Personal locator high-precision positioning method and system

Abstract

The invention discloses a high-precision positioning method and system for a personal positioner, relates to the field of high-precision positioning of personal positioners, is used for improving positioning precision and reliability, and comprises the following steps: acquiring inertial measurement unit data and wireless signal data of the personal positioner; a magnetometer, a gyroscope, an accelerometer and a barometer are arranged in the personal locator; according to the inertial measurement unit data and the wireless signal data, judging a current user behavior mode and an environment category; adjusting a positioning processing mode according to the user behavior mode and the environment category; the adjustment comprises changing the effect of different wireless signal data in positioning and modifying error description parameters of a position estimation program; and generating position information and corresponding credibility according to the adjusted positioning processing mode.

Description

Technical Field

[0001] This invention relates to the field of high-precision positioning of personal locators, and more particularly to a high-precision positioning method and system for personal locators. Background Technology

[0002] Traditional personal locators typically integrate multiple technologies to provide high-precision positioning services in complex urban environments or indoor settings. However, in some extremely complex urban environments, such as urban canyons filled with tall buildings, locators face multiple challenges, severely impacting their positioning accuracy and reliability, and potentially even outputting misleading or incorrect location information.

[0003] In practical applications, when a user wearing a GPS tracker moves from an open area into a typical urban canyon, the strength and quality of the GPS signal rapidly decline, or even disappear completely. At this point, the system switches to an inertial navigation mode primarily based on the inertial measurement unit (IMU), attempting to use cellular networks and Wi-Fi signals for auxiliary correction. However, the user's actual movement pattern in an urban canyon may not be a smooth, linear walk, but rather involves avoiding pedestrians, navigating temporary obstacles, or frequently stopping, turning, or even taking a few steps backward in crowded commercial areas. This highly irregular and non-linear movement pattern poses a severe challenge to the accuracy of the IMU's dead zone estimation. Inherent errors in the IMU, such as accelerometer bias drift and gyroscope random walk, accumulate over long periods or during complex movements. When the movement pattern deviates from the system's preset "normal walking" model, the accumulation rate of these errors accelerates significantly, leading to a rapid increase in the deviation between the inertial-estimated position and the actual position. A further challenge lies in certain urban canyon areas where not only are GPS signals limited, but even cellular and Wi-Fi signals used for auxiliary correction may become extremely sparse or unreliable. To make matters worse, users may pass through areas with unique electromagnetic environments during their movement. Under the combined effect of these adverse factors, the filtering algorithms within the positioning system, despite their design goal of fusing multi-source data and estimating uncertainties, may fail to adequately capture all these nonlinear and transient disturbances in such extremely complex scenarios.

[0004] Ultimately, when guardians or rescuers check the wearer's location via a mobile app, they will see a highly accurate location reported "confidently" by the system. However, this location information may be far from the wearer's actual location. This "high-confidence" false location result is more dangerous than no location at all, because it can mislead the judgment and actions of guardians or rescuers, potentially leading to serious consequences in an emergency. Summary of the Invention

[0005] This application discloses a high-precision positioning method and system for personal locators, aiming to solve the problem that in complex urban environments or indoor scenarios, the positioning accuracy and reliability of existing personal locators are severely affected by various challenges such as limited signal, complex user behavior patterns, and electromagnetic interference, and may even output misleading location information.

[0006] In a first aspect, this application discloses a high-precision positioning method for a personal locator, comprising the following steps: The system acquires inertial measurement unit data and wireless signal data from a personal locator; the personal locator has a built-in magnetometer, gyroscope, accelerometer, and barometer. Based on inertial measurement unit data and wireless signal data, determine the current user behavior pattern and the type of environment they are in; The location processing method is adjusted according to user behavior patterns and environmental categories; this adjustment includes changing the role of different wireless signal data in location and modifying the error description parameters of the location estimation program. Based on the adjusted positioning processing method, location information and corresponding credibility are generated.

[0007] Optionally, determine the current user behavior pattern, including: Based on the data from the inertial measurement unit, determine the user's step frequency, step length change rate, turning frequency, vertical displacement change rate, horizontal acceleration, and angular velocity; When a user's cadence is within a preset cadence range, the step length change rate is less than the step length change rate threshold, and the turning frequency is less than the frequency threshold, the user's behavior pattern is determined to be normal walking. When the step size change rate is detected to be greater than the step size change rate threshold within a preset time, the user behavior pattern is determined to be avoidance or shuttle. When the duration of the vertical displacement rate of change being greater than the vertical displacement rate of change threshold is greater than the duration threshold, and the horizontal acceleration and angular velocity are both less than the preset threshold, the user behavior pattern is determined to be riding an elevator; the vertical displacement rate of change is the rate of change of displacement in the direction perpendicular to the ground.

[0008] Optionally, determine the category of the environment, including: Based on the wireless signal data, determine the RSSI fluctuation amplitude, signal-to-noise ratio, number of visible signal sources, and MAC address of the wireless signal; If the RSSI fluctuation amplitude is greater than the preset fluctuation amplitude, or the signal-to-noise ratio is lower than the preset signal-to-noise ratio, or the number of visible signal sources is less than the preset number and the MAC address matching rate in the location server database is lower than the preset matching rate threshold, the environment category is determined to be a signal sparse environment. Based on the wireless signal data, determine the standard deviation of the magnetometer reading stability and the deviation between the magnetometer heading and the gyroscope integrated heading; If the standard deviation of the magnetometer reading stability is greater than the standard deviation threshold, or the deviation between the magnetometer heading and the gyroscope integrated heading is greater than the deviation threshold, the environment is determined to be an electromagnetic interference environment.

[0009] Optionally, the location processing method can be adjusted based on user behavior patterns and environment categories, including: When the user's behavior pattern is normal walking, the location processing method will not be adjusted; When the user's behavior pattern is avoidance or shuttle, reduce the weight of accelerometer and gyroscope data in heading and displacement estimation, increase the sensitivity to instantaneous zero velocity update or zero angular velocity update, and expand the covariance of attitude estimation in the filtering procedure. When the user's behavior pattern is taking an elevator, increase the weight of the barometer in vertical positioning and reduce the drift accumulation penalty in horizontal inertial estimation. When the environment is a signal-sparse environment, increase the measurement noise covariance of the auxiliary positioning signal in the filtering program to reduce its correction effect on the position estimation. When the environment is classified as an electromagnetic interference environment, the weight of magnetometer data in heading estimation is reduced, and the gyroscope integration is relied upon to maintain short-term heading.

[0010] Optionally, the method also includes: Acquire the gyroscope measurement data from the initial inertial measurement unit (IMU) data; the initial IMU data is the raw IMU data without any processing. To determine whether electromagnetic field characteristics at a specific frequency exist; Determine if there is an angular deviation between the gyroscope integrated attitude and the accelerometer-calculated attitude; When the presence of electromagnetic field characteristics at a specific frequency is determined, and an angular deviation exists, it is inferred that the gyroscope has a systematic bias. Estimate the gyroscope bias; The bias is subtracted from the gyroscope measurements in the initial inertial measurement unit (IMU) data to obtain the IMU data.

[0011] Optionally, the gyroscope bias may satisfy the following relationship: b_g_new=b_g_old+K_bias (delta_heading_error-b_g_old); Where b_g_new is the current bias of the gyroscope, b_g_old is the initial bias of the gyroscope, K_bias is the preset adaptive adjustment coefficient, and delta_heading_error is the instantaneous deviation between the gyroscope's integrated heading and the reference heading.

[0012] Optionally, determine whether there are electromagnetic field characteristics at a specific frequency, including: Identify energy intensity within a specific frequency range; If the duration of an energy intensity greater than a preset energy intensity threshold within a specific frequency range exceeds a preset duration, it is determined that an electromagnetic field characteristic exists. If the energy intensity within a specific frequency range is less than or equal to a preset energy intensity threshold, or if the duration of the energy intensity within a specific frequency range being greater than the preset energy intensity threshold does not exceed a preset duration, then it is determined that there is no electromagnetic field characteristic.

[0013] Optional, determining credibility includes: Extract various error features that cause positioning errors; these error features include inertial estimation drift features, wireless signal interference features, magnetic field interference features, and vertical displacement error features. Based on various error characteristics, an error contribution factor matrix is ​​determined; each element C_ij in the error contribution factor matrix represents the contribution of the i-th error source to the uncertainty of the j-th state variable; the state variables include position, velocity, and attitude. The confidence level is determined based on the error contribution factor matrix.

[0014] Optionally, the method also includes: Calculate the standard deviation of the accelerometer data within a preset time window, and the rate of change of the gyroscope angular velocity within a preset time window; When the standard deviation exceeds the standard deviation threshold and the rate of change is greater than the rate of change threshold, the accelerometer data is integrated to estimate the drift velocity without external correction. The drift characteristics are determined by inertial estimation based on the square of the drift velocity.

[0015] Secondly, this application also discloses a high-precision positioning system for a personal locator, the system comprising: The data acquisition module is used to acquire inertial measurement unit data and wireless signal data from the personal locator; the personal locator has a built-in magnetometer, gyroscope, accelerometer, and barometer; The context judgment module is used to determine the current user behavior pattern and the type of environment based on inertial measurement unit data and wireless signal data. The processing method adjustment module is used to adjust the positioning processing method according to user behavior patterns and environmental categories; the adjustment includes changing the role of different wireless signal data in positioning, and modifying the error description parameters of the location estimation program; The information generation and evaluation module is used to generate location information and corresponding credibility based on the adjusted positioning processing method.

[0016] Beneficial effects This application discloses a high-precision positioning method for personal locators. By acquiring inertial measurement unit (IMU) data and wireless signal data from the personal locator, and determining the current user behavior pattern and environmental category based on this data, the method dynamically adjusts the positioning processing approach. This includes changing the role of different wireless signal data in positioning and modifying the error description parameters of the location estimation program, ultimately generating location information and corresponding confidence levels. This method effectively solves the technical challenges in existing technologies, such as reduced positioning accuracy and reliability, and even the output of high-confidence erroneous location information, caused by problems like limited GNSS signals, irregular user movement patterns, sparse or unreliable auxiliary positioning signals, and electromagnetic interference in complex urban environments or indoor scenarios. Through intelligent judgment of user behavior patterns and environmental categories, this application can specifically optimize sensor data fusion strategies and error models, avoiding the limitations of traditional fixed-parameter positioning algorithms in complex scenarios. This significantly improves the accuracy and robustness of positioning and provides a reliable level that truly reflects the positioning quality, thus avoiding misleading users. It has significant practical application value in scenarios such as emergency rescue and elderly / child monitoring. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of a high-precision positioning method for a personal locator provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of another high-precision positioning method for a personal locator provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a high-precision positioning system for a personal locator provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0019] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0020] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0021] Personal locator: This usually refers to a portable device that has a variety of built-in sensors and communication modules, designed to determine the wearer's geographical location in real time or near real time.

[0022] Inertial Measurement Unit (IMU): One of the core components of a personal locator, it typically integrates a magnetometer, gyroscope, accelerometer, and barometer.

[0023] Magnetometer: Used to measure the strength and direction of the magnetic field in the surrounding environment, which can help determine the heading of the equipment.

[0024] Gyroscope: Used to measure the angular velocity of a device; by integrating the angular velocity, the attitude change of the device can be calculated.

[0025] Accelerometer: Used to measure the linear acceleration of equipment, which can be used to calculate the displacement and velocity of the equipment.

[0026] Barometer: Used to measure ambient air pressure and can be used to estimate the vertical height change of equipment.

[0027] Wireless signal data: This typically includes Wi-Fi signals, cellular base station signals, Bluetooth signals, etc. The strength, quality, and source information of these signals can be used to assist in positioning.

[0028] User behavior pattern: refers to the current movement state of a user wearing a personal locator, such as walking normally, avoiding or weaving, taking an elevator, etc.

[0029] Environment category: refers to the characteristics of the external environment in which the personal locator is currently located, such as a sparse signal environment or an electromagnetic interference environment.

[0030] Location processing method: refers to the algorithm and parameter configuration used to calculate location information, including the weights of different sensor data and wireless signal data in the fusion algorithm, the parameters of the error model, etc.

[0031] Location information: refers to the geographical coordinates of the wearer calculated by the personal locator.

[0032] Credibility: refers to the assessment of the accuracy and reliability of the generated location information, usually expressed as probability or error range.

[0033] The implementation environment of this application is usually a complex urban environment or indoor scene. In these scenarios, the GPS signal may be limited and there are complex factors such as multipath effect, signal blockage, and electromagnetic interference.

[0034] The following specific embodiments will provide a detailed description and explanation of a high-precision positioning method for a personal locator provided in this application.

[0035] Reference Figure 1 This invention provides a high-precision positioning method for a personal locator, comprising the following steps: S1, acquire inertial measurement unit data and wireless signal data from the personal locator.

[0036] The personal locator is equipped with a magnetometer, gyroscope, accelerometer, and barometer.

[0037] Inertial measurement unit (IMU) data can be obtained by reading the outputs of built-in magnetometer, gyroscope, accelerometer, and barometer sensors. For example, the accelerometer can provide triaxial acceleration data, the gyroscope can provide triaxial angular velocity data, the magnetometer can provide triaxial magnetic field strength data, and the barometer can provide air pressure values.

[0038] Wireless signal data can be obtained by scanning for nearby Wi-Fi access points, cellular base stations, or Bluetooth beacons using the wireless communication module built into a personal locator. For example, it can acquire Wi-Fi signal strength indicators, service set identifiers, and media access control addresses, as well as cellular base station signal strength and base station identifiers. This data acquisition can be periodic, such as multiple times per second, to ensure real-time data availability and continuity.

[0039] S2. Based on the inertial measurement unit data and wireless signal data, determine the current user behavior pattern and the type of environment.

[0040] User behavior patterns can be determined based on inertial measurement unit (IMU) data analysis. For example, gait characteristics can be identified by analyzing accelerometer data, and turning behavior can be identified by analyzing gyroscope data. Environmental categories can be determined by combining wireless signal data. For example, the signal coverage and reliability of the current environment can be assessed by analyzing Wi-Fi signal strength fluctuations, signal-to-noise ratio, number of visible signal sources, and media access control address matching rate. Furthermore, the stability of magnetometer readings and the deviation between the magnetometer heading and the gyroscope integrated heading can be used to determine the presence of electromagnetic interference.

[0041] S3. Adjust the location processing method according to user behavior patterns and environment categories.

[0042] The adjustments include changing the role of different wireless signal data in positioning and modifying the error description parameters of the location estimation program.

[0043] Adjustments can include changing the role of different wireless signal data in positioning and modifying the error description parameters of the position estimation procedure. For example, under certain user behavior patterns, it may be necessary to reduce the weight of certain sensor data in heading or displacement estimation, or increase sensitivity to instantaneous zero-velocity updates. In certain environmental categories, it may be necessary to increase the measurement noise covariance of auxiliary positioning signals in the filtering procedure to reduce their corrective effect on position estimation, or reduce the weight of magnetometer data in heading estimation. This dynamic adjustment ensures that the positioning algorithm can better adapt to the current situation and avoids over-reliance on unreliable data sources in unsuitable scenarios.

[0044] S4. Generate location information and corresponding credibility based on the adjusted positioning processing method.

[0045] The third periodicity characteristic includes the time frequency of product defects.

[0046] Position information can be calculated by fusing inertial measurement unit (IMU) data and wireless signal data, combined with adjusted parameters, using Kalman filtering, particle filtering, or other state estimation algorithms. Reliability generation requires evaluating various error characteristics that contribute to positioning errors, such as inertial estimation drift, wireless signal interference, magnetic field interference, and vertical displacement error. Based on these error characteristics, an error contribution factor matrix can be determined, where each element represents the contribution of a specific error source to the uncertainty of a specific state variable (such as position, velocity, or attitude). Finally, the overall reliability of the generated position information can be determined based on the error contribution factor matrix.

[0047] The high-precision positioning method for personal locators proposed in this application can significantly improve the positioning accuracy and reliability of personal locators in complex environments by real-time perception and adaptive adjustment of positioning processing methods based on user behavior patterns and environmental categories.

[0048] Specifically, the core innovation of this application lies in its context-aware and adaptive adjustment mechanism. Traditional positioning methods typically employ fixed data fusion strategies and error models, which can lead to a sharp decline in positioning performance when environmental and user behavior patterns change drastically. For example, when a user transitions from normal walking to avoidance or weaving, the nonlinearity of their movement pattern increases. If a fixed model designed for normal walking is still used, inertial estimation errors will accumulate rapidly. Similarly, when the locator enters an environment with sparse signals or electromagnetic interference, continuing to assign excessive weight to wireless signals or magnetometer data will introduce even greater errors.

[0049] This application introduces a step of "determining the current user behavior pattern and the type of environment," enabling the positioning system to perceive the external context in real time. For example, when the system determines that the user is in "avoidance or shuttle" mode, it will correspondingly "reduce the weight of accelerometer and gyroscope data in heading and displacement estimation, increase the sensitivity to instantaneous zero velocity or zero angular velocity updates, and expand the covariance of attitude estimation in the filtering procedure," thereby better adapting to such irregular movements and suppressing the rapid accumulation of inertial estimation errors. When the system determines that the environment is a "signal-sparse environment," it will "increase the measurement noise covariance of auxiliary positioning signals in the filtering procedure, reducing its correction effect on position estimation," avoiding incorrect corrections introduced by unreliable wireless signal data.

[0050] Compared to the closest existing technologies, the advantages of this application lie in its dynamism and robustness. Existing technologies often perform well in specific scenarios, but their performance is severely limited in the varied and complex urban environment. This application, through refined identification of user behavior patterns and environmental categories, and accordingly meticulous adjustments to the positioning processing method, enables the positioning system to flexibly adjust its strategy based on "road conditions" and "driver status," much like an experienced navigator. Therefore, this application effectively solves the problems of accumulated positioning errors and misleading high confidence levels caused by single or fixed processing modes in traditional methods, significantly improving the positioning accuracy and reliability of personal locators in complex environments. Especially in challenging scenarios such as limited GPS signals, unreliable wireless signals, and severe electromagnetic interference, it can provide more accurate and reliable location information, thereby avoiding serious consequences caused by incorrect positioning in emergency situations.

[0051] like Figure 2 As shown, determining the current user behavior pattern includes the following steps: S101. Based on the data from the inertial measurement unit, determine the user's step frequency, step length change rate, turning frequency, vertical displacement change rate, horizontal acceleration, and angular velocity.

[0052] Inertial measurement unit (IMU) data refers to raw or pre-processed data collected by the magnetometer, gyroscope, accelerometer, and barometer built into the personal locator (PLO). This data reflects the PLO's motion and attitude changes in space.

[0053] Specifically, a user's step frequency can be obtained by analyzing periodic peaks in accelerometer data, representing the number of steps the user takes per unit time. The step length change rate reflects the fluctuation in the length of each step and can be calculated by analyzing the displacement obtained from integrating the accelerometer readings. Turning frequency refers to the number of times the user changes direction per unit time, typically detected using gyroscope angular velocity data. The vertical displacement change rate refers to the rate of change of displacement perpendicular to the ground, which can be estimated using barometer data or integrated accelerometer data in the vertical direction. Horizontal acceleration and angular velocity are directly obtained from the readings of the accelerometer and gyroscope on the horizontal plane.

[0054] S102. When the user's cadence is within the preset cadence range, the step length change rate is less than the step length change rate threshold, and the turning frequency is less than the frequency threshold, the user's behavior mode is determined to be normal walking.

[0055] As a preferred implementation, a series of preset parameters need to be set to accurately identify different user behavior patterns. For example, the preset step frequency range can be determined based on statistical analysis of a large amount of user walking data, and is usually set to the frequency range of normal human walking. The preset thresholds for step length change rate, frequency, vertical displacement change rate, duration, and horizontal acceleration and angular velocity are all parameters calibrated based on empirical data or experimental results, used to distinguish the movement characteristics under different behavior patterns.

[0056] S103. When the step size change rate is detected to be greater than the step size change rate threshold within a preset time, the user behavior pattern is determined to be avoidance or shuttle.

[0057] S104. When the duration of the vertical displacement rate of change being greater than the vertical displacement rate of change threshold is greater than the duration threshold, and the horizontal acceleration and angular velocity are both less than the preset threshold, the user behavior mode is determined to be riding the elevator.

[0058] Among them, the rate of change of vertical displacement is the rate of change of displacement in the direction perpendicular to the ground.

[0059] This application's solution involves in-depth analysis of inertial measurement unit (IMU) data acquired by a personal locator to extract various kinematic parameters closely related to the user's motion state, including cadence, stride length change rate, turning frequency, vertical displacement change rate, and horizontal acceleration and angular velocity. These parameters comprehensively and meticulously characterize the user's motion features. By setting reasonable thresholds and judgment conditions, these kinematic parameters are matched with preset user behavior patterns, thereby achieving accurate identification of the current user behavior pattern. For example, the identification of a normal walking pattern relies on the regularity of cadence, the smoothness of stride length changes, and the low frequency of turning; avoidance or shuttle patterns are captured by the instantaneous increase in the stride length change rate to detect their irregularity; while the identification of an elevator riding pattern is mainly based on significant changes in vertical displacement and relatively smooth horizontal movement. This comprehensive judgment mechanism based on multi-dimensional inertial data enables the system to effectively distinguish the user's motion state in different scenarios.

[0060] The above technical solution enables refined identification of user behavior patterns. Compared to methods relying solely on a single sensor or simple threshold judgments, this solution significantly improves the accuracy and robustness of user behavior pattern judgment by comprehensively analyzing multiple inertial measurement unit (IMU) data, including user gait frequency, step length change rate, turning frequency, vertical displacement change rate, horizontal acceleration, and angular velocity, combined with multiple judgment conditions. This provides more precise input for subsequent positioning processing adjustments, ensuring that the positioning system employs the most suitable algorithm and parameters under different user behavior patterns, thereby effectively reducing positioning errors and improving the overall high-precision positioning performance of the personal locator.

[0061] The above-mentioned environmental categories include: S201. Based on the wireless signal data, determine the RSSI fluctuation amplitude, signal-to-noise ratio, number of visible signal sources, and MAC address of the wireless signal.

[0062] The RSSI fluctuation amplitude of a wireless signal refers to the range or dispersion of the Received Signal Strength Indication (RSSI) within a specific time window. In environments with poor signal coverage or obstructions, RSSI readings are often unstable and fluctuate significantly. The signal-to-noise ratio (SNR) is the ratio of effective signal power to noise power in a wireless signal; a lower SNR generally indicates poorer signal quality and potential interference. The number of visible signal sources refers to the number of wireless signal transmitters (such as Wi-Fi access points and Bluetooth beacons) that a personal locator can detect during a scanning period. A low number usually means the device is in an area with sparse signal coverage. A MAC address is a unique hardware identifier for a network device. By matching it with known MAC addresses with geographic location information in a location server database, the reliability and density of signal sources in the current environment can be assessed.

[0063] S202. If the RSSI fluctuation amplitude is greater than the preset fluctuation amplitude, or the signal-to-noise ratio is lower than the preset signal-to-noise ratio, or the number of visible signal sources is less than the preset number and the MAC address matching rate in the positioning server database is lower than the preset matching rate threshold, the environment category is determined to be a signal sparse environment.

[0064] S203. Based on the wireless signal data, determine the standard deviation of the magnetometer reading stability and the deviation between the magnetometer heading and the gyroscope integral heading.

[0065] S204. If the standard deviation of the magnetometer reading stability is greater than the standard deviation threshold, or the deviation between the magnetometer heading and the gyroscope integral heading is greater than the deviation threshold, the environment is determined to be an electromagnetic interference environment.

[0066] Furthermore, the standard deviation of magnetometer reading stability refers to the statistical dispersion of the geomagnetic field strength readings measured by the magnetometer over a period of time. In environments with external electromagnetic interference, the magnetometer readings become unstable, leading to an increase in its standard deviation. The deviation between the magnetometer heading and the gyroscope integrated heading refers to the angular difference between the device heading calculated from the magnetometer and the heading obtained by integrating gyroscope data. Ideally, the two should be highly consistent, but in electromagnetic interference environments, the magnetometer's heading estimate is significantly affected, resulting in an increased deviation from the gyroscope integrated heading. Preset fluctuation amplitude, preset signal-to-noise ratio, preset quantity, preset matching rate threshold, standard deviation threshold, and deviation threshold are all parameters that can be set according to actual application scenarios and experience, used to define the critical conditions for different environmental categories.

[0067] This application's solution, through comprehensive analysis of wireless signal data and magnetometer data, can accurately identify the environmental category of a personal locator. Specifically, by monitoring the RSSI fluctuation amplitude, signal-to-noise ratio, number of visible signal sources, and MAC address matching rate of the wireless signal, the quality and density of signal coverage can be effectively determined. When these indicators show poor signal quality or sparse signal sources, it can be inferred that the current environment is a sparse signal environment. Furthermore, by analyzing the stability standard deviation of the magnetometer readings and the deviation between the magnetometer heading and the gyroscope's integrated heading, the presence of external electromagnetic interference can be identified. Instability in the magnetometer readings or significant deviation from the gyroscope's integrated heading are strong evidence of electromagnetic interference. Therefore, this application can provide accurate environmental context input for subsequent adjustments to the positioning processing method, ensuring that the positioning algorithm can be optimized according to actual environmental conditions.

[0068] Through the above technical solutions, personal locators can perceive the characteristics of their environment more precisely, distinguishing between environments with sparse signals and environments with electromagnetic interference. This accurate judgment of environmental categories allows for more targeted adjustments to subsequent positioning processing methods. For example, the weight of wireless signals can be reduced in environments with sparse signals, and the weight of magnetometers can be reduced in environments with electromagnetic interference. This effectively avoids a decrease in positioning accuracy due to environmental factors and significantly improves the robustness and accuracy of personal locators in complex and changing environments.

[0069] In some embodiments described above, this application proposes adjusting the positioning processing method based on user behavior patterns and environmental categories to change the role of different wireless signal data in positioning and modify the error description parameters of the location estimation program. However, in practical applications, user behavior patterns and environmental categories are diverse. If a refined adjustment strategy is not provided for each specific scenario, the positioning processing method may lack adaptability, thereby affecting positioning accuracy and robustness.

[0070] In response, this application further proposes the aforementioned method of adjusting location processing based on user behavior patterns and environmental categories, including: S301. When the user's behavior pattern is normal walking, the location processing method is not adjusted.

[0071] S302. When the user's behavior pattern is avoidance or shuttle, reduce the weight of accelerometer and gyroscope data in heading and displacement estimation, increase the sensitivity to instantaneous zero velocity update or zero angular velocity update, and expand the covariance of attitude estimation in the filtering procedure.

[0072] When user behavior patterns involve avoidance or weaving, it typically indicates the user is in a complex environment with dense crowds or numerous obstacles, resulting in frequent changes in trajectory and significant fluctuations in acceleration and angular velocity. In such cases, to avoid excessive influence of noise and sudden, violent movements from inertial sensor data on heading and displacement estimation, the weight of accelerometer and gyroscope data in these estimations can be reduced. Simultaneously, to capture the user's precise state during brief pauses or turns, sensitivity to instantaneous zero-velocity or zero-angular-velocity updates can be increased for more accurate zero-velocity or zero-angular-velocity detection and correction. Furthermore, increasing the covariance of attitude estimation in the filtering procedure allows for greater uncertainty in attitude estimation over short periods, thus better adapting to rapidly changing motion attitudes.

[0073] S303. When the user's behavior pattern is taking an elevator, increase the weight of the barometer in vertical positioning and reduce the drift accumulation penalty in horizontal inertial calculation.

[0074] In practical applications, when users are riding elevators, vertical displacement changes significantly, while horizontal inertial estimation is easily affected by elevator start-up, stopping, or swaying. Therefore, the weight of the barometer in vertical positioning can be increased, utilizing its high-precision vertical height measurement capability to correct the vertical position. Simultaneously, reducing the drift accumulation penalty in horizontal inertial estimation can minimize horizontal position drift caused by minor horizontal swaying within the elevator or accumulated sensor noise.

[0075] S304. When the environment is a signal-sparse environment, increase the measurement noise covariance of the auxiliary positioning signal in the filtering program to reduce its correction effect on the position estimation.

[0076] Furthermore, when the environment is characterized by sparse signal coverage, such as in basements, tunnels, or areas with severe signal obstruction, the quality and quantity of wireless signals are both unsatisfactory. In such cases, assigning excessively high confidence to auxiliary positioning signals (such as Wi-Fi and Bluetooth) may introduce significant errors. Therefore, increasing the measurement noise covariance of the auxiliary positioning signals in the filtering process is equivalent to expressing a lower confidence level for these signals in the mathematical model, thereby reducing their corrective effect on location estimation and avoiding the introduction of erroneous corrections.

[0077] S305. When the environment is classified as an electromagnetic interference environment, reduce the weight of magnetometer data in heading estimation and rely on the integration of gyroscope to maintain short-term heading.

[0078] When the environment is classified as an electromagnetic interference (EMI) environment, such as near motors, transformers, or strong magnetic field equipment, magnetometer readings can be severely affected, leading to inaccurate heading estimates. In such cases, to avoid introducing errors from magnetometer data, the weight of magnetometer data in heading estimation can be reduced. Instead, gyroscope integration can be relied upon to maintain short-term heading, as gyroscopes have high relative accuracy over short periods. Although drift exists, their short-term stability is superior to that of an interfered magnetometer in EMI environments.

[0079] This application's solution addresses the shortcomings of traditional positioning methods in adapting to complex and changing scenarios by dynamically adjusting key parameters and data weights in the positioning processing method based on different user behavior patterns and environmental categories. Specifically, in normal walking mode, the system maintains standard processing to ensure efficiency; in dynamic movement modes such as obstacle avoidance or weaving, it effectively suppresses noise and error accumulation caused by instantaneous motion by reducing the weight of inertial data and enhancing the sensitivity of zero-velocity or zero-angular-velocity updates, while simultaneously expanding the attitude covariance to adapt to rapid attitude changes. When riding an elevator, the vertical positioning weight of the barometer is increased, combined with a reduction in the horizontal drift penalty, ensuring the accuracy of vertical positioning and reducing horizontal misjudgments. In signal-sparse environments, the system reduces the negative impact of unreliable signals on position estimation by increasing the measurement noise covariance of auxiliary positioning signals. In electromagnetic interference environments, the magnetometer data weight is reduced, and gyroscope integration is used instead to maintain short-term heading, effectively avoiding heading errors caused by magnetic field interference. It is precisely this refined adaptive adjustment mechanism that enables the positioning system to optimize its performance according to real-time scenarios.

[0080] Through the above technical solution, this application can significantly improve the performance of personal locators in high-precision positioning. By finely identifying different user behavior patterns and environmental categories, and dynamically adjusting the positioning processing method accordingly, it effectively solves the problem of accuracy degradation in complex and variable scenarios under a single fixed positioning strategy. Specifically, in challenging scenarios such as users engaging in vigorous movement, riding elevators, or being in environments with sparse signals or electromagnetic interference, this solution can adaptively optimize the use strategy and filtering parameters of sensor data, thereby effectively suppressing the influence of various error sources, such as inertial estimation drift, wireless signal interference, and magnetic field interference. Therefore, this application achieves high-precision position estimation under various complex situations, improves the robustness and reliability of the positioning system, and provides users with more stable and accurate positioning services.

[0081] In some embodiments described above in this application, the high-precision positioning method for personal locators relies on inertial measurement unit (IMU) data. However, in practical applications, the gyroscope in the IMU may be affected by environmental factors or its own hardware characteristics, resulting in systematic bias. If the systematic bias problem of the gyroscope is not resolved, its output angular velocity data will be inaccurate, leading to accumulated errors in subsequent attitude estimation, heading calculation, and position calculation, severely affecting the accuracy and reliability of positioning.

[0082] In response, this application further proposes an optimization scheme for the aforementioned high-precision positioning method for personal locators, which further includes: S401. Obtain the gyroscope measurement data from the initial inertial measurement unit data.

[0083] The initial inertial measurement unit (IMU) data consists of raw IMU data that has not undergone any processing.

[0084] Among them, the data measured by the gyroscope is the core information used to reflect changes in the angular velocity of the equipment.

[0085] S402. Determine whether there are electromagnetic field characteristics at a specific frequency.

[0086] Specifically, it can identify the energy intensity within a specific frequency range; if the duration of the energy intensity within the specific frequency range being greater than a preset energy intensity threshold exceeds a preset duration, it is determined that electromagnetic field characteristics exist; if the energy intensity within the specific frequency range is less than or equal to the preset energy intensity threshold, or if the duration of the energy intensity within the specific frequency range being greater than the preset energy intensity threshold does not exceed a preset duration, it is determined that electromagnetic field characteristics do not exist.

[0087] S403. Determine whether there is an angular deviation between the gyroscope integrated attitude and the accelerometer calculated attitude.

[0088] Furthermore, determining the angular deviation between the gyroscope-integrated attitude and the accelerometer-calculated attitude typically involves an attitude fusion algorithm. The gyroscope continuously updates its attitude by integrating the angular velocity, while the accelerometer provides an attitude reference using the gravity vector when the device is stationary or moving at a constant speed. When the gyroscope exhibits a systematic bias, its integrated attitude will gradually deviate from the attitude calculated by the accelerometer. Therefore, continuously monitoring the angular difference between these two attitude estimates can serve as a basis for determining whether the gyroscope is biased.

[0089] S404. When it is determined that there are electromagnetic field characteristics at a specific frequency and there is an angular deviation, it is inferred that the gyroscope has a systematic bias.

[0090] S405. Estimate the bias of the gyroscope.

[0091] This estimation process can employ various adaptive filtering algorithms, such as Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF), by estimating the gyroscope's bias as a state variable. In some cases, the gyroscope's non-zero output can also be directly measured as the bias when the device is stationary.

[0092] In one embodiment, the bias of the gyroscope satisfies the following relationship: b_g_new=b_g_old+K_bias (delta_heading_error-b_g_old); Where b_g_new is the current bias of the gyroscope, b_g_old is the initial bias of the gyroscope, K_bias is the preset adaptive adjustment coefficient, and delta_heading_error is the instantaneous deviation between the gyroscope's integrated heading and the reference heading.

[0093] Specifically, `b_g_new` refers to the gyroscope bias determined by the system at the current moment or after one update iteration. It represents the latest estimate of the gyroscope's zero-bias error. `b_g_old` can be understood as the estimated gyroscope bias held by the system at the beginning of the current update cycle or before this adjustment. `K_bias` is a preset adaptive adjustment coefficient, which controls the step size and response speed of the bias update. It is usually a value between 0 and 1, used to balance the weight between new measurement information and old estimates to ensure the stability and convergence of the bias estimation. `delta_heading_error` refers to the instantaneous angular difference between the heading obtained by integrating gyroscope data and the reference heading calculated by other independent sensors (such as accelerometers or magnetometers, or by fusing data from multiple sensors). This deviation reflects the heading error accumulated by the gyroscope over a short period of time, part of which is caused by the gyroscope bias.

[0094] By introducing the above formula, adaptive estimation and updating of the gyroscope bias is achieved. The core idea of ​​this formula is to use the instantaneous deviation delta_heading_error between the gyroscope's integrated heading and the reference heading to correct the current bias estimate b_g_old. When there is a difference between delta_heading_error and b_g_old, it indicates that the current bias estimate may not be accurate enough and needs adjustment. By multiplying this difference by the adaptive adjustment coefficient K_bias, an adjustment amount can be calculated and added to b_g_old to obtain a new bias estimate b_g_new. This error feedback-based mechanism allows the gyroscope bias to be dynamically adjusted over time and according to environmental changes, effectively compensating for the gyroscope's systematic bias, avoiding the accumulation of bias errors, and thus improving the accuracy of heading estimation.

[0095] In some preferred embodiments, a specific example is given below. Assume that at the initial startup of the personal locator, the initial bias of the gyroscope, b_g_old, is set to a preset value, for example, 0.01 rad / s. During positioning, the system continuously acquires gyroscope data and integrates it to calculate the heading, while combining accelerometer or magnetometer data (or fusing data from multiple sensors) to obtain a reference heading. At a certain moment, the instantaneous deviation delta_heading_error between the gyroscope integrated heading and the reference heading is detected to be 0.02 rad / s. At this time, if the preset adaptive adjustment coefficient K_bias is 0.1, then according to the formula b_g_new = b_g_old + K_bias... (delta_heading_error-b_g_old), the new gyroscope bias b_g_new will be calculated as: b_g_new=0.01+0.1 (0.02-0.01)=0.01+0.1 0.01 = 0.011 rad / s.

[0096] In the next update cycle, this new bias of 0.011 rad / s will be used as b_g_old in the next bias estimation. Through this iterative process, the gyroscope bias will gradually converge to its true value. Even when the gyroscope bias changes slowly over time, this mechanism can continuously track and correct the bias, thus ensuring the long-term accuracy of heading estimation.

[0097] S406. Subtract the bias from the gyroscope measurement data in the initial inertial measurement unit data to obtain the inertial measurement unit data.

[0098] This application's solution effectively addresses potential accuracy issues with raw inertial measurement unit (IMU) data by introducing a detection and correction mechanism for systematic bias in the gyroscope. Specifically, by simultaneously detecting electromagnetic field characteristics at specific frequencies and the angular deviation between the gyroscope's integrated attitude and the accelerometer-calculated attitude, this application can more accurately distinguish between instantaneous noise, random drift, and persistent systematic bias in the gyroscope. Detection of electromagnetic field characteristics helps identify the impact of external interference sources on gyroscope readings, while attitude deviation detection verifies the reliability of gyroscope data from an internal consistency perspective. When both occur simultaneously, the system can infer the existence of systematic bias in the gyroscope with high confidence, avoiding misjudgments that might result from a single criterion. Subsequently, by accurately estimating this bias and subtracting it from the raw data, the IMU data input into subsequent positioning algorithms is ensured to have higher purity and accuracy. This provides more reliable foundational data for subsequent user behavior pattern judgment, environmental category identification, and adjustments to positioning processing methods, fundamentally improving the performance of the entire high-precision positioning method.

[0099] Through the above technical solution, this application can effectively identify and eliminate the systematic bias of the gyroscope in a personal locator, significantly improving the quality of inertial measurement unit (IMU) data. Compared to directly using uncorrected raw IMU data for positioning, this solution fundamentally reduces the accumulation of attitude and heading estimation errors caused by gyroscope bias, thereby greatly improving the long-term stability and accuracy of positioning. Especially in complex environments where satellite signals are blocked or wireless signals are sparse, the positioning system's dependence on IMU data increases. The implementation of this solution can effectively suppress inertial calculation drift and ensure the reliability of positioning results. Furthermore, by comprehensively judging electromagnetic field characteristics and attitude deviations, this solution improves the robustness of gyroscope bias detection, avoids miscorrection, and further enhances the adaptability and accuracy of the positioning system.

[0100] In some preferred embodiments, a specific example is given below. Suppose a personal locator is worn by a user, and upon entering an industrial plant containing large electrical equipment, the equipment generates electromagnetic interference at a specific frequency.

[0101] First, the personal locator continuously acquires raw measurement data from its built-in gyroscope. Simultaneously, the system performs spectral analysis on the magnetometer data, detecting a persistent energy peak near 50Hz with an intensity exceeding a preset threshold, thus determining the presence of electromagnetic field characteristics at a specific frequency.

[0102] Meanwhile, the attitude fusion module inside the locator continuously compares the attitude obtained from gyroscope integration with the attitude calculated by the accelerometer. Due to electromagnetic interference causing a bias in the gyroscope data, the system detects a continuous and increasing angular deviation between the gyroscope-integrated attitude and the accelerometer-calculated attitude.

[0103] When both conditions—namely, the existence of electromagnetic field characteristics at a specific frequency and a continuous angular deviation—are simultaneously met, the system infers that the gyroscope has a systematic bias caused by external electromagnetic interference.

[0104] Subsequently, the system initiates an adaptive bias estimation algorithm, such as a Kalman filter-based bias estimator, to estimate the gyroscope's bias in the three axes in real time. For example, it estimates that the gyroscope has a bias of +0.05 rad / s on the X-axis, -0.03 rad / s on the Y-axis, and +0.01 rad / s on the Z-axis.

[0105] Finally, these estimated biases are subtracted from the raw gyroscope measurement data in real time. For example, if the raw X-axis angular velocity reading is 0.1 rad / s, after bias subtraction it becomes 0.1 - 0.05 = 0.05 rad / s. The bias-corrected gyroscope data, together with data from other inertial sensors, forms more accurate inertial measurement unit (IMU) data, which is then fed into the subsequent positioning processing flow, ensuring high-precision positioning results even in environments with strong electromagnetic interference.

[0106] The above technical solution enables accurate and robust determination of the presence of specific frequency electromagnetic field characteristics in the environment where a personal locator is located. This determination method avoids false alarms that may arise from judging solely based on instantaneous signal strength, improving the accuracy and reliability of electromagnetic interference identification. Consequently, it can more accurately trigger or prevent the inference and estimation process of the gyroscope's systematic bias, thus ensuring the quality of inertial measurement unit data. This provides more reliable input for subsequent user behavior pattern judgment, environmental category judgment, and adjustment of positioning processing methods, ultimately improving the overall performance and positioning accuracy of the high-precision positioning method for personal locators.

[0107] Specifically, in the aforementioned high-precision positioning method for personal locators, in order to provide a corresponding credibility assessment for the generated location information, this application proposes a method for determining credibility.

[0108] Determining credibility includes: S501. Extract various error features that cause positioning errors.

[0109] The error characteristics include inertial calculation drift characteristics, wireless signal interference characteristics, magnetic field interference characteristics, and vertical displacement error characteristics.

[0110] Among these, inertial estimation drift characteristics can be understood as the cumulative error characteristics caused by factors such as sensor noise and integration errors when estimating position based on inertial measurement unit data. Wireless signal interference characteristics refer to the characteristics of wireless signals (such as Wi-Fi, Bluetooth, cellular signals, etc.) being affected by multipath effects, obstruction, and attenuation during propagation, leading to fluctuations in parameters such as signal strength and signal-to-noise ratio, thus affecting positioning accuracy. Magnetic field interference characteristics refer to the characteristics of the ambient magnetic field being affected by local magnetic objects or electromagnetic devices, causing abnormal magnetometer readings, thus affecting heading estimation and positioning accuracy. Vertical displacement error characteristics refer to the displacement error characteristics in the vertical direction caused by factors such as fluctuations in barometer readings, inaccurate floor identification, or the accumulation of errors in the vertical component of inertial estimation.

[0111] S502. Determine the error contribution factor matrix based on various error characteristics.

[0112] In the error contribution factor matrix, each element C_ij represents the contribution of the i-th error source to the uncertainty of the j-th state variable; the state variables include position, velocity, and attitude.

[0113] The error contribution factor matrix is ​​a mathematical tool that quantifies the impact of different error sources on the uncertainty of different state variables. Here, "position" typically refers to the user's coordinates in three-dimensional space, such as X, Y, and Z coordinates; "velocity" refers to the user's velocity components in three-dimensional space; and "attitude" refers to the orientation of the user's device, such as pitch, roll, and yaw angles. The value of each element C_ij in this matrix reflects the degree of contribution of the i-th error source to the uncertainty (i.e., error) of the j-th state variable. For example, C_11 might represent the contribution of inertial estimation drift characteristics to position uncertainty.

[0114] S503. Determine the credibility based on the error contribution factor matrix.

[0115] Determining credibility can be understood as a quantitative assessment of the accuracy of the generated location information. This determination process can be based on the analysis of an error contribution factor matrix, such as through weighted summation of the elements in the matrix, statistical analysis, or machine learning model processing, to comprehensively evaluate the overall uncertainty level of the current positioning results. Generally, the lower the uncertainty, the higher the credibility.

[0116] This application's solution systematically identifies and quantifies various error sources affecting positioning accuracy, constructing an error contribution factor matrix. This matrix clearly reveals the specific impact of different error sources (such as inertial calculation drift, wireless signal interference, magnetic field interference, and vertical displacement error) on the uncertainties of key state variables (such as position, velocity, and attitude). Therefore, based on these quantified contribution factors, the overall reliability of the current positioning results can be comprehensively evaluated, thereby generating an objective and instructive credibility index. This method transforms the quality assessment of positioning results from a simple empirical judgment into a scientific quantification based on multi-dimensional error analysis.

[0117] The above technical solution enables a refined reliability assessment of location information generated by personal locators. This assessment process not only considers multiple potential sources of error but also quantifies the specific impact of these error sources on different location state variables, thus providing a more comprehensive and accurate location reliability index. This helps users or subsequent systems better understand the quality of the current location results and take further corrections or decisions when necessary, significantly improving the practicality and reliability of the location results.

[0118] Specifically, in the above-mentioned high-precision positioning method for personal locators, the method for determining the drift characteristics by inertial estimation can be further refined into the following steps.

[0119] The method also includes: S601. Calculate the standard deviation of the accelerometer data within a preset time window, and the rate of change of the gyroscope angular velocity within a preset time window.

[0120] Specifically, accelerometer data reflects the linear motion state of the personal locator. By calculating its standard deviation within a preset time window, the fluctuation of the accelerometer readings can be quantified, thus reflecting the smoothness or abruptness of the device's motion. Simultaneously, the gyroscope measures the device's angular velocity, and its rate of change within a preset time window indicates the stability of the device's rotational motion. The preset time window is used for sampling and statistical analysis of sensor data; its length can be adjusted according to the actual application scenario and the required response speed, for example, it can be set to several seconds to tens of seconds. The standard deviation threshold and rate of change threshold are preset parameters used to determine whether the device's motion state meets the conditions requiring drift estimation.

[0121] S602. When the standard deviation exceeds the standard deviation threshold and the rate of change is greater than the rate of change threshold, integrate the accelerometer data to estimate the drift velocity without external correction.

[0122] When both the standard deviation of the accelerometer data and the rate of change of the gyroscope angular velocity exceed their respective thresholds, it indicates that the device may be in an unstable state of motion, making inertial estimation more prone to cumulative errors. Based on this, by integrating the accelerometer data, the rate of position change estimated by the device using only inertial sensor data can be calculated without external reference (such as wireless signal) correction. This rate includes the inherent error accumulation of the inertial sensor, i.e., drift.

[0123] S603. Determine the inertial drift characteristics based on the square of the drift velocity.

[0124] This application's solution analyzes accelerometer and gyroscope angular velocity data from a personal locator to dynamically identify potential drift caused by inertial estimation under specific motion conditions. Specifically, when the standard deviation of the accelerometer data and the rate of change of the gyroscope angular velocity both exceed preset thresholds, it indicates that the device may be experiencing violent motion or rapid attitude changes, such as when the user is moving quickly, avoiding obstacles, or weaving. In this situation, the cumulative effect of errors in the inertial measurement unit becomes more significant, leading to a decrease in the accuracy of position estimation. By integrating the accelerometer data at this time, the drift velocity under pure inertial estimation can be simulated, thereby quantifying the degree of inertial estimation drift. Squaring this drift velocity yields a more representative inertial estimation drift characteristic, which is used to construct the subsequent error contribution factor matrix.

[0125] The above technical solution enables a dynamic and quantitative assessment of the inherent drift error of inertial estimation in personal locators under different motion states. This allows for a more accurate reflection of the contribution of inertial estimation to overall positioning uncertainty when determining reliability, thereby improving the precision and accuracy of reliability assessment. Compared to simply treating inertial estimation drift as a fixed value, this solution adaptively adjusts the drift characteristics according to the actual motion conditions, making the generated position information and its reliability more consistent with reality, thus improving the robustness and reliability of the high-precision positioning method for personal locators.

[0126] like Figure 3 As shown in the figure, this embodiment of the invention also provides a high-precision positioning system for a personal locator. The system includes: The data acquisition module is used to acquire inertial measurement unit data and wireless signal data from the personal locator; the personal locator has a built-in magnetometer, gyroscope, accelerometer, and barometer; The context judgment module is used to determine the current user behavior pattern and the type of environment based on inertial measurement unit data and wireless signal data. The processing method adjustment module is used to adjust the positioning processing method according to user behavior patterns and environmental categories; the adjustment includes changing the role of different wireless signal data in positioning, and modifying the error description parameters of the location estimation program; The information generation and evaluation module is used to generate location information and corresponding credibility based on the adjusted positioning processing method.

[0127] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0128] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0129] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0130] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A high-precision positioning method for a personal locator, characterized in that, include: The system acquires inertial measurement unit data and wireless signal data from a personal locator; the personal locator has a built-in magnetometer, gyroscope, accelerometer, and barometer. Based on the inertial measurement unit data and the wireless signal data, determine the current user behavior pattern and the type of environment. The positioning processing method is adjusted according to the user behavior pattern and the environment category; the adjustment includes changing the role of different wireless signal data in positioning and modifying the error description parameters of the location estimation program. Based on the adjusted positioning processing method, location information and corresponding credibility are generated.

2. The high-precision positioning method for a personal locator according to claim 1, characterized in that, Determine the current user behavior pattern, including: Based on the data from the inertial measurement unit, the user's step frequency, step length change rate, turning frequency, vertical displacement change rate, horizontal acceleration, and angular velocity are determined. When the user's cadence is within a preset cadence range, the step length change rate is less than the step length change rate threshold, and the turning frequency is less than the frequency threshold, the user's behavior pattern is determined to be normal walking. When the step size change rate is detected to be greater than the step size change rate threshold within a preset time, the user behavior pattern is determined to be avoidance or weaving. When the duration of the vertical displacement rate of change being greater than the vertical displacement rate of change threshold is greater than the duration threshold, and the horizontal acceleration and angular velocity are both less than the preset threshold, the user behavior mode is determined to be riding an elevator; the vertical displacement rate of change is the rate of change of displacement in the direction perpendicular to the ground.

3. The high-precision positioning method for a personal locator according to claim 1, characterized in that, Determine the category of the environment, including: Based on the wireless signal data, determine the RSSI fluctuation amplitude, signal-to-noise ratio, number of visible signal sources, and MAC address of the wireless signal; If the RSSI fluctuation amplitude is greater than the preset fluctuation amplitude, or the signal-to-noise ratio is lower than the preset signal-to-noise ratio, or the number of visible signal sources is less than the preset number and the MAC address has a matching rate in the location server database that is lower than the preset matching rate threshold, the environment category is determined to be a signal sparse environment. Based on the wireless signal data, determine the standard deviation of magnetometer reading stability and the deviation between magnetometer heading and gyroscope integrated heading; If the standard deviation of the magnetometer reading stability is greater than the standard deviation threshold, or the deviation between the magnetometer heading and the gyroscope integrated heading is greater than the deviation threshold, the environment is determined to be an electromagnetic interference environment.

4. The high-precision positioning method for a personal locator according to claim 1, characterized in that, The step of adjusting the location processing method based on the user behavior pattern and the environment category includes: When the user's behavior pattern is normal walking, the location processing method will not be adjusted. When the user behavior pattern is avoidance or shuttle, reduce the weight of accelerometer and gyroscope data in heading and displacement estimation, increase the sensitivity to instantaneous zero velocity update or zero angular velocity update, and expand the covariance of attitude estimation in the filtering procedure. When the user's behavior pattern is taking an elevator, increase the weight of the barometer in vertical positioning and reduce the drift accumulation penalty in horizontal inertial estimation. When the environment is a signal-sparse environment, increase the measurement noise covariance of the auxiliary positioning signal in the filtering program to reduce its correction effect on the position estimation. When the environment is classified as an electromagnetic interference environment, the weight of magnetometer data in heading estimation is reduced, and the gyroscope integration is relied upon to maintain short-term heading.

5. A high-precision positioning method for a personal locator according to claim 1, characterized in that, The method further includes: Acquire the gyroscope measurement data from the initial inertial measurement unit (IMU) data; the initial IMU data is the raw IMU data without any processing. To determine whether electromagnetic field characteristics at a specific frequency exist; Determine if there is an angular deviation between the gyroscope integrated attitude and the accelerometer-calculated attitude; When it is determined that there are electromagnetic field characteristics at a specific frequency and the aforementioned angular deviation exists, it is inferred that the gyroscope has a systematic bias. Estimate the bias of the gyroscope; The bias is subtracted from the gyroscope data in the initial inertial measurement unit data to obtain the inertial measurement unit data.

6. A high-precision positioning method for a personal locator according to claim 5, characterized in that, The bias of the gyroscope satisfies the following relationship: b_g_new=b_g_old+K_bias (delta_heading_error-b_g_old); Where b_g_new is the current bias of the gyroscope, b_g_old is the initial bias of the gyroscope, K_bias is the preset adaptive adjustment coefficient, and delta_heading_error is the instantaneous deviation between the gyroscope's integrated heading and the reference heading.

7. A high-precision positioning method for a personal locator according to claim 5, characterized in that, Determining whether a specific frequency electromagnetic field characteristic exists includes: Identify energy intensity within a specific frequency range; If the duration for which the energy intensity within the specific frequency range is greater than a preset energy intensity threshold exceeds a preset duration, then it is determined that electromagnetic field characteristics exist. If the energy intensity within the specific frequency range is less than or equal to a preset energy intensity threshold, or if the duration for which the energy intensity within the specific frequency range is greater than the preset energy intensity threshold does not exceed a preset duration, then it is determined that there is no electromagnetic field characteristic.

8. A high-precision positioning method for a personal locator according to claim 1, characterized in that, Determining the credibility includes: Extract various error features that cause positioning errors; the error features include inertial calculation drift features, wireless signal interference features, magnetic field interference features, and vertical displacement error features; Based on the various error characteristics, an error contribution factor matrix is ​​determined; each element C_ij in the error contribution factor matrix represents the contribution of the i-th error source to the uncertainty of the j-th state variable; the state variables include position, velocity, and attitude. The confidence level is determined based on the error contribution factor matrix.

9. A high-precision positioning method for a personal locator according to claim 8, characterized in that, The method further includes: Calculate the standard deviation of the accelerometer data within a preset time window, and the rate of change of the gyroscope angular velocity within a preset time window; When the standard deviation exceeds the standard deviation threshold and the rate of change is greater than the rate of change threshold, the accelerometer data is integrated to estimate the drift velocity without external correction. The inertial drift characteristics are determined based on the square of the drift velocity.

10. A high-precision positioning system for a personal locator, characterized in that, The system includes: The data acquisition module is used to acquire inertial measurement unit data and wireless signal data from the personal locator; the personal locator has a built-in magnetometer, gyroscope, accelerometer, and barometer; The context judgment module is used to determine the current user behavior pattern and the type of environment based on the inertial measurement unit data and the wireless signal data. The processing mode adjustment module is used to adjust the positioning processing mode according to the user behavior pattern and the environment category; the adjustment includes changing the role of different wireless signal data in positioning and modifying the error description parameters of the location estimation program; The information generation and evaluation module is used to generate location information and corresponding credibility based on the adjusted positioning processing method.

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