Multi-mode positioning work card system and method integrating speed sensor and Beidou positioning

Abstract

The invention relates to a multimode positioning work card system integrating a speed sensor and Beidou positioning and a method thereof. The system comprises a Beidou positioning module, an IMU (Inertial Measurement Unit) and a data processing module, and can dynamically fuse Beidou and IMU data and output an accurate positioning result. And the data processing module integrates a sensor calibration unit, a self-adaptive Kalman filter and a map auxiliary correction unit, and performs IMU error calibration, signal fusion and trajectory correction. The system dynamically adjusts the trust weight according to the Beidou signal quality through adaptive Kalman filtering, IMU dead reckoning is started when the signal is interrupted, a stride frequency detection and other motion constraint models are introduced, and the positioning error is remarkably reduced. In addition, the map auxiliary correction function corrects the IMU reckoning trajectory through preset geo-fence data, and the problem of positioning drift of a traditional system in a complex environment is solved.

Description

Technical Field

[0001] The present invention relates to the field of positioning and tracking, and in particular to a multi-mode positioning card system and method that integrates a speed sensor and Beidou positioning. Background Art

[0002] The BeiDou Satellite Navigation System (BDS), my country's independently developed global satellite navigation system, provides high-precision positioning services worldwide. Its public service accuracy reaches meter-levels, providing reliable absolute position information for various terminal devices in open environments. However, the BeiDou system relies on satellite signals for positioning. In signal-blocking scenarios, such as indoors, in tunnels, and in densely populated areas with tall buildings, satellite signal strength significantly attenuates or even completely ceases, rendering positioning ineffective. This creates the so-called "urban canyon effect" or "indoor positioning blind spots." This technical bottleneck severely restricts the BeiDou system's application in complex environments. In particular, in scenarios requiring continuous and reliable positioning, such as tunnel construction, warehousing and logistics, and indoor navigation, a single reliance on BeiDou positioning technology no longer meets practical needs.

[0003] Furthermore, existing systems still face technical bottlenecks in error suppression. On the one hand, IMU hardware errors (such as bias drift and temperature drift) directly affect the accuracy of fusion positioning. Although initial errors can be reduced through factory calibration, changes in ambient temperature and device aging will still cause errors to gradually accumulate over long periods of operation, and most existing systems lack effective online calibration mechanisms. On the other hand, map information, as important prior knowledge, can effectively constrain the IMU's inferred trajectory and avoid conflicts with the actual environment. However, existing technologies are relatively simple in their application of geofencing, typically only setting hard boundary constraints and lacking detailed modeling of complex scenarios (such as multi-story buildings and indoor obstacle distribution), resulting in limited trajectory correction effects. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the existing technology and to propose a multi-mode positioning card system and method that integrates a speed sensor and Beidou positioning.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions: a multi-mode positioning card system that integrates a speed sensor and Beidou positioning, the system comprising a Beidou positioning module, an IMU inertial measurement unit, and a data processing module; the data processing module integrates a sensor calibration unit, an adaptive Kalman filter, and a map-assisted correction unit, for dynamically fusing Beidou and IMU data and outputting a final positioning result; the sensor calibration unit is used to perform error calibration on the IMU inertial measurement unit, the error calibration including accelerometer bias calibration and gyroscope temperature drift suppression; the accelerometer bias calibration calculates the zero bias error in real time through a stationary or uniform motion trajectory when the Beidou signal is available.

[0006] Preferably, the IMU inertial measurement unit includes a 3-axis gyroscope and a 3-axis accelerometer, with a measurement range of ±250° / s to ±2000° / s and ±2g to ±16g, respectively, and a sampling frequency of 100Hz to 1000Hz.

[0007] Preferably, the adaptive Kalman filter dynamically adjusts the trust weight according to the Beidou signal quality. When the signal is good, the Beidou data dominates and resets the IMU integral error. When the signal is interrupted, the IMU dead reckoning is dominant and a motion constraint model is introduced.

[0008] Preferably, the map-based auxiliary correction uses preset scene geographic information to force correction to a reasonable path when the IMU-calculated trajectory conflicts with the map.

[0009] Preferably, the Beidou positioning module adopts a hybrid positioning mode switching mechanism, including a smooth transition mechanism, which gradually increases the IMU weight when entering indoors from outdoors, and uses the first restored Beidou signal to reversely compensate for the IMU accumulated error when returning to outdoors.

[0010] Preferably, the motion constraint model of the adaptive Kalman filter is based on cadence detection, and the trajectory is estimated by constraining the pedestrian's stride length using the IMU.

[0011] Preferably, the map-assisted correction unit presets geo-fence data including building boundaries and indoor-outdoor transition area boundaries.

[0012] A method for a multi-mode positioning card system integrating a speed sensor and Beidou positioning, wherein the method comprises the following specific steps: S1: Obtain the initial position information through the Beidou positioning module and calibrate the IMU inertial measurement unit through the sensor calibration unit; S2: The data processing module receives data from the Beidou positioning module and the IMU inertial measurement unit in real time, dynamically fuses the data through an adaptive Kalman filter, and adjusts the trust weight based on the Beidou signal quality; S3: When the BeiDou signal is interrupted, the IMU dead reckoning mode is started and a motion constraint model is introduced to suppress drift. S4: The map-assisted correction unit detects conflicts between the IMU-calculated trajectory and the preset geofence, and forcibly corrects the trajectory to a reasonable path; S5: When switching between indoor and outdoor scenes, a hybrid positioning mode switching mechanism is executed to smoothly adjust the IMU weights and compensate for the accumulated error.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a multi-mode positioning card system and method that integrates speed sensors and Beidou positioning. In terms of positioning accuracy, the present invention dynamically integrates Beidou and IMU data through adaptive Kalman filtering. When the signal is good, Beidou data is dominant and the IMU error is reset. When the signal is interrupted, IMU dead reckoning is started and a motion constraint model (such as pedestrian cadence detection) is introduced, reducing the position error under a 30-minute signal interruption from ±50 meters of the traditional solution to ±5 meters; at the same time, the map-based auxiliary correction function forcibly corrects the IMU-calculated trajectory through preset geographic fences (such as building boundaries, indoor and outdoor transition areas), further suppressing the cumulative error, so that the positioning result always remains within a reasonable path range, and solving the positioning drift problem of traditional systems in complex scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0015] In order to provide a further understanding of the purpose, structure, features, and functions of the present invention, the present invention is described in detail below with reference to the embodiments.

[0016] Please refer to Figure 1 The present invention provides a multi-mode positioning card system that integrates a speed sensor and Beidou positioning. The system includes a Beidou positioning module, an IMU inertial measurement unit and a data processing module; the data processing module integrates a sensor calibration unit, an adaptive Kalman filter and a map-assisted correction unit, and is used to dynamically fuse Beidou and IMU data and output the final positioning result; the sensor calibration unit is used to calibrate the error of the IMU inertial measurement unit, and the error calibration includes accelerometer bias calibration and gyroscope temperature drift suppression; the accelerometer bias calibration calculates the zero bias error in real time through a stationary or uniform motion trajectory when the Beidou signal is available.

[0017] By integrating data from the Beidou positioning module and the IMU (Inertial Measurement Unit), and employing sensor calibration and an adaptive Kalman filter algorithm, high-precision, multimodal positioning is achieved. The sensor calibration unit effectively suppresses IMU sensor errors, particularly accelerometer bias and gyroscope temperature drift, improving the system's positioning accuracy and stability. The combination of the Kalman filter and the map-assisted correction module not only enhances system robustness but also improves positioning reliability in complex environments, ultimately providing more accurate and real-time positioning results to meet the needs of applications in a variety of dynamic environments.

[0018] Preferably, the IMU inertial measurement unit includes a 3-axis gyroscope and a 3-axis accelerometer, with a measurement range of ±250° / s to ±2000° / s and ±2g to ±16g, respectively, and a sampling frequency of 100Hz to 1000Hz.

[0019] The IMU inertial measurement unit combines a 3-axis gyroscope and a 3-axis accelerometer, with a wide range of measurement (±250° / s~±2000° / s and ±2g~±16g) and a high sampling frequency (100Hz~1000Hz), capable of providing high-precision, high-dynamic range motion data acquisition. This makes it particularly outstanding in complex motion environments, capable of accurately capturing fast movements, small changes, and large angle and acceleration changes, greatly improving the reliability and accuracy of motion trajectory, posture control, and real-time dynamic monitoring. Preferably, the adaptive Kalman filter dynamically adjusts the trust weight according to the Beidou signal quality. When the signal is good, the Beidou data dominates and resets the IMU integral error. When the signal is interrupted, the IMU dead reckoning is dominant and a motion constraint model is introduced.

[0020] Adaptive Kalman filtering dynamically adjusts the trust weights of Beidou signals and IMU data to ensure accurate positioning results in different signal environments.

[0021] When the Beidou signal quality is good, the system relies on Beidou data first and resets the IMU integral error, effectively eliminating the cumulative error of inertial navigation; when the Beidou signal is interrupted, the system relies on the IMU for dead reckoning and combines it with the motion constraint model for compensation, so that the positioning results remain stable and continuous, avoiding positioning drift or failure caused by signal loss, thereby improving the robustness and reliability of the system in complex dynamic environments.

[0022] Preferably, the map-based auxiliary correction uses preset scene geographic information to force correction to a reasonable path when the IMU-calculated trajectory conflicts with the map.

[0023] By combining pre-set scene geographic information, the system automatically performs auxiliary corrections when the IMU-calculated trajectory conflicts with map data, forcing the positioning result to a reasonable path. This mechanism effectively avoids trajectory deviations caused by IMU error accumulation or environmental interference, ensuring that the system always drives or moves along the true path, significantly improving positioning accuracy and navigation reliability. This can significantly reduce path drift and positioning errors, especially in complex or dynamic environments, and enhance system stability and reliability.

[0024] Preferably, the Beidou positioning module adopts a hybrid positioning mode switching mechanism, including a smooth transition mechanism, which gradually increases the IMU weight when entering indoors from outdoors, and uses the first restored Beidou signal to reversely compensate for the IMU accumulated error when returning to outdoors.

[0025] Through the hybrid positioning mode switching mechanism, a smooth transition function is realized in the Beidou positioning module, which can intelligently adjust the positioning strategy according to environmental changes.

[0026] When entering indoors from outdoors, the system gradually increases the weight of the IMU data to ensure high-precision positioning even when the Beidou signal is weak or fails; and when returning outdoors from indoors, the system uses the first restored Beidou signal for reverse compensation to correct the accumulated error of the IMU and avoid positioning drift.

[0027] This mechanism effectively balances external signals and inertial navigation data, improves the continuity and reliability of the positioning system in different environments, and ensures seamless switching and precise navigation.

[0028] This invention achieves seamless coverage across all scenarios through a dynamic weight adjustment mechanism. Traditional systems are prone to positional jumps due to sudden signal changes when switching between indoor and outdoor environments. However, this invention employs a smooth transition mechanism: When transitioning from outdoor to indoor, the IMU weight is gradually increased to avoid sudden changes. When returning from indoor to outdoor, the first recovered Beidou signal is used to reversely compensate for the IMU's accumulated error, ensuring the continuity and stability of positioning results. Furthermore, by constraining the IMU's estimated trajectory through a cadence detection model (for example, setting a pedestrian's stride length to 1.2 meters per step), the rationality of the trajectory in complex motion conditions is further improved, enabling the system to adapt to the needs of various motion scenarios, including pedestrians and vehicles.

[0029] Preferably, the motion constraint model of the adaptive Kalman filter is based on cadence detection, and the trajectory is estimated by constraining the pedestrian's stride length using the IMU.

[0030] By introducing a motion constraint model based on cadence detection through an adaptive Kalman filter, the pedestrian's stride length information is used to constrain and correct the IMU-derived trajectory. This approach effectively reduces trajectory deviations caused by IMU error accumulation, especially in stable gait conditions. By using precise stride length data to correct positioning, the accuracy and stability of gait navigation are improved. This design is particularly suitable for pedestrian navigation and gait tracking scenarios, providing more accurate positioning results in complex environments and enhancing the system's adaptability and robustness to gait motion.

[0031] Preferably, the map-assisted correction unit presets geo-fence data including building boundaries and indoor-outdoor transition area boundaries.

[0032] By presetting geo-fence data in the map-assisted correction unit, including building boundaries and indoor-outdoor transition area boundaries, environmental changes can be accurately identified during the positioning process.

[0033] When the system approaches building boundaries or enters the transition zone between indoor and outdoor areas, it automatically corrects positioning results to prevent drift or error accumulation. Especially in complex indoor and outdoor environments, this mechanism effectively combines map information with path constraints to ensure that the positioning system always follows a reasonable path, improving positioning accuracy and reliability and avoiding positioning failures caused by signal interruption or error accumulation.

[0034] A method for a multi-mode positioning card system integrating a speed sensor and Beidou positioning, wherein the method comprises the following specific steps: S1: Obtain the initial position information through the Beidou positioning module and calibrate the IMU inertial measurement unit through the sensor calibration unit.

[0035] When a construction worker enters the work area with a work card, the system starts to perform the following operations: In open areas such as tunnel entrances, the Beidou positioning module receives BDS B1 / B2 frequency signals to obtain the initial position coordinates (longitude, latitude, and altitude) of the work card with an accuracy of 1-3 meters; this position information serves as the benchmark starting point for subsequent IMU dead reckoning.

[0036] The sensor calibration unit uses the current static state (a construction worker briefly stopping) or uniform motion trajectory (such as walking at a constant speed to the tunnel entrance) to calculate the accelerometer bias error and gyroscope temperature drift parameters in real time. The specific calculation method is as follows: S11: Collect the acceleration values ​​of the three axes in the static state and take the average value of multiple sampling as the zero bias error compensation value (accuracy ±0.1mg).

[0037] S12: Combined with the temperature sensor data, a drift model is established at the current ambient temperature to dynamically correct the angular velocity measurement (temperature drift suppression accuracy is ±0.05° / h).

[0038] S2: The data processing module receives data from the Beidou positioning module and the IMU inertial measurement unit in real time, dynamically fuses the data through an adaptive Kalman filter, and adjusts the trust weight according to the Beidou signal quality.

[0039] After the construction workers enter the tunnel, the system receives BeiDou and IMU data in real time, and the data processing module performs dynamic fusion through the adaptive Kalman filter: The BeiDou module continuously monitors the signal-to-noise ratio (SNR) and the number of visible satellites.

[0040] If the SNR is ≥15dB and the number of satellites is ≥4, it is judged as "good signal"; if the SNR is <10dB or the number of satellites is <3, it is judged as "signal interruption".

[0041] When the signal is good, BeiDou data dominates the fusion process, with a weight of 0.9-1.0, and the IMU data weight is reduced to 0.1-0.0. At the same time, BeiDou data is used to reset the IMU integration error (eliminating accumulated drift).

[0042] S3: When the BeiDou signal is interrupted, the IMU dead reckoning mode is started and a motion constraint model is introduced to suppress drift.

[0043] When the construction workers completely enter the tunnel (Beidou signal interruption), the system starts the IMU dead reckoning mode and introduces the gait model to constrain the trajectory: S31: Cadence detection: The IMU accelerometer collects triaxial acceleration data at a sampling rate of 500 Hz and uses a peak detection algorithm to identify pedestrian steps (typical cadence is 1.5-2.0 steps / second). S32: Stride length estimation: Based on an ergonomic model, the average stride length is set to 1.2 meters per step (adjustable based on individual differences). Each time a complete stride is detected, the IMU-derived position update is calculated as the stride length × the direction cosine matrix (determined by the heading angle measured by the gyroscope). S33: Motion Constraint: Compares the instantaneous speed calculated by the IMU with the theoretical speed calculated by the step length / cadence. If the deviation exceeds a threshold (such as 0.5m / s), the filter gain is dynamically adjusted to suppress the propagation of outliers.

[0044] S4: The map-assisted correction unit detects conflicts between the IMU-calculated trajectory and the preset geofence, and forcibly corrects the trajectory to a reasonable path.

[0045] The work card is pre-installed with a digital map of the tunnel, which includes geo-fence data such as the tunnel centerline, width (usually 5-7 meters), turning radius (minimum 3 meters), and entrance and exit locations. When the calculated position exceeds the feasible area, an alarm is triggered and the route is forced to be corrected until the vehicle exits the tunnel. S5: When switching between indoor and outdoor scenes, a hybrid positioning mode switching mechanism is executed to smoothly adjust the IMU weights and compensate for the accumulated error.

[0046] When the construction workers exit the tunnel, the system switches to hybrid positioning mode.

[0047] The BeiDou module continuously scans for satellite signals. When the signal-to-noise ratio (SNR) is ≥12dB for three consecutive seconds and the number of satellites is ≥3, it is considered "signal recovery."

[0048] The data processing module estimates the upper limit of the position error based on the signal interruption duration (e.g., 30 minutes) and the IMU drift model (angular velocity drift of 0.1% / h). The error correction vector is calculated by using the BeiDou position data that was restored for the first time and combining it with the IMU trajectory calculated during the interruption period.

[0049] The correction vector is injected back into the system state estimation through the Kalman filter to control the final positioning error within a certain range.

[0050] The present invention has been described with reference to the above embodiments. However, the above embodiments are merely exemplary embodiments of the present invention. It should be noted that the disclosed embodiments do not limit the scope of the present invention. On the contrary, modifications and improvements that do not depart from the spirit and scope of the present invention are intended to be protected by the present invention.

Claims

1. A multi-mode positioning card system integrating a speed sensor and Beidou positioning, characterized by: The system includes a Beidou positioning module, an IMU inertial measurement unit, and a data processing module; the data processing module integrates a sensor calibration unit, an adaptive Kalman filter, and a map-assisted correction unit, and is used to dynamically fuse Beidou and IMU data and output a final positioning result; the sensor calibration unit is used to calibrate the IMU inertial measurement unit, and the error calibration includes accelerometer bias calibration and gyroscope temperature drift suppression; The accelerometer bias calibration calculates the zero bias error in real time through a stationary or uniform motion trajectory when the Beidou signal is available.

2. The multi-mode positioning card system integrating a speed sensor and Beidou positioning according to claim 1, characterized in that: The IMU inertial measurement unit includes a 3-axis gyroscope and a 3-axis accelerometer with a range of ±250° / s~±2000° / s and ±2g~±16g, and a sampling frequency of 100Hz~1000Hz.

3. The multi-mode positioning card system integrating a speed sensor and Beidou positioning according to claim 1, characterized in that: The adaptive Kalman filter dynamically adjusts the trust weight according to the Beidou signal quality. When the signal is good, Beidou data dominates and resets the IMU integral error. When the signal is interrupted, the IMU dead reckoning is dominant and a motion constraint model is introduced.

4. The multi-mode positioning card system integrating a speed sensor and Beidou positioning according to claim 1, characterized in that: The map-based auxiliary correction uses preset scene geographic information to force correction to a reasonable path when the IMU-calculated trajectory conflicts with the map.

5. The multi-mode positioning card system integrating a speed sensor and Beidou positioning according to claim 1, characterized in that: The Beidou positioning module adopts a hybrid positioning mode switching mechanism, including a smooth transition mechanism, which gradually increases the IMU weight when entering indoors from outdoors, and uses the first restored Beidou signal to reversely compensate for the IMU accumulated error when returning to outdoors.

6. The multi-mode positioning card system integrating a speed sensor and Beidou positioning according to claim 3, characterized in that: The motion constraint model of the adaptive Kalman filter is based on cadence detection and the trajectory is estimated by the pedestrian's step length constraint IMU.

7. The multi-mode positioning card system integrating a speed sensor and Beidou positioning according to claim 1, characterized in that: The map-assisted correction unit presets geographic fence data including building boundaries and indoor-outdoor transition area boundaries.

8. A method for a multi-mode positioning card system integrating a speed sensor and Beidou positioning, characterized in that: The specific steps of the method are as follows: S1: Obtain the initial position information through the Beidou positioning module and calibrate the IMU inertial measurement unit through the sensor calibration unit; S2: The data processing module receives data from the BeiDou positioning module and the IMU inertial measurement unit in real time, dynamically fuses the data through an adaptive Kalman filter, and adjusts the trust weight based on the BeiDou signal quality; S3: When the BeiDou signal is interrupted, the IMU dead reckoning mode is started and a motion constraint model is introduced to suppress drift. S4: The map-assisted correction unit detects conflicts between the IMU-calculated trajectory and the preset geofence, and forcibly corrects the trajectory to a reasonable path; S5: When switching between indoor and outdoor scenes, a hybrid positioning mode switching mechanism is executed to smoothly adjust the IMU weights and compensate for the accumulated error.

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