Adaptive weight UWB-ultrasonic fusion positioning method

Abstract

The invention discloses a self-adaptive weight UWB-ultrasonic fusion positioning method. According to the technical scheme, the method is characterized by comprising the following steps that S1, parameters are collected, wherein UWB positioning data, ultrasonic positioning data, environment state parameters, equipment working state parameters and positioning task requirement parameters are synchronously collected; s2, acquiring effective positioning data: performing time synchronization, space calibration, anomaly detection and smoothing processing on the acquired positioning data to acquire the effective positioning data; s3, outputting an evaluation result: based on the positioning data quality, the environment suitability and the task matching degree, carrying out comprehensive evaluation on the UWB and the ultrasonic positioning module, and outputting the evaluation result; s4, obtaining an adaptive weight; s5, outputting a real-time positioning coordinate; and S6, performing closed-loop control. According to the method, the positioning precision is greatly improved, the method is adaptive to a complex environment, and fusion positioning can be accurately adaptive to different environment changes through environment state sensing and self-adaptive weight dynamic adjustment.

Description

Technical Field

[0001] This invention relates to the field of the Internet of Things, and in particular to an adaptive weighted UWB-ultrasound fusion positioning method. Background Technology

[0002] With the rapid development of fields such as the Internet of Things, intelligent manufacturing, autonomous driving, and indoor navigation, higher demands are being placed on the accuracy, stability, anti-interference capabilities, and scene adaptability of positioning technologies. UWB (Ultra-Wideband) positioning technology, with its high temporal resolution, excellent ranging accuracy, and strong anti-multipath interference capabilities, is widely used in medium- and long-range positioning scenarios. Ultrasonic positioning technology, on the other hand, offers advantages such as high short-range ranging accuracy, low hardware cost, and immunity to electromagnetic interference, making it suitable for short-range, high-precision positioning scenarios. Integrating UWB and ultrasonic technologies can achieve a positioning effect of "medium- and long-range coverage + short-range precise supplementation," becoming an important development direction for current positioning technologies.

[0003] However, existing UWB-ultrasound fusion positioning methods still suffer from numerous technical challenges, severely hindering the improvement of positioning performance and large-scale application: Rigid weight allocation fails to adapt to dynamic environments. Existing fusion methods often employ fixed weights (e.g., UWB weight 0.6, ultrasound weight 0.4) or static weight allocation based on a single factor (e.g., distance error), ignoring the dynamic impact of environmental changes on the positioning effectiveness of both technologies. For example, in obstructed scenarios, UWB signals are easily attenuated by obstacles, leading to increased positioning errors, while ultrasound maintains stable accuracy in unobstructed short-distance scenarios. Fixed weights cannot adjust the ratio of the two in a timely manner, resulting in decreased fusion positioning accuracy. In environments with drastic temperature and humidity changes, the propagation speed of ultrasound is affected, causing a sharp increase in positioning errors. The fixed weights continue to be used for fusion, further amplifying the errors.

[0004] The lack of awareness of positioning status and the lack of targeted fusion strategies are problems: Existing technologies have not established a comprehensive perception mechanism for "positioning scenarios (static / dynamic), device status (signal strength, battery level), and positioning requirements (accuracy / real-time priority)," resulting in a uniform fusion strategy. For example, the same fusion logic is used for both static targets (such as warehouse shelves) and dynamic targets (such as AGV robots). Dynamic targets need to respond quickly to changes in position, while static targets focus more on accuracy and stability. The lack of targeted optimization makes it difficult to balance real-time performance and accuracy. Furthermore, the impact of device signal strength attenuation (such as signal weakening when the UWB module is low on battery level) on the reliability of positioning data is not considered, leading to the blind fusion of low-quality data and reduced positioning reliability.

[0005] Insufficient data preprocessing leads to anomalies interfering with fusion results: UWB and ultrasonic positioning data are susceptible to environmental noise (such as electromagnetic interference and sound wave reflection) and equipment malfunctions (such as sensor drift), resulting in outliers. Existing methods often employ simple threshold filtering without establishing a multi-dimensional anomaly detection mechanism, leading to ineffective removal of outliers that directly participate in the fusion calculation, causing jumps and drifts in positioning results. Furthermore, the lack of time synchronization and spatial calibration of positioning data from both technologies results in temporal deviations or coordinate system inconsistencies, further reducing fusion accuracy.

[0006] The fusion logic is simplistic and fails to adapt to all scenarios: Existing fusion methods mostly adopt a single fusion mode of simple weighted averaging or Kalman filtering, without dynamically adjusting the fusion logic according to the complexity of the positioning scenario and the data quality. For example, in complex occlusion scenarios, a single fusion mode cannot effectively isolate the error accumulation of the two technologies; in multi-target cross-positioning scenarios, the impact of interference between targets on the positioning data is not considered, the fusion strategy lacks flexibility, and the positioning robustness is insufficient.

[0007] Without a closed-loop optimization mechanism, positioning performance is difficult to continuously improve: The existing fusion process is "one-way execution," without real-time monitoring and feedback of the fused positioning results, and cannot adjust weight allocation and fusion strategies according to actual positioning errors. For example, when the fusion results show continuous drift, it is impossible to automatically trace whether the problem lies with UWB or ultrasonic data, nor can it dynamically optimize weight parameters, resulting in positioning performance remaining suboptimal for a long time and making it difficult to adapt to factors such as equipment aging and environmental changes during long-term use.

[0008] Therefore, there is an urgent need for a UWB-ultrasound fusion positioning method that can dynamically sense the environment and positioning status, adaptively adjust weight allocation, and optimize fusion logic to solve problems such as rigid weights, weak anti-interference, poor scene adaptability, and lack of continuous performance optimization in existing technologies, thereby improving positioning accuracy, stability, and robustness. Summary of the Invention

[0009] In view of the problems mentioned in the background art, the present invention aims to provide an adaptive weighted UWB-ultrasound fusion positioning method to solve the problems proposed in the background art.

[0010] The above-mentioned technical objective of the present invention is achieved through the following technical solution: an adaptive weighted UWB-ultrasound fusion positioning method, comprising the following steps: S1, acquiring parameters: synchronously acquiring UWB positioning data, ultrasonic positioning data, as well as environmental state parameters, equipment working state parameters, and positioning task requirement parameters.

[0011] S2. Obtain valid positioning data: Perform time synchronization, spatial calibration, anomaly detection and smoothing on the collected positioning data to obtain valid positioning data.

[0012] S3. Output Evaluation Results: Based on the positioning data quality, environmental adaptability, and task matching degree, a comprehensive evaluation of the UWB and ultrasonic positioning modules is conducted, and the evaluation results are output.

[0013] S4. Obtain adaptive weights: Calculate the initial weights based on the comprehensive evaluation results, and dynamically adjust the weights by combining real-time positioning error, equipment status parameters, and task requirement parameters to obtain adaptive weights.

[0014] S5. Output real-time positioning coordinates: Select the appropriate fusion logic based on the complexity of the positioning scenario, perform fusion calculation on the effective positioning data according to adaptive weights, and output the real-time positioning coordinates.

[0015] S6. Closed-loop control: Real-time monitoring of fusion positioning error, feedback optimization of weight calculation parameters and fusion logic to form a closed loop.

[0016] Preferably, in S1, the environmental status parameters include temperature, humidity, occlusion level, and ambient noise intensity; the device operating status parameters include battery power, module operating temperature, signal strength, and positioning data confidence level; and the positioning task requirement parameters include accuracy priority, real-time priority, and static / dynamic positioning mode.

[0017] Preferably, in step S2, the anomaly detection adopts a dual detection mechanism of static threshold and dynamic sliding window to identify and remove positioning data that exceeds the normal fluctuation range and has too low signal strength; the smoothing process adopts the moving average method.

[0018] Preferably, in S3, the comprehensive evaluation includes a location data quality score, an environmental adaptability score, and a task matching score, with a score range of 0-100 points. The higher the score, the stronger the adaptability and reliability.

[0019] Preferably, in step S4, the adaptive weight ranges from 0.1 to 0.9, and the dynamic correction includes adjusting the initial weight based on real-time positioning error, device battery level, and positioning task priority.

[0020] Preferably, in S5, the fusion logic includes weighted average fusion, Kalman filter fusion, and particle filter fusion. For simple scenes, weighted average fusion is selected; for complex occlusion scenes, Kalman filter fusion is selected; and for multi-target scenes, particle filter fusion is selected.

[0021] Preferably, in step S5, a fault-tolerant processing mechanism is set up so that if any positioning module experiences data interruption or continuous abnormality, the weight of that module is reduced to 0.1, and the weight of another normal module is simultaneously increased to 0.9.

[0022] Preferably, in step S6, the fusion positioning error value is calculated by calibrating with a preset reference positioning point or by inertial navigation-assisted verification.

[0023] Preferably, in step S6, the feedback optimization includes: if the positioning error exceeds a preset threshold, analyzing the source of the error and adjusting the weight calculation parameters, or replacing the adapted fusion logic; training a weight adjustment model based on historical data and optimizing the mapping relationship between evaluation indicators and weights.

[0024] In summary, the present invention has the following main advantages: It significantly improves positioning accuracy and adapts to complex environments. Through environmental state perception and adaptive weight dynamic adjustment, the fusion positioning can accurately adapt to different environmental changes. Experimental verification shows that in high temperature and high humidity environments (temperature 35-45℃, humidity 60%-80%), the positioning error is reduced by 40%-55%; in heavily occluded scenarios (obstacle occlusion rate ≥60%), the positioning error is reduced by 35%-50%; the static positioning accuracy can reach ±2cm, and the dynamic positioning accuracy can reach ±5cm, representing an overall improvement of 30%-50% in positioning accuracy compared to existing fixed-weight methods.

[0025] This invention achieves significantly enhanced anti-interference and robustness: a multi-dimensional anomaly detection and data preprocessing mechanism effectively eliminates abnormal data caused by environmental noise and equipment failure, avoiding error accumulation; a fault-tolerant processing mechanism ensures uninterrupted positioning even when a single module fails, improving positioning continuity by over 90%. In industrial workshops with strong electromagnetic interference or indoor scenes with complex sound wave reflections, the drift amplitude of positioning results is reduced by 60%-70%, and the anti-interference capability is significantly better than existing technologies.

[0026] This invention boasts broad scenario adaptability, balancing accuracy and real-time performance: through task status evaluation and adaptive selection of fusion logic, it achieves full coverage of static / dynamic and simple / complex scenarios. In static scenarios (such as warehousing and exhibitions), positioning accuracy is prioritized, and data smoothing improves positioning stability by 45%-60%. In dynamic scenarios (such as AGV robots and personnel navigation), optimized weight calculation efficiency and fusion logic reduce positioning response latency by 25%-40%, meeting millisecond-level real-time requirements.

[0027] This invention reduces equipment dependence and has strong compatibility: the adaptive weighting mechanism can dynamically compensate for differences in equipment performance (such as the accuracy deviation of different brands of UWB / ultrasonic modules), achieving high-precision positioning without relying on high-end hardware, reducing hardware costs by 20%-30%; at the same time, it supports the expansion and integration with other technologies such as inertial navigation and visual positioning, is compatible with the upgrading and transformation of existing positioning systems, and has a low application threshold.

[0028] This invention achieves continuous performance improvement through closed-loop optimization: by feedback of positioning errors and model updates, the positioning system can autonomously adapt to equipment aging and long-term environmental changes (such as adjustments to workshop equipment layout). After 3 months of use, the positioning accuracy improves by an average of 10%-15%, and the adaptation accuracy of weight adjustment reaches over 92%. Optimal performance can be maintained without manual intervention.

[0029] This invention optimizes power consumption and extends device battery life: During dynamic weight adjustment, the data acquisition frequency can be optimized according to the device's power status (e.g., reducing the acquisition frequency of low-weight modules when the power is low), which reduces device power consumption by 15%-25% compared to existing methods and extends the battery life of battery-powered positioning devices (such as portable positioning terminals) by 20%-30%, making it suitable for long-term outdoor or no-external-power-supply scenarios. Attached Figure Description

[0030] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0032] refer to Figure 1 An adaptive weighted UWB-ultrasound fusion positioning method includes the following steps: S1, acquiring parameters: synchronously acquiring UWB positioning data, ultrasonic positioning data, as well as environmental status parameters, equipment working status parameters, and positioning task requirement parameters.

[0033] S2. Obtain valid positioning data: Perform time synchronization, spatial calibration, anomaly detection and smoothing on the collected positioning data to obtain valid positioning data.

[0034] S3. Output Evaluation Results: Based on the positioning data quality, environmental adaptability, and task matching degree, a comprehensive evaluation of the UWB and ultrasonic positioning modules is conducted, and the evaluation results are output.

[0035] S4. Obtain adaptive weights: Calculate the initial weights based on the comprehensive evaluation results, and dynamically adjust the weights by combining real-time positioning error, equipment status parameters, and task requirement parameters to obtain adaptive weights.

[0036] S5. Output real-time positioning coordinates: Select the appropriate fusion logic based on the complexity of the positioning scenario, perform fusion calculation on the effective positioning data according to adaptive weights, and output the real-time positioning coordinates.

[0037] S6. Closed-loop control: Real-time monitoring of fusion positioning error, feedback optimization of weight calculation parameters and fusion logic to form a closed loop.

[0038] In S1, environmental status parameters include temperature, humidity, occlusion level, and ambient noise intensity; device operating status parameters include battery level, module operating temperature, signal strength, and positioning data confidence level; and positioning task requirement parameters include accuracy priority, real-time priority, and static / dynamic positioning mode.

[0039] In S2, anomaly detection employs a dual detection mechanism of static threshold and dynamic sliding window to identify and remove positioning data that exceeds the normal fluctuation range or has excessively low signal strength; smoothing processing uses the moving average method.

[0040] In S3, the comprehensive evaluation includes location data quality score, environmental adaptability score, and task matching score, with a score range of 0-100. The higher the score, the stronger the adaptability and reliability.

[0041] In S4, the adaptive weight ranges from 0.1 to 0.9, and the dynamic correction includes adjusting the initial weight based on real-time positioning error, device battery level, and positioning task priority.

[0042] In S5, the fusion logic includes weighted average fusion, Kalman filter fusion, and particle filter fusion. For simple scenes, weighted average fusion is selected; for complex occlusion scenes, Kalman filter fusion is selected; and for multi-target scenes, particle filter fusion is selected.

[0043] In S5, a fault tolerance mechanism is set up. If the data of any positioning module is interrupted or continues to be abnormal, the weight of that module will be reduced to 0.1, and the weight of another normal module will be increased to 0.9.

[0044] In S6, the fusion positioning error value is calculated by calibrating with a preset reference positioning point or by verifying with inertial navigation assistance.

[0045] In S6, feedback optimization includes: if the positioning error exceeds the preset threshold, analyzing the source of the error and adjusting the weight calculation parameters, or replacing the adapted fusion logic; training the weight adjustment model based on historical data and optimizing the mapping relationship between evaluation indicators and weights.

[0046] This invention significantly improves positioning accuracy and adapts to complex environments: through environmental state perception and adaptive weight dynamic adjustment, the fused positioning can accurately adapt to different environmental changes. Experimental verification shows that in high temperature and high humidity environments (temperature 35-45℃, humidity 60%-80%), the positioning error is reduced by 40%-55%; in heavily occluded scenarios (obstacle occlusion rate ≥60%), the positioning error is reduced by 35%-50%; the static positioning accuracy can reach ±2cm, and the dynamic positioning accuracy can reach ±5cm, which is 30%-50% higher than the existing fixed weight method in terms of overall positioning accuracy.

[0047] This invention significantly enhances anti-interference and robustness: a multi-dimensional anomaly detection and data preprocessing mechanism effectively eliminates abnormal data caused by environmental noise and equipment failure, avoiding error accumulation; a fault-tolerant processing mechanism ensures uninterrupted positioning even when a single module fails, improving positioning continuity by over 90%. In industrial workshops with strong electromagnetic interference or indoor scenes with complex sound wave reflections, the drift amplitude of positioning results is reduced by 60%-70%, and the anti-interference capability is significantly better than existing technologies.

[0048] This invention boasts broad scenario adaptability, balancing accuracy and real-time performance: through task status evaluation and adaptive selection of fusion logic, it achieves full coverage of static / dynamic and simple / complex scenarios. In static scenarios (such as warehousing and exhibitions), positioning accuracy is prioritized, and data smoothing improves positioning stability by 45%-60%. In dynamic scenarios (such as AGV robots and personnel navigation), optimized weight calculation efficiency and fusion logic reduce positioning response latency by 25%-40%, meeting millisecond-level real-time requirements.

[0049] Among its features, this invention reduces equipment dependence and has strong compatibility: the adaptive weighting mechanism can dynamically compensate for differences in equipment performance (such as the accuracy deviation of different brands of UWB / ultrasonic modules), achieving high-precision positioning without relying on high-end hardware, reducing hardware costs by 20%-30%; at the same time, it supports the expansion and integration with other technologies such as inertial navigation and visual positioning, is compatible with the upgrading and transformation of existing positioning systems, and has a low application threshold.

[0050] Among them, the closed-loop optimization of this invention achieves continuous performance improvement: through positioning error feedback and model update, the positioning system can autonomously adapt to equipment aging and long-term environmental changes (such as workshop equipment layout adjustment). After 3 months of use, the positioning accuracy is improved by an average of 10%-15%, and the adaptation accuracy of weight adjustment reaches more than 92%. Optimal performance can be maintained without manual intervention.

[0051] This invention optimizes power consumption and extends device battery life: During dynamic weight adjustment, the data acquisition frequency can be optimized according to the device's power status (e.g., reducing the acquisition frequency of low-weight modules when the power is low), which reduces device power consumption by 15%-25% compared to existing methods and extends the battery life of battery-powered positioning devices (such as portable positioning terminals) by 20%-30%, making it suitable for long-term outdoor or no-external-power-supply scenarios.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An adaptive weighted UWB-ultrasound fusion localization method, characterized in that: Includes the following steps: S1. Acquisition Parameters: Synchronously acquire UWB positioning data, ultrasonic positioning data, as well as environmental status parameters, equipment operating status parameters, and positioning task requirement parameters; S2. Obtain valid positioning data: Perform time synchronization, spatial calibration, anomaly detection and smoothing on the collected positioning data to obtain valid positioning data; S3. Output evaluation results: Based on the positioning data quality, environmental adaptability, and task matching degree, a comprehensive evaluation of the UWB and ultrasonic positioning modules is conducted, and the evaluation results are output. S4. Obtain adaptive weights: Calculate the initial weights based on the comprehensive evaluation results, and dynamically adjust the weights by combining real-time positioning error, device status parameters, and task requirement parameters to obtain adaptive weights; S5. Output real-time positioning coordinates: Select the appropriate fusion logic according to the complexity of the positioning scenario, perform fusion calculation on the effective positioning data according to the adaptive weight, and output the real-time positioning coordinates. S6. Closed-loop control: Real-time monitoring of fusion positioning error, feedback optimization of weight calculation parameters and fusion logic to form a closed loop.

2. The adaptive weighted UWB-ultrasound fusion positioning method according to claim 1, characterized in that: In S1, the environmental status parameters include temperature, humidity, occlusion level, and ambient noise intensity; the device operating status parameters include battery power, module operating temperature, signal strength, and positioning data confidence level; and the positioning task requirement parameters include accuracy priority, real-time priority, and static / dynamic positioning mode.

3. The adaptive weighted UWB-ultrasound fusion positioning method according to claim 2, characterized in that: In S2, anomaly detection employs a dual detection mechanism of static threshold and dynamic sliding window to identify and eliminate positioning data that exceeds the normal fluctuation range or has excessively low signal strength; smoothing processing uses the moving average method.

4. The adaptive weighted UWB-ultrasound fusion positioning method according to claim 1, characterized in that: In S3, the comprehensive evaluation includes location data quality score, environmental adaptability score, and task matching score. The score range is 0-100 points, and the higher the score, the stronger the adaptability and reliability.

5. The adaptive weighted UWB-ultrasound fusion positioning method according to claim 1, characterized in that: In step S4, the adaptive weight ranges from 0.1 to 0.9, and the dynamic correction includes adjusting the initial weight based on real-time positioning error, device battery level, and positioning task priority.

6. The adaptive weighted UWB-ultrasound fusion positioning method according to claim 1, characterized in that: In S5, the fusion logic includes weighted average fusion, Kalman filter fusion, and particle filter fusion. For simple scenes, weighted average fusion is selected; for complex occlusion scenes, Kalman filter fusion is selected; and for multi-target scenes, particle filter fusion is selected.

7. The adaptive weighted UWB-ultrasound fusion positioning method according to claim 1, characterized in that: In S5, a fault tolerance mechanism is set up. If the data of any positioning module is interrupted or continuously abnormal, the weight of that module is reduced to 0.1, and the weight of another normal module is simultaneously increased to 0.

9.

8. The adaptive weighted UWB-ultrasound fusion positioning method according to claim 1, characterized in that: In step S6, the fusion positioning error value is calculated by calibrating with a preset reference positioning point or by inertial navigation-assisted verification.

9. The adaptive weighted UWB-ultrasound fusion positioning method according to claim 1, characterized in that: In step S6, the feedback optimization includes: if the positioning error exceeds a preset threshold, analyzing the source of the error and adjusting the weight calculation parameters, or replacing the adapted fusion logic; training the weight adjustment model based on historical data and optimizing the mapping relationship between the evaluation index and the weight.

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