speed and trajectory curvature. The moving speed is calculated by integrating the acceleration data of the positioning object, and the trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object.

[0106] In one embodiment, a positioning tag integrating a Bluetooth Low Energy (BLE) module, an Ultra Wideband (UWB) module, and a motion sensor is used. The positioning tag adopts a low-power design. The BLE module uses the CC2650 LaunchPad chip, supports the 2.4GHz band, and broadcasts data packets at a frequency of 10Hz to ensure a high update rate of positioning results. The UWB module uses the DecaWave DW1000 chip, with an initial transmission frequency set to 0.25Hz, which can be dynamically adjusted later. The motion sensor uses the ADXL345 triaxial accelerometer, with a sampling frequency consistent with the BLE module (10Hz), synchronously collecting acceleration data of the positioning object. A positioning network is formed by 6 BLE anchor points and 6 UWB anchor points. The anchor points are evenly deployed on the indoor ceiling, with a spacing controlled at 5-8 meters to ensure no dead zones in signal coverage. After the positioning tag is activated, it synchronously collects motion status data of the positioned object and simultaneously collects two types of signal parameters at a preset frequency: The BLE module broadcasts data packets at a frequency of 10Hz, and the BLE anchor point collects the received signal strength of the Bluetooth Low Energy signal when receiving the data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication; The UWB module sends signals at a preset frequency of 0.25Hz-10Hz, and the UWB anchor point accurately records the signal reception time, collecting the reception time of the ultra-wideband signal with a time measurement accuracy of 0.2ns.

[0107] In one embodiment, the BLE anchor uses Texas Instruments' CC2540 USB evaluation kit to acquire the received signal strength of the Bluetooth Low Energy signal when receiving BLE data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication. The UWB anchor is built based on the DW1000 chip, with a time measurement accuracy of 0.2ns. When receiving UWB data packets, it acquires the reception time of the ultra-wideband signal and uploads the reception timestamp data to the system controller.

[0108] Acceleration data of the positioning object is acquired using a triaxial accelerometer at a frequency of 10Hz. The movement speed is calculated by integrating the acquired acceleration data, with the integration time step consistent with the acceleration acquisition frequency (e.g., 0.1s). The trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object. The position coordinates of the five most recent moments are selected, and a quadratic polynomial is used to fit the trajectory curve. The curvature is then calculated using the curvature formula, and the curvature at the midpoint of the fitted curve is taken as the current trajectory curvature.

[0109] The Time Difference of Arrival (TDOA) of the UWB signal is calculated based on the difference in reception time of the UWB signal. One UWB anchor point is selected as the reference anchor point (e.g., the first one). Then, the TDOA data for the m-th group is... That is, m=5 sets of TDOA measurement data, which effectively avoids time difference redundancy between multiple anchor points.

[0110] In step S2, a positioning model is constructed based on the extended Kalman filter algorithm, and the two-dimensional position coordinates and corresponding velocity components of the positioning tag are used as the state vector.

[0111] State vector Represented as: Where x and y are the two-dimensional position coordinates of the positioning tag. Let x be the velocity components corresponding to x and y, and k be the time step number.

[0112] In step S3, the acquisition frequency of the ultra-wideband signal is dynamically adjusted based on the combined judgment result of the moving speed and trajectory curvature.

[0113] Dynamically adjusting the acquisition frequency of the ultra-wideband signal includes increasing the acquisition frequency of the ultra-wideband signal when the moving speed exceeds a preset threshold or the trajectory curvature exceeds a preset threshold. Preset threshold and trajectory curvature When the preset threshold is reached, the sampling frequency of the ultra-wideband signal is reduced.

[0114] In one embodiment, when the moving speed is greater than 1.4 m / s or the trajectory curvature is greater than a preset threshold, the acquisition frequency of the ultra-wideband signal is increased. In this case, if the initial frequency of the UWB module is set to 0.25 Hz, the UWB module frequency is increased to 2 Hz-5 Hz to ensure sufficient high-precision TDOA data is acquired even under complex motion conditions, thus guaranteeing positioning accuracy; moving speed 1.4 m / s and trajectory curvature When the preset threshold is reached, the acquisition frequency of the ultra-wideband signal is reduced, and the UWB module frequency is maintained between 0.25Hz and 1Hz to reduce unnecessary power consumption. When the target object is stationary (moving speed is 0), the UWB frequency drops to 0.25Hz to maximize power saving. The frequency adjustment period is consistent with the broadcast period of the BLE module (e.g., 0.1s) to ensure timely adjustment.

[0115] Example 5:

[0116] like Figure 1 As shown, this embodiment of the invention provides a hybrid indoor positioning method based on Kalman filtering, including the following steps:

[0117] Step S1: Collect the received signal strength of Bluetooth Low Energy signal and the reception time of UWB signal, and simultaneously collect the motion state data of the located object;

[0118] Step S2: Construct a positioning model based on the extended Kalman filter algorithm, and use the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector;

[0119] Step S3: Dynamically adjust the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature;

[0120] Step S4: In the time update stage, introduce the state transition matrix and the process noise covariance matrix to predict the current state vector and the state covariance matrix;

[0121] Step S5: The received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time are combined to form a measurement vector. The extended Kalman filter equation is introduced in the measurement update stage to correct the predicted state vector and output the real-time position of the positioning object.

[0122] In step S1, the received signal strength of Bluetooth Low Energy signal and the received time of Ultra Wideband signal are collected, and the motion state data of the positioning object are collected simultaneously.

[0123] The time difference of arrival (TDOA) of an ultra-wideband (UWB) signal is calculated based on the difference in its reception time. The mathematical expression is as follows:

[0124] ;

[0125] in, Let be the arrival time difference between the i-th receiving point and the j-th receiving point. Let be the ultra-wideband signal reception time at the i-th receiving point. Let be the ultra-wideband signal reception time at the j-th receiving point;

[0126] The motion state data includes the moving speed and trajectory curvature. The moving speed is calculated by integrating the acceleration data of the positioning object, and the trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object.

[0127] In one embodiment, a positioning tag integrating a Bluetooth Low Energy (BLE) module, an Ultra Wideband (UWB) module, and a motion sensor is used. The positioning tag adopts a low-power design. The BLE module uses the CC2650 LaunchPad chip, supports the 2.4GHz band, and broadcasts data packets at a frequency of 10Hz to ensure a high update rate of positioning results. The UWB module uses the DecaWave DW1000 chip, with an initial transmission frequency set to 0.25Hz, which can be dynamically adjusted later. The motion sensor uses the ADXL345 triaxial accelerometer, with a sampling frequency consistent with the BLE module (10Hz), synchronously collecting acceleration data of the positioning object. A positioning network is formed by 6 BLE anchor points and 6 UWB anchor points. The anchor points are evenly deployed on the indoor ceiling, with a spacing controlled at 5-8 meters to ensure no dead zones in signal coverage. After the positioning tag is activated, it synchronously collects motion status data of the positioned object and simultaneously collects two types of signal parameters at a preset frequency: The BLE module broadcasts data packets at a frequency of 10Hz, and the BLE anchor point collects the received signal strength of the Bluetooth Low Energy signal when receiving the data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication; The UWB module sends signals at a preset frequency of 0.25Hz-10Hz, and the UWB anchor point accurately records the signal reception time, collecting the reception time of the ultra-wideband signal with a time measurement accuracy of 0.2ns.

[0128] In one embodiment, the BLE anchor uses Texas Instruments' CC2540 USB evaluation kit to acquire the received signal strength of the Bluetooth Low Energy signal when receiving BLE data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication. The UWB anchor is built based on the DW1000 chip, with a time measurement accuracy of 0.2ns. When receiving UWB data packets, it acquires the reception time of the ultra-wideband signal and uploads the reception timestamp data to the system controller.

[0129] Acceleration data of the positioning object is acquired using a triaxial accelerometer at a frequency of 10Hz. The movement speed is calculated by integrating the acquired acceleration data, with the integration time step consistent with the acceleration acquisition frequency (e.g., 0.1s). The trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object. The position coordinates of the five most recent moments are selected, and a quadratic polynomial is used to fit the trajectory curve. The curvature is then calculated using the curvature formula, and the curvature at the midpoint of the fitted curve is taken as the current trajectory curvature.

[0130] The Time Difference of Arrival (TDOA) of the UWB signal is calculated based on the difference in reception time of the UWB signal. One UWB anchor point is selected as the reference anchor point (e.g., the first one). Then, the TDOA data for the m-th group is... That is, m=5 sets of TDOA measurement data, which effectively avoids time difference redundancy between multiple anchor points.

[0131] In step S2, a positioning model is constructed based on the extended Kalman filter algorithm, and the two-dimensional position coordinates and corresponding velocity components of the positioning tag are used as the state vector.

[0132] State vector Represented as: Where x and y are the two-dimensional position coordinates of the positioning tag. Let x be the velocity components corresponding to x and y, and k be the time step number.

[0133] In step S3, the acquisition frequency of the ultra-wideband signal is dynamically adjusted based on the combined judgment result of the moving speed and trajectory curvature.

[0134] Dynamically adjusting the acquisition frequency of the ultra-wideband signal includes increasing the acquisition frequency of the ultra-wideband signal when the moving speed exceeds a preset threshold or the trajectory curvature exceeds a preset threshold. Preset threshold and trajectory curvature When the preset threshold is reached, the sampling frequency of the ultra-wideband signal is reduced.

[0135] In one embodiment, when the moving speed is greater than 1.4 m / s or the trajectory curvature is greater than a preset threshold, the acquisition frequency of the ultra-wideband signal is increased. In this case, if the initial frequency of the UWB module is set to 0.25 Hz, the UWB module frequency is increased to 2 Hz-5 Hz to ensure sufficient high-precision TDOA data is acquired even under complex motion conditions, thus guaranteeing positioning accuracy; moving speed 1.4 m / s and trajectory curvature When the preset threshold is reached, the acquisition frequency of the ultra-wideband signal is reduced, and the UWB module frequency is maintained between 0.25Hz and 1Hz to reduce unnecessary power consumption. When the target object is stationary (moving speed is 0), the UWB frequency drops to 0.25Hz to maximize power saving. The frequency adjustment period is consistent with the broadcast period of the BLE module (e.g., 0.1s) to ensure timely adjustment.

[0136] In step S4, a state transition matrix and a process noise covariance matrix are introduced during the time update phase to predict the current state vector and the state covariance matrix.

[0137] The mathematical expression for the time update phase is:

[0138] ,

[0139] ;

[0140] in, To predict the state vector, This is the optimal state vector of the previous iteration cycle. and They are respectively and The corresponding state covariance matrix, Here is the state transition matrix. The process noise covariance matrix is ​​obtained using a discrete white noise acceleration model.

[0141] Example 6:

[0142] like Figure 1 As shown, this embodiment of the invention provides a hybrid indoor positioning method based on Kalman filtering, including the following steps:

[0143] Step S1: Collect the received signal strength of Bluetooth Low Energy signal and the reception time of UWB signal, and simultaneously collect the motion state data of the located object;

[0144] Step S2: Construct a positioning model based on the extended Kalman filter algorithm, and use the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector;

[0145] Step S3: Dynamically adjust the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature;

[0146] Step S4: In the time update stage, introduce the state transition matrix and the process noise covariance matrix to predict the current state vector and the state covariance matrix;

[0147] Step S5: The received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time are combined to form a measurement vector. The extended Kalman filter equation is introduced in the measurement update stage to correct the predicted state vector and output the real-time position of the positioning object.

[0148] In step S1, the received signal strength of Bluetooth Low Energy signal and the received time of Ultra Wideband signal are collected, and the motion state data of the positioning object are collected simultaneously.

[0149] The time difference of arrival (TDOA) of an ultra-wideband (UWB) signal is calculated based on the difference in its reception time. The mathematical expression is as follows:

[0150] ;

[0151] in, Let be the arrival time difference between the i-th receiving point and the j-th receiving point. Let be the ultra-wideband signal reception time at the i-th receiving point. Let be the ultra-wideband signal reception time at the j-th receiving point;

[0152] The motion state data includes the moving speed and trajectory curvature. The moving speed is calculated by integrating the acceleration data of the positioning object, and the trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object.

[0153] In one embodiment, a positioning tag integrating a Bluetooth Low Energy (BLE) module, an Ultra Wideband (UWB) module, and a motion sensor is used. The positioning tag adopts a low-power design. The BLE module uses the CC2650 LaunchPad chip, supports the 2.4GHz band, and broadcasts data packets at a frequency of 10Hz to ensure a high update rate of positioning results. The UWB module uses the DecaWave DW1000 chip, with an initial transmission frequency set to 0.25Hz, which can be dynamically adjusted later. The motion sensor uses the ADXL345 triaxial accelerometer, with a sampling frequency consistent with the BLE module (10Hz), synchronously collecting acceleration data of the positioning object. A positioning network is formed by 6 BLE anchor points and 6 UWB anchor points. The anchor points are evenly deployed on the indoor ceiling, with a spacing controlled at 5-8 meters to ensure no dead zones in signal coverage. After the positioning tag is activated, it synchronously collects motion status data of the positioned object and simultaneously collects two types of signal parameters at a preset frequency: The BLE module broadcasts data packets at a frequency of 10Hz, and the BLE anchor point collects the received signal strength of the Bluetooth Low Energy signal when receiving the data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication; The UWB module sends signals at a preset frequency of 0.25Hz-10Hz, and the UWB anchor point accurately records the signal reception time, collecting the reception time of the ultra-wideband signal with a time measurement accuracy of 0.2ns.

[0154] In one embodiment, the BLE anchor uses Texas Instruments' CC2540 USB evaluation kit to acquire the received signal strength of the Bluetooth Low Energy signal when receiving BLE data packets, with a measurement resolution of 1dB. The data is transmitted to the system controller in real time via wireless communication. The UWB anchor is built based on the DW1000 chip, with a time measurement accuracy of 0.2ns. When receiving UWB data packets, it acquires the reception time of the ultra-wideband signal and uploads the reception timestamp data to the system controller.

[0155] Acceleration data of the positioning object is acquired using a triaxial accelerometer at a frequency of 10Hz. The movement speed is calculated by integrating the acquired acceleration data, with the integration time step consistent with the acceleration acquisition frequency (e.g., 0.1s). The trajectory curvature is obtained by fitting a curve to the position coordinate sequence of the positioning object. The position coordinates of the five most recent moments are selected, and a quadratic polynomial is used to fit the trajectory curve. The curvature is then calculated using the curvature formula, and the curvature at the midpoint of the fitted curve is taken as the current trajectory curvature.

[0156] The Time Difference of Arrival (TDOA) of the UWB signal is calculated based on the difference in reception time of the UWB signal. One UWB anchor point is selected as the reference anchor point (e.g., the first one). Then, the TDOA data for the m-th group is... That is, m=5 sets of TDOA measurement data, which effectively avoids time difference redundancy between multiple anchor points.

[0157] In step S2, a positioning model is constructed based on the extended Kalman filter algorithm, and the two-dimensional position coordinates and corresponding velocity components of the positioning tag are used as the state vector.

[0158] State vector Represented as: Where x and y are the two-dimensional position coordinates of the positioning tag. Let x be the velocity components corresponding to x and y, and k be the time step number.

[0159] In step S3, the acquisition frequency of the ultra-wideband signal is dynamically adjusted based on the combined judgment result of the moving speed and trajectory curvature.

[0160] Dynamically adjusting the acquisition frequency of the ultra-wideband signal includes increasing the acquisition frequency of the ultra-wideband signal when the moving speed exceeds a preset threshold or the trajectory curvature exceeds a preset threshold. Preset threshold and trajectory curvature When the preset threshold is reached, the sampling frequency of the ultra-wideband signal is reduced.

[0161] In one embodiment, when the moving speed is greater than 1.4 m / s or the trajectory curvature is greater than a preset threshold, the acquisition frequency of the ultra-wideband signal is increased. In this case, if the initial frequency of the UWB module is set to 0.25 Hz, the UWB module frequency is increased to 2 Hz-5 Hz to ensure sufficient high-precision TDOA data is acquired even under complex motion conditions, thus guaranteeing positioning accuracy; moving speed 1.4 m / s and trajectory curvature When the preset threshold is reached, the acquisition frequency of the ultra-wideband signal is reduced, and the UWB module frequency is maintained between 0.25Hz and 1Hz to reduce unnecessary power consumption. When the target object is stationary (moving speed is 0), the UWB frequency drops to 0.25Hz to maximize power saving. The frequency adjustment period is consistent with the broadcast period of the BLE module (e.g., 0.1s) to ensure timely adjustment.

[0162] In step S4, a state transition matrix and a process noise covariance matrix are introduced during the time update phase to predict the current state vector and the state covariance matrix.

[0163] The mathematical expression for the time update phase is:

[0164] ,

[0165] ;

[0166] in, To predict the state vector, This is the optimal state vector of the previous iteration cycle. and They are respectively and The corresponding state covariance matrix, Here is the state transition matrix. The process noise covariance matrix is ​​obtained using a discrete white noise acceleration model.

[0167] In step S5, the received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time are combined to form a measurement vector. The predicted state vector is corrected by introducing the extended Kalman filter equation in the measurement update stage, and the real-time position of the positioning object is output.

[0168] The measurement update phase introduces the extended Kalman filter equation to correct the predicted state vector, including:

[0169] ,

[0170] ,

[0171] ;

[0172] in, Here is the Kalman gain matrix. To predict the state vector, for The corresponding state covariance matrix, The linearization matrix of the measurement function, This is the corrected optimal state vector. For measurement function, To measure the noise covariance matrix, For measurement vectors, It is the identity matrix;

[0173] When only the received signal strength data is acquired, ;

[0174] When simultaneously acquiring received signal strength data and time difference of arrival data acquired periodically based on the ultra-wideband signal reception time, Where n is the number of Bluetooth Low Energy module anchor points, and m is the number of time difference of arrival measurement groups. This is the received signal strength data for the nth Bluetooth Low Energy module anchor point. This is the arrival time difference measurement data for the m-th group.

[0175] The theoretical value of the received signal strength is calculated using the logarithmic distance path loss model. The mathematical expression is as follows:

[0176] ;

[0177] in, For reference distance The received power at that location, For path loss power, To predict the distance between the location and the nth Bluetooth Low Energy module anchor point.

[0178] Output the real-time position of the located object as a corrected optimal state vector. The two-dimensional position coordinate components in the image are used to output the real-time position of the object being located. ,in This refers to the real-time position coordinates of the object along the x-axis. This refers to the real-time position coordinates of the object along the y-axis. , Corresponding to the optimal state vector The values ​​of the x and y components.

[0179] In one embodiment, setting Data collected based on actual scenarios , , The mathematical expression is calculated based on the predicted state vector and the coordinates of the nth Bluetooth Low Energy module anchor point: ,in, For predicting the state vector, Let be the coordinates of the anchor point of the nth Bluetooth Low Energy module.

[0180] Example 7:

[0181] like Figure 2 As shown, this embodiment also provides a hybrid indoor positioning system based on Kalman filtering, including a data acquisition module 1, a model building module 2, a frequency adjustment module 3, a state prediction module 4, and a state correction module 5;

[0182] Data acquisition module 1 acquires the received signal strength of Bluetooth Low Energy signal and the reception time of UWB signal, and simultaneously acquires motion state data of the located object;

[0183] Among them, motion state data includes movement speed and trajectory curvature, and the arrival time difference of ultra-wideband signals is calculated based on the difference in reception time of ultra-wideband signals;

[0184] Model building module 2 constructs a positioning model based on the extended Kalman filter algorithm, using the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector;

[0185] Frequency adjustment module 3 dynamically adjusts the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature.

[0186] The dynamic adjustment of the ultra-wideband signal acquisition frequency includes: increasing the acquisition frequency of the ultra-wideband signal when the moving speed is greater than a preset threshold or the trajectory curvature is greater than a preset threshold, and adjusting the moving speed accordingly. Preset threshold and trajectory curvature When the preset threshold is reached, the sampling frequency of the ultra-wideband signal is reduced.

[0187] State prediction module 4 introduces the state transition matrix and process noise covariance matrix during the time update phase to predict the current state vector and state covariance matrix.

[0188] The state correction module 5 combines the received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time to form a measurement vector. Through the measurement update stage, the extended Kalman filter equation is introduced to correct the predicted state vector and output the real-time position of the positioning object.

[0189] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The methods disclosed in the embodiments are described simply because they correspond to the systems disclosed in the embodiments; relevant details can be found in the method section.

[0190] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0191] In the embodiments provided by this invention, it should be understood that the disclosed systems, methods, and approaches can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0192] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0193] In addition, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit.

[0194] Similarly, in the various embodiments of the present invention, each processing unit can be integrated into a functional module, or each processing unit can exist physically, or two or more processing units can be integrated into a functional module.

[0195] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0196] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0197] The above-disclosed embodiments are merely preferred embodiments of the present invention, but the present invention is not limited thereto. Any non-creative variations that can be conceived by those skilled in the art, as well as any improvements and modifications made without departing from the principles of the present invention, should fall within the protection scope of the present invention.

Claims

1. A hybrid indoor positioning method based on Kalman filtering, characterized in that, Includes the following steps: Step S1: Collect the received signal strength of Bluetooth Low Energy signal and the reception time of UWB signal, and simultaneously collect the motion state data of the located object; Step S2: Construct a positioning model based on the extended Kalman filter algorithm, and use the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector; Step S3: Dynamically adjust the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature; Step S4: In the time update stage, introduce the state transition matrix and the process noise covariance matrix to predict the current state vector and the state covariance matrix; Step S5: The received signal strength and the arrival time difference obtained periodically based on the ultra-wideband signal reception time are combined to form a measurement vector. The predicted state vector is corrected by introducing the extended Kalman filter equation in the measurement update stage, and the real-time position of the positioning object is output. The measurement update phase introduces the extended Kalman filter equation to correct the predicted state vector, including: ， ， ； in, Here is the Kalman gain matrix. To predict the state vector, for The corresponding state covariance matrix, The linearization matrix of the measurement function, This is the corrected optimal state vector. For measurement function, To measure the noise covariance matrix, For measurement vectors, It is the identity matrix; When only the received signal strength data is acquired, ; When simultaneously acquiring received signal strength data and time difference of arrival data acquired periodically based on the ultra-wideband signal reception time, Where n is the number of Bluetooth Low Energy module anchor points, and m is the number of time difference of arrival measurement groups. This is the received signal strength data for the nth Bluetooth Low Energy module anchor point. This is the arrival time difference measurement data for the m-th group; The theoretical value of the received signal strength is calculated using the logarithmic distance path loss model. The mathematical expression is as follows: ； in, For reference distance The received power at that location, For path loss power, To predict the distance between the location and the anchor point of the nth Bluetooth Low Energy module; The real-time position of the output positioning object is represented as follows: ,in , represents the real-time position coordinates of the object along the x-axis. This refers to the real-time position coordinates of the object along the y-axis. , Corresponding to the optimal state vector The values ​​of the x and y components.

2. The hybrid indoor positioning method based on Kalman filtering according to claim 1, characterized in that, Step S1 further includes calculating the arrival time difference of the ultra-wideband signal based on the difference in the reception time of the ultra-wideband signal, the mathematical expression of which is: ； in, Let be the arrival time difference between the i-th receiving point and the j-th receiving point. Let be the ultra-wideband signal reception time at the i-th receiving point. Let be the ultra-wideband signal reception time at the j-th receiving point; Motion data includes movement speed and trajectory curvature.

3. The hybrid indoor positioning method based on Kalman filtering according to claim 1, characterized in that, In step S2, the state vector Represented as: Where x and y are the two-dimensional position coordinates of the positioning tag. Let x be the velocity components corresponding to x and y, and k be the time step number.

4. The hybrid indoor positioning method based on Kalman filtering according to claim 2, characterized in that, In step S3, dynamically adjusting the acquisition frequency of the ultra-wideband signal includes increasing the acquisition frequency of the ultra-wideband signal when the moving speed is greater than a preset threshold or the trajectory curvature is greater than a preset threshold. Preset threshold and trajectory curvature When the preset threshold is reached, the sampling frequency of the ultra-wideband signal is reduced.

5. The hybrid indoor positioning method based on Kalman filtering according to claim 1, characterized in that, In step S4, the mathematical expression for the time update phase is: ， ； in, To predict the state vector, This is the optimal state vector of the previous iteration cycle. and They are respectively and The corresponding state covariance matrix, Here is the state transition matrix. Let be the process noise covariance matrix.

6. A system for the hybrid indoor positioning method based on Kalman filtering as described in claim 1, characterized in that, It includes a data acquisition module, a model building module, a frequency adjustment module, a state prediction module, and a state correction module; The data acquisition module collects the received signal strength of Bluetooth Low Energy signals and the reception time of Ultra Wideband signals, and simultaneously collects the motion status data of the located object. The model building module constructs a positioning model based on the extended Kalman filter algorithm, using the two-dimensional position coordinates and corresponding velocity components of the positioning tag as the state vector; The frequency adjustment module dynamically adjusts the acquisition frequency of the ultra-wideband signal based on the combined judgment results of the moving speed and trajectory curvature. The state prediction module introduces the state transition matrix and process noise covariance matrix during the time update phase to predict the current state vector and state covariance matrix. The state correction module combines the received signal strength and the time difference of arrival obtained periodically based on the ultra-wideband signal reception time to form a measurement vector. Through the measurement update stage, the extended Kalman filter equation is introduced to correct the predicted state vector and output the real-time position of the positioning object.

7. The system according to claim 6, characterized in that, The motion state data in the data acquisition module includes moving speed and trajectory curvature. The arrival time difference of the ultra-wideband signal is calculated based on the difference in the reception time of the ultra-wideband signal. The frequency adjustment module dynamically adjusts the acquisition frequency of the ultra-wideband signal, including: increasing the acquisition frequency of the ultra-wideband signal when the moving speed exceeds a preset threshold or the trajectory curvature exceeds a preset threshold; and adjusting the moving speed... Preset threshold and trajectory curvature When the preset threshold is reached, the sampling frequency of the ultra-wideband signal is reduced.

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