An indoor-outdoor seamless positioning method and system based on dynamic entropy value fuzzy evaluation and adaptive filtering
By employing dynamic entropy fuzzy evaluation and adaptive filtering techniques, the ping-pong effect and positioning jump problems of indoor and outdoor positioning systems in signal changing environments are solved, achieving seamless positioning switching with high accuracy and robustness, and adapting to dynamic changes in complex signal scenarios.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI UNIVERSITY OF ELECTRIC POWER
- Filing Date
- 2026-04-29
- Publication Date
- 2026-06-09
AI Technical Summary
Existing indoor and outdoor positioning systems are prone to ping-pong effects and positioning jumps in environments with large changes in signal quality. They cannot adapt to rapidly changing signal environments, resulting in unstable positioning accuracy, especially in complex signal scenarios where they cannot provide stable positioning results.
A method based on dynamic entropy fuzzy evaluation and adaptive filtering is adopted. Multiple signal quality evaluation indicators are collected in real time through a sliding window. The time-varying entropy method is used for dynamic weighting and a fuzzy logic controller to calculate the comprehensive confidence level. Combined with nonlinear mapping function and extended Kalman filter, adaptive fusion and smooth handover of multi-source observation data are achieved.
It achieves high-precision and robust seamless indoor and outdoor positioning in dynamic environments, can respond to complex signal scenarios in real time, ensures a smooth and natural positioning switching process, and outputs continuous and reliable location information.
Smart Images
Figure CN122170892A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to multi-sensor fusion positioning technology, and in particular to a seamless indoor and outdoor positioning method and system based on dynamic entropy value fuzzy evaluation and adaptive filtering. Background Technology
[0002] In existing technologies, indoor and outdoor positioning systems typically rely on threshold switching and fixed-weight fusion methods to achieve positioning switching between indoor and outdoor environments. Threshold switching methods usually determine the switching timing by setting thresholds for signal strength or the number of satellites. However, this method is prone to ping-pong effects or positioning jumps in environments with significant signal quality variations, especially in signal boundary areas (such as doorways or passageways), leading to unstable positioning accuracy and an inability to adapt to rapidly changing signal environments.
[0003] Fixed-weight fusion sets a fixed weight ratio for different signal sources, such as GNSS and UWB, during the signal fusion process. However, this method cannot adjust the weight of the signal sources in real time according to changes in the environment. Especially in complex signal scenarios, such as urban canyons and shady areas, it cannot adapt to fluctuations in signal quality, resulting in a significant decrease in positioning accuracy and failing to provide stable positioning results.
[0004] Furthermore, simply relying on IMU (Inertial Measurement Unit) to increase weights has limitations in practical applications. In highly dynamic environments, the auxiliary information provided by the IMU offers limited accuracy improvement without an effective mathematical model, and it struggles to address dynamic signal switching issues. Therefore, existing technologies have failed to effectively solve problems such as uneven signal switching, unstable accuracy, and inability to adapt to environmental changes during the positioning process.
[0005] To overcome the above problems, a new method is urgently needed that can smoothly switch positioning signals in dynamic environments, thereby improving the system's adaptability, accuracy, and robustness. Summary of the Invention
[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a seamless indoor and outdoor positioning method and system based on dynamic entropy value fuzzy evaluation and adaptive filtering.
[0007] The objective of this invention can be achieved through the following technical solutions: A seamless indoor / outdoor positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering, the method comprising: GNSS observation data, UWB ranging values, and IMU motion status data are collected and cached in real time over a continuous period of time through a sliding window; Based on the GNSS observation data and UWB ranging values, the time-varying entropy method is used to dynamically assign weights to multiple signal quality evaluation indicators, and to calculate the GNSS signal quality evaluation value and UWB signal quality evaluation value that are environmentally adaptive at the current moment. Based on the GNSS signal quality assessment value and the UWB signal quality assessment value, a comprehensive confidence level characterizing the credibility of GNSS observations is calculated using a fuzzy logic controller. The measurement noise covariance matrix is adaptively adjusted based on the comprehensive confidence level using a nonlinear mapping function. The current system state is determined by comparing the GNSS signal quality assessment value with a preset threshold. Based on the current system state and IMU motion state data, EKF measurement updates and positioning calculations are performed to obtain the current positioning. Repeated data collection and positioning calculations are then performed to achieve smooth handover of the positioning trajectory. The system states include outdoor certainty state, indoor certainty state, and transition buffer state. If the current system is in the transition buffer state, an asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and measurement noise covariance matrix corresponding to the UWB observation, and a multi-source observation joint update strategy is executed to achieve smooth handover of positioning trajectories.
[0008] Furthermore, the sliding window adopts a first-in-first-out queue mechanism, and the window acquisition setting matches the system state judgment switching frequency.
[0009] Furthermore, the signal quality evaluation indicators of the GNSS observation data include at least the number of visible satellites, the mean carrier-to-noise ratio of each satellite, the mean variance of the carrier-to-noise ratio of each satellite, and the position accuracy factor. The signal quality evaluation indicators of the UWB ranging values include at least the number of effective base stations, the mean RSSI, the variance of RSSI, and the variance of the TOF ranging values.
[0010] Furthermore, the process of dynamically weighting multiple signal quality evaluation indicators using the time-varying entropy method includes: The numerical sequences of each signal quality evaluation index within the sliding window are normalized, and their discrete probability distributions are statistically calculated based on the normalized data. Then, the information entropy of each signal quality evaluation index within the sliding window is calculated. Based on the aforementioned information entropy dynamics, and following the principle that the lower the information entropy, the higher the stability of the indicator, and the higher the weight assigned, the dynamic weight of each signal quality evaluation indicator is calculated. The GNSS signal quality assessment value and UWB signal quality assessment value for the current time are calculated by weighting and summing the dynamic weights and normalized index values.
[0011] Furthermore, the fuzzy logic controller has a dual-input single-output structure. The process of calculating the comprehensive confidence level, which characterizes the reliability of GNSS observations, through the fuzzy logic controller includes: The GNSS signal quality assessment value and the UWB signal quality assessment value are input into the fuzzy logic controller. The membership degree of each fuzzy language subset corresponding to the GNSS signal quality assessment value and the UWB signal quality assessment value is calculated through a preset membership function. Based on a preset fuzzy rule base, the Mamdani-type inference method is used to activate all matching fuzzy rules in the fuzzy rule base according to each membership degree. The activation strength of each rule is calculated by taking the smaller value, and the output conclusions of all activated rules are synthesized by taking the larger value to obtain an aggregated fuzzy set about the comprehensive confidence. The aggregated fuzzy set is defuzzified using the centroid method, and the centroid of the fuzzy set is calculated to obtain the comprehensive confidence level.
[0012] Furthermore, the preset threshold includes an outdoor switching threshold. Indoor / outdoor switching threshold When the GNSS signal quality assessment value is greater than the outdoor handover threshold, the current system is in the outdoor sure state. When the GNSS signal quality assessment value is less than the indoor handover threshold, the current system is in the indoor sure state. When the GNSS signal quality assessment value is greater than or equal to the indoor handover threshold and less than or equal to the outdoor handover threshold, the current system is in the transition buffer state.
[0013] Furthermore, the process of updating EKF measurements and calculating positioning based on the current system state includes: If the current system is in an outdoor certainty state, then based on the measured noise covariance matrix, GNSS observation data and IMU motion state data, calculate the Kalman gain, update the state estimate and covariance estimate, and complete the GNSS single-source positioning solution; If the current system is in an indoor certain state, then based on the measured noise covariance matrix, UWB ranging value and IMU motion state data, calculate the Kalman gain, update the state estimate and covariance estimate, and complete the UWB single-source positioning solution. If the current system is in a transition buffer state, an asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and the measurement noise covariance matrix corresponding to the UWB observations. A multi-source observation joint update strategy is then executed to perform multi-source fusion positioning solution.
[0014] Furthermore, the process of executing the multi-source observation joint update strategy and performing multi-source fusion positioning calculation includes: A joint measurement vector is constructed based on the collected GNSS observation data and UWB ranging values; An asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and the measurement noise covariance matrix corresponding to UWB observations. Based on the joint measurement vector, a block measurement matrix adapted to it is constructed; Based on the joint measurement vector, asymmetric block diagonal matrix, block measurement matrix, and IMU motion state data, the Kalman gain is calculated, the state estimate and covariance estimate are updated, and the multi-source fusion positioning solution is completed.
[0015] An indoor / outdoor seamless positioning system based on dynamic entropy value fuzzy evaluation and adaptive filtering, the system comprising: Data acquisition module: It collects and caches GNSS observation data, UWB ranging values and IMU motion status data in real time through a sliding window, and transmits the processed standardized data packets to other modules in real time through a high-speed serial bus. Dynamic entropy evaluation module: Based on the GNSS observation data and UWB ranging values, the time-varying entropy method is used to dynamically assign weights to multiple signal quality evaluation indicators, and calculate the environmental adaptive GNSS signal quality evaluation value and UWB signal quality evaluation value at the current moment; Fuzzy logic control module: Based on the GNSS signal quality assessment value and the UWB signal quality assessment value, the fuzzy logic controller calculates the comprehensive confidence level that characterizes the credibility of GNSS observations; The adaptive fusion positioning module: utilizes a nonlinear mapping function to adaptively adjust the measurement noise covariance matrix based on the comprehensive confidence level; determines the current system state by comparing the GNSS signal quality assessment value with a preset threshold, and performs EKF measurement updates and positioning calculations based on the current system state and IMU motion state data to obtain the current positioning; repeats data acquisition and positioning calculations to achieve smooth handover of positioning trajectories; wherein, The system states include outdoor certainty state, indoor certainty state, and transition buffer state. If the current system is in the transition buffer state, an asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and measurement noise covariance matrix corresponding to the UWB observation, and a multi-source observation joint update strategy is executed to achieve smooth handover of positioning trajectories.
[0016] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the indoor-outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering as described above.
[0017] Compared with the prior art, the beneficial effects of the present invention include: 1. This invention constructs a closed-loop collaborative adaptive information processing and control framework. This framework uses information entropy as a theoretical tool to dynamically quantify the uncertainty of signal quality, employs fuzzy logic as a decision-making bridge to transform quality assessment into continuous confidence parameters, and finally achieves optimal fusion of multi-source observation data through adaptive extended Kalman filtering. The entire positioning process is uniformly scheduled by a three-level system state determination, ensuring a high degree of matching between system behavior and the physical environment. Through the deep integration of theoretical framework and technical principles, this invention possesses strong environmental adaptability and can respond in real time to complex and changing signal scenarios. The positioning switching process is smooth and natural, maintaining high robustness even under extreme signal conditions, and stably outputting continuous and reliable location information. It achieves a balance between high-precision positioning and high reliability in dynamic environments, providing an effective solution for seamless indoor and outdoor positioning.
[0018] 2. This invention abandons traditional evaluation methods using fixed thresholds or simple statistics, and innovatively introduces information entropy as a quantification tool for signal quality uncertainty. By calculating the information entropy of the normalized value sequence of various signal quality indicators (such as the number of visible satellites, position accuracy factor PDOP, and mean and variance of carrier-to-noise ratio) within a sliding window, the system not only generates a comprehensive score, but more importantly, outputs a stability measure for each indicator. This weighting mechanism based on dynamic probability distribution enables the system to fundamentally distinguish between gradual signal degradation and sudden interference. Furthermore, the fuzzy logic controller constructed in this invention utilizes both the comprehensive score and the entropy information of each indicator in its rule base design. This deep collaborative decision-making mechanism of dynamic entropy evaluation and fuzzy logic enables the system to respond more precisely and forward-lookingly to complex changes in the signal environment than traditional methods, providing a more robust decision basis for subsequent filtering and fusion.
[0019] 3. In this invention, the sliding window setting is matched with the system state judgment switching frequency to ensure that the signal quality data contained in the window can fully reflect the gradual change process of the environment during the state transition, thereby providing a coherent data basis for the dynamic entropy value evaluation and adaptive adjustment.
[0020] 4. This invention innovatively integrates comprehensive confidence level as a key adjustment parameter into the extended Kalman filter framework. Its basic principle is to dynamically convert confidence level into adjustment coefficients of the measurement noise covariance matrix through a preset nonlinear exponential mapping function. When signal quality is excellent and confidence level approaches 1, the measurement noise covariance matrix corresponding to GNSS approaches the reference value, and the filter fully trusts and utilizes GNSS observations. When signal quality deteriorates and confidence level decreases, the measurement noise covariance matrix grows exponentially, thereby automatically reducing the filter's trust weight for the current GNSS observations. This mechanism theoretically ensures that the filter can achieve a dynamic optimal balance between the accuracy and robustness of state estimation based on the real-time reliability of the observation data. It utilizes the rapid exponential decay (dynamic stripping) of GNSS weights to avoid severe deviations during momentary obstruction, while retaining the high weight of UWB (fixed anchoring) to maintain system state convergence, thus overcoming the gain oscillation risk that symmetrical adjustment easily generates in the signal boundary region.
[0021] 5. This invention defines three discrete states: an outdoor certainty state, a transition buffer state, and an indoor certainty state. Its core innovation lies in the specific design of the transition buffer state. In this state, the system does not simply select either GNSS or UWB, but simultaneously accepts observation data from both. Fusion is achieved by constructing a dynamic-fixed asymmetric block-diagonal joint measurement noise covariance matrix specifically for the transition state. GNSS signals exhibit non-stationary degradation upon entering a building, making them suitable for characterization using a dynamic model based on real-time confidence assessment to achieve interference isolation. UWB, on the other hand, exhibits stable error characteristics within the line-of-sight range, and a fixed noise covariance matrix can serve as a stability benchmark anchor point for the system. This block-diagonal asymmetric form mathematically guarantees that the update steps of the two observation sources are independent, ensuring that drastic fluctuations at the GNSS end do not contaminate the high-precision UWB observations through covariance cross-contamination. Attached Figure Description
[0022] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a flowchart of the dynamic entropy calculation based on a sliding window according to the present invention; Figure 3 This is a membership function diagram of the fuzzy controller of the present invention; Figure 4 This is an adaptive adjustment curve of the measurement noise covariance matrix of the present invention; Figure 5 This is the three-level state machine transition logic diagram of the present invention; Figure 6 This is a trajectory comparison diagram between the method of this invention and the traditional method in indoor-outdoor switching scenarios. Detailed Implementation
[0023] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0024] Example 1 This embodiment discloses an indoor / outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering, the method as follows: Figure 1 As shown, steps S1-S5 are included, and each step is described in detail below: Step S1: Collect and cache GNSS observation data, UWB ranging values and IMU motion status data in real time over a continuous period of time through a sliding window.
[0025] The sliding window adopts a first-in-first-out (FIFO) queue mechanism, and the window acquisition settings are matched with the system state judgment switching frequency.
[0026] Specifically, the window size is 50 frames, and the data update frequency for each frame is 10Hz. The sliding window setting matches the switching speed of the three-level state machine, ensuring that the signal quality data contained in the window can fully reflect the gradual change of the environment during the state transition, thereby providing a coherent data foundation for dynamic entropy value assessment and adaptive adjustment.
[0027] After extracting multiple key quality metrics from each frame of data, the values of each metric need to be standardized for unified cross-metric analysis. A real-time normalization method based on the current sliding window is employed. For any given metric... Let its current observation value be... And it forms a set of all historical values within the current sliding window. Its normalized value The calculation formula is as follows: This formula uses the maximum and minimum values within a window as a dynamic benchmark to map the current value to the [0,1] interval. If the maximum value within the window equals the minimum value, the normalization result is defined as 0.5. The advantage of this method is that its normalization benchmark adapts to changes in the environment, and can more sensitively reflect the relative fluctuations of the indicator value.
[0028] Step S2: Based on GNSS observation data and UWB ranging values, the time-varying entropy method is used to dynamically assign weights to multiple signal quality evaluation indicators, and calculate the environmental adaptive GNSS signal quality evaluation value and UWB signal quality evaluation value at the current moment.
[0029] The signal quality evaluation indicators for GNSS observation data should include at least the number of visible satellites, the mean carrier-to-noise ratio (C / N0) of each satellite, the mean variance of the carrier-to-noise ratio of each satellite, and the position accuracy factor (PDOP).
[0030] The signal quality evaluation metrics for UWB ranging values should include at least the number of effective base stations, the mean RSSI, the variance of RSSI, and the variance of TOF ranging values.
[0031] like Figure 2 As shown, Figure 2 This is a flowchart illustrating the dynamic entropy calculation process based on a sliding window, showing the specific steps of window sliding and weight updates. The diagram clearly describes how the entropy value in the sliding window is updated based on real-time data.
[0032] The process of dynamically weighting multiple signal quality evaluation indicators using the time-varying entropy method includes: The numerical sequences of each signal quality evaluation index within the sliding window are normalized, and their discrete probability distributions are statistically calculated based on the normalized data. Then, the information entropy of each signal quality evaluation index within the sliding window is calculated. Based on the dynamics of information entropy, and following the principle that the lower the information entropy, the higher the stability of the indicator, and the higher the weight assigned, the dynamic weight of each signal quality evaluation indicator is calculated. The GNSS signal quality assessment value and UWB signal quality assessment value for the current time are calculated by weighting and summing the dynamic weights and normalized index values.
[0033] Specifically, the information entropy of each normalized index sequence within the sliding window is calculated to quantify its uncertainty. For index i, its normalized value forms a discrete probability distribution over the history of window L frames. Information entropy The calculation formula is as follows: in, It represents the probability that the normalized value of index i falls within the j-th equal partition (this can be obtained through histogram statistics), where M is the number of partitions. Entropy value. The larger the value, the more volatile the indicator has been recently, and the worse its stability.
[0034] Based on the entropy calculation results, the system assigns dynamic weights to each indicator. The core principle followed is: the more stable an indicator's performance is within a recent window (the lower its entropy value), the more reliable the information it contains, and the higher its weight should be in the overall score. Dynamic Weights The calculation formula is as follows: Where N is the total number of indicators involved in the evaluation. The formula first calculates the stability of each indicator. Then, normalization is performed to ensure that the sum of all weights is 1.
[0035] When dynamically weighting these indicators, the time-varying entropy method can sensitively capture the fluctuations of each indicator within the recent window through information entropy calculation, enabling GNSS signal quality scoring to more accurately characterize the comprehensive uncertainty of the signal environment, rather than just instantaneous intensity.
[0036] Finally, the system generates a real-time signal quality score. The overall quality score of the GNSS signal and the quality assessment value of the UWB signal are derived from the following formula: The score is a weighted sum of the current normalized values of each indicator and their dynamic weights, with the result strictly constrained within the range of [0,1]. The closer the score is to 1, the better the signal quality. The entire process, from the enqueueing of new data to the output of the updated score, is completed within milliseconds, achieving adaptive, quantitative, and real-time evaluation of signal quality with each incoming frame of data. This dynamic scoring mechanism provides a quantitative environmental awareness basis for subsequent intelligent decision-making and smooth switching.
[0037] Step S3: Based on the GNSS signal quality assessment value and the UWB signal quality assessment value, calculate the comprehensive confidence level that characterizes the credibility of GNSS observations through a fuzzy logic controller.
[0038] The fuzzy logic controller has a dual-input, single-output structure, with the input being the GNSS signal quality score. UWB signal quality assessment value The output is the overall confidence level β.
[0039] Figure 3 This is a graph of the membership function of a fuzzy controller. Figure 4 For the measurement noise covariance matrix Adaptive adjustment curve. This graph illustrates the working principle of the fuzzy controller and shows the mapping relationship between signal quality and measurement noise covariance matrix adjustment.
[0040] The overall quality score of the GNSS signal and the quality assessment value of the UWB signal both have a universe of discourse of [0, 1]. The output variable is the overall confidence level β of the GNSS observation data, which also has a universe of discourse of [0, 1]. A higher value indicates a higher level of confidence in the current GNSS signal by the system.
[0041] The process of calculating the comprehensive confidence level, which characterizes the credibility of GNSS observations, using a fuzzy logic controller includes: The GNSS signal quality assessment value and the UWB signal quality assessment value are input into the fuzzy logic controller. The membership degree of each fuzzy language subset corresponding to the GNSS signal quality assessment value and the UWB signal quality assessment value is calculated through the preset membership function. Based on a pre-set fuzzy rule base, the Mamdani-type inference method is adopted to activate all matching fuzzy rules in the fuzzy rule base according to each membership degree. The activation strength of each rule is calculated by taking the smaller value, and the output conclusions of all activated rules are synthesized by taking the larger value to obtain an aggregated fuzzy set about the comprehensive confidence. The centroid method is used to defuzzify the aggregated fuzzy set, and the centroid of the fuzzy set is calculated to obtain the comprehensive confidence level.
[0042] Specifically, to process these precise input values, the system defines a fuzzy linguistic subset for each variable. The linguistic values of the input GNSS and UWB can be divided into {Poor (P), Medium (M), Good (G)}, and the linguistic values of the output β can be divided into {Low (L), Medium (M), High (H)}. Each linguistic value corresponds to a triangular membership function. After obtaining the precise input score, the system calculates the membership degree of the value to each fuzzy subset using the membership function, completing the mapping from precise values to fuzzy concepts.
[0043] The pre-defined fuzzy rule base uses the rule format "IF premise, then conclusion". For example, a typical rule is: "IF GNSS is G (good) AND UWB is G (good), THEN β is H (high)". The system employs a Mamdani-type inference method, activating all matching fuzzy rules based on the membership degree of the current input value. It then calculates the activation strength of each rule using a "smaller" operation, and finally synthesizes the output conclusions of all activated rules using a "larger" operation, ultimately obtaining a fuzzy set regarding the confidence level β.
[0044] The specific formula for calculating the overall confidence level is as follows: in, To output the universe of discourse The j-th discrete point, The total number of discrete points. This represents the membership value of the j-th discrete point after aggregation. Through this calculation, a precise overall confidence score within the interval [0, 1] is finally obtained. .
[0045] The design of the fuzzy rule base of the fuzzy logic controller is related to the uncertainty of the index calculated by the time-varying entropy method. This enables the output confidence β to be affected by fuzzy reasoning when the information entropy of the key index changes abruptly, thereby achieving a feedforward fast response.
[0046] Step S4: Using a nonlinear mapping function, the measurement noise covariance matrix is adaptively adjusted according to the comprehensive confidence level β.
[0047] Writing the overall confidence β at time t Step S4 specifically involves integrating the confidence level. As a dynamic adjustment variable, the amplitude of the GNSS channel in the measurement noise covariance matrix is controlled by an exponential nonlinear mapping function. Its expression is: in, Let be the reference noise covariance matrix of the GNSS signal under ideal conditions. An adjustment coefficient greater than 0. For example... Figure 4 As shown, the function is designed so that when the signal quality is good and the confidence level is high... When it approaches 1, Approaching the reference value, the filter fully trusts the GNSS observations; when the signal quality is poor and the confidence level is low... During descent, As the number of observations increases exponentially, the filter automatically reduces the trust weight of the current GNSS observation.
[0048] Step S5: The current system status is determined by comparing the GNSS signal quality assessment value with a preset threshold, and the EKF measurement is updated and the positioning is calculated based on the current system status and IMU motion status data to obtain the current positioning.
[0049] Repeat steps S1-S5 above to achieve a smooth handover of the positioning trajectory.
[0050] The system states include outdoor confidence state, indoor confidence state, and transition buffer state.
[0051] Specifically, in this embodiment, a three-level state machine comprising an outdoor confidence state, an indoor confidence state, and a transition buffer state is used to implement the comprehensive confidence level. Triggered state switching.
[0052] Figure 5 The diagram shows the transition logic of a three-level state machine, illustrating how the system switches between outdoor, indoor, and transitional states, and explains the conditions and logic for state switching.
[0053] The three-level state machine presets a pair of mutually hysteretic outdoor handover thresholds. Indoor / outdoor switching threshold By adjusting the coefficient With transition interval width Cooperative matching, limiting the transition buffer state The exponential growth slope is used to ensure a smooth and continuous decay of GNSS observation weights during the time the carrier traverses the transition zone.
[0054] Preset thresholds include outdoor handover thresholds. Indoor / outdoor switching threshold When the GNSS signal quality assessment value When the value exceeds the outdoor handover threshold, the current system is in an outdoor confidence state. (The GNSS signal quality assessment value is also mentioned.) When the signal quality assessment value is less than the indoor handover threshold, the current system is in an indoor certainty state. When the system is greater than or equal to the indoor handover threshold and less than or equal to the outdoor handover threshold, the current system is in a transition buffer state.
[0055] The process of updating EKF measurements and calculating positioning based on the current system status includes: If the current system is in an outdoor certainty state, then based on the measurement noise covariance matrix, GNSS observation data and IMU motion state data, calculate the Kalman gain, update the state estimate and covariance estimate, and complete the GNSS single-source positioning solution; If the current system is in an indoor certain state, then based on the measurement noise covariance matrix, UWB ranging value and IMU motion state data, calculate the Kalman gain, update the state estimate and covariance estimate, and complete the UWB single-source positioning solution. If the current system is in a transition buffer state, an asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and the measurement noise covariance matrix corresponding to the UWB observations. A multi-source observation joint update strategy is then executed to perform multi-source fusion positioning solution.
[0056] The process of implementing a multi-source observation joint update strategy and performing multi-source fusion localization solution includes: A joint measurement vector is constructed based on the collected GNSS observation data and UWB ranging values. ; An asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and the measurement noise covariance matrix corresponding to UWB observations. ; Based on the joint measurement vector, a block measurement matrix adapted to it is constructed; Based on the joint measurement vector, asymmetric block diagonal matrix, block measurement matrix, and IMU motion state data, the Kalman gain is calculated, the state estimate and covariance estimate are updated, and the multi-source fusion localization solution is completed.
[0057] Specifically, the formula used is as follows: Calculate the Kalman gain: in, The Kalman gain matrix at time k is used to assign confidence weights between predicted and observed values, enabling dynamic weight allocation between GNSS and UWB observations. Let be the prior state estimation covariance matrix at time k, which is obtained by predicting the posterior covariance from the previous time step using the state equation. This is the measurement matrix (observation matrix) at time k, used to map the state space to the observation space. In the transition buffer state, it is in block form. , Let be the measurement noise covariance matrix at time k, and let be the outdoor certainty state time. The indoor certainty state is fixed. The transition buffer state is an asymmetric block diagonal matrix. .
[0058] Updated state estimate: in, Let be the posterior state estimate at time k, which is the final positioning result output by the system. The prior state estimate at time k is obtained from IMU motion data through state equation prediction. Let K be the Kalman gain matrix at time k. Let be the measurement observation vector at time k, and let be the outdoor certainty state. The indoor state of certainty is The transition buffer state is the joint measurement vector. .
[0059] Update covariance estimate: in, This is the posterior state estimation covariance matrix at time k, used to quantify the uncertainty of the current state estimation, record the reliability of the positioning results, and provide a basis for filtering calculations at the next time step. It is an identity matrix with the same dimensions as the system state vector. Let be the prior state estimation covariance matrix at time k.
[0060] Specifically, in the transition buffer state, the system employs a joint observation update strategy. This is implemented by constructing a joint measurement vector. : in, and These represent the raw observation data from GNSS and UWB, respectively. Corresponding asymmetric block diagonal matrices are also constructed. It adopts a diagonal matrix form: In this design, It is an adaptive matrix that is dynamically adjusted based on real-time comprehensive confidence levels, and This is the fixed noise covariance matrix corresponding to the UWB observations. This block diagonal structure ensures the independence of the two types of observations in the filter update, while also... The adaptive changes enable a smooth transition of weights.
[0061] When a user moves from outdoors to the indoor doorway, the system's workflow is as follows: Initially in a confident outdoor state, primarily relying on GNSS positioning. As the user approaches the doorway, the GNSS signal begins to be obstructed, and the quality score... The decrease in confidence level β leads to a decrease in confidence level β. Increase. When When the system descends to the preset transition range, it automatically enters the transition buffer state.
[0062] In the transition state, the system initiates a joint update, processing both GNSS and UWB observations simultaneously. Because... The weight of GNSS observations in filtering has increased significantly, automatically decreasing while the weight of UWB observations has relatively increased. As users continue to move indoors, GNSS signal quality deteriorates further, and UWB gradually becomes the dominant positioning source. When GNSS signal quality remains below the indoor handover threshold, the system smoothly transitions to an indoor certainty state, fully switching to UWB positioning.
[0063] The entire switching process is fully automated, with state transitions based on real-time signal quality assessment. This design ensures that the system outputs a continuous and smooth positioning trajectory in environmental boundary areas, without jumps or interruptions, achieving a truly seamless indoor and outdoor positioning experience.
[0064] Figure 6 The trajectory comparison diagram shows the trajectory comparison between the method of this invention and the traditional method in indoor and outdoor switching scenarios, highlighting the smooth transition effect.
[0065] Example 2 This embodiment, based on Embodiment 1 above, discloses an indoor / outdoor seamless positioning system based on dynamic entropy fuzzy evaluation and adaptive filtering. The system includes a data acquisition module, a dynamic entropy evaluation module, a fuzzy logic control module, and an adaptive fusion positioning module. The system constructs a hierarchical hardware and software collaborative architecture, realizing the entire process from signal acquisition to location calculation through precise timing synchronization and data fusion.
[0066] Data acquisition module: It collects and caches GNSS observation data, UWB ranging values and IMU motion status data in real time through a sliding window, and transmits the processed standardized data packets to other modules in real time through a high-speed serial bus.
[0067] Specifically, the system initializes a fixed-length First-In-First-Out (FIFO) queue of length L in memory as a sliding time window. The length L can be set according to the actual application scenario and system response requirements; for example, it can be set to L = 50 frames. This design ensures the real-time nature of the system analysis, enabling it to reflect recent signal changes. Whenever a new set of data frames containing GNSS and UWB observations arrives, the system performs standard operations: inserting the new frame data at the tail of the queue while removing the oldest frame data from the head of the queue. This mechanism ensures that the analysis window always contains the latest L frames of data, allowing the evaluation results to respond instantly to dynamic changes in the signal environment.
[0068] Dynamic entropy evaluation module: Based on GNSS observation data and UWB ranging values, the time-varying entropy method is used to dynamically assign weights to multiple signal quality evaluation indicators, and calculate the environmentally adaptive GNSS signal quality evaluation value and UWB signal quality evaluation value at the current moment.
[0069] Fuzzy logic control module: Based on the GNSS signal quality assessment value and the UWB signal quality assessment value, the fuzzy logic controller calculates the comprehensive confidence level that characterizes the credibility of GNSS observations.
[0070] Adaptive fusion positioning module: Utilizes a nonlinear mapping function to adaptively adjust the measurement noise covariance matrix based on the comprehensive confidence level; determines the current system state by comparing the GNSS signal quality assessment value with a preset threshold, and performs EKF measurement updates and positioning calculations based on the current system state and IMU motion state data to obtain the current positioning; repeats data acquisition and positioning calculations to achieve smooth handover of positioning trajectories.
[0071] For details regarding the above modules, please refer to the relevant descriptions and effects in Example 1 for further understanding.
[0072] In the data acquisition section, the system integrates a set of high-precision sensor modules as the foundation for environmental perception. Specifically, this includes a multi-frequency GNSS receiver for acquiring satellite navigation signals outdoors and in open areas, providing raw observation data including pseudorange, carrier phase, and carrier-to-noise ratio; an ultra-wideband positioning unit consisting of a base station and a mobile tag, responsible for providing accurate ranging information indoors or in environments where satellite signals are obstructed; and a nine-axis inertial measurement unit that continuously provides three-axis acceleration, three-axis angular velocity, and three-axis magnetic field data during the vehicle's motion to support dead reckoning. All sensors are synchronized through a precise time synchronization mechanism to ensure the spatiotemporal consistency of multi-source data.
[0073] Data processing is handled by an embedded edge computing platform. This embodiment can utilize an embedded motherboard based on a high-performance ARM Cortex-M series microcontroller (such as the STM32H7 series), or an industrial computer with greater computing power. This platform is the core of the system's computation, responsible for running the entire positioning fusion algorithm. It receives and preprocesses raw data streams from various sensors in real time through various communication interfaces, performing complex computational tasks including dynamic signal quality assessment, fuzzy logic control, extended Kalman filter fusion, and state machine management.
[0074] The core idea and final decision of the entire system are embodied at the software algorithm level. The system performs real-time diagnosis of environmental signals through a dynamic entropy evaluation module, quantifies the diagnostic results into confidence parameters through a fuzzy logic controller, and uses these parameters to dynamically adjust the internal parameters of the fusion filter. Finally, a three-level state machine is used to achieve seamless and smooth switching of the positioning strategy. This system transforms multi-sensor data into stable, continuous, and high-precision location information output, providing reliable support for various location-based services.
[0075] Example 3 Based on Embodiment 1, this embodiment provides an electronic device, including: one or more processors and a memory, wherein the memory stores one or more programs, the one or more programs including instructions for executing the aforementioned indoor and outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering.
[0076] At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to implement the aforementioned seamless indoor and outdoor positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering. Of course, in addition to software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.
[0077] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0078] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0079] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.
Claims
1. A seamless indoor / outdoor positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering, characterized in that, The method includes: GNSS observation data, UWB ranging values, and IMU motion status data are collected and cached in real time over a continuous period of time through a sliding window; Based on the GNSS observation data and UWB ranging values, the time-varying entropy method is used to dynamically assign weights to multiple signal quality evaluation indicators, and to calculate the GNSS signal quality evaluation value and UWB signal quality evaluation value that are environmentally adaptive at the current moment. Based on the GNSS signal quality assessment value and the UWB signal quality assessment value, a comprehensive confidence level characterizing the credibility of GNSS observations is calculated using a fuzzy logic controller. The measurement noise covariance matrix is adaptively adjusted based on the comprehensive confidence level using a nonlinear mapping function. The current system state is determined by comparing the GNSS signal quality assessment value with a preset threshold. Based on the current system state and IMU motion state data, EKF measurement updates and positioning calculations are performed to obtain the current positioning. Repeated data collection and positioning calculations are then performed to achieve smooth handover of the positioning trajectory. The system states include outdoor certainty state, indoor certainty state, and transition buffer state. If the current system is in the transition buffer state, an asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and measurement noise covariance matrix corresponding to the UWB observation, and a multi-source observation joint update strategy is executed to achieve smooth handover of positioning trajectories.
2. The indoor and outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering according to claim 1, characterized in that, The sliding window adopts a first-in-first-out queue mechanism, and the window acquisition settings are matched with the system state judgment switching frequency.
3. The indoor and outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering according to claim 1, characterized in that, The signal quality evaluation indicators for the GNSS observation data include at least the number of visible satellites, the mean carrier-to-noise ratio of each satellite, the mean variance of the carrier-to-noise ratio of each satellite, and the position accuracy factor. The signal quality evaluation indicators for the UWB ranging values include at least the number of effective base stations, the mean RSSI, the RSSI variance, and the variance of the TOF ranging values.
4. The indoor and outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering according to claim 1, characterized in that, The process of dynamically weighting multiple signal quality evaluation indicators using the time-varying entropy method includes: The numerical sequences of each signal quality evaluation index within the sliding window are normalized, and their discrete probability distributions are statistically calculated based on the normalized data. Then, the information entropy of each signal quality evaluation index within the sliding window is calculated. Based on the aforementioned information entropy dynamics, and following the principle that the lower the information entropy, the higher the stability of the indicator, and the higher the weight assigned, the dynamic weight of each signal quality evaluation indicator is calculated. The GNSS signal quality assessment value and UWB signal quality assessment value for the current time are calculated by weighting and summing the dynamic weights and normalized index values.
5. The indoor and outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering according to claim 1, characterized in that, The fuzzy logic controller has a dual-input, single-output structure. The process of calculating the comprehensive confidence level, which characterizes the reliability of GNSS observations, through the fuzzy logic controller includes: The GNSS signal quality assessment value and the UWB signal quality assessment value are input into the fuzzy logic controller. The membership degree of each fuzzy language subset corresponding to the GNSS signal quality assessment value and the UWB signal quality assessment value is calculated through a preset membership function. Based on a preset fuzzy rule base, the Mamdani-type inference method is used to activate all matching fuzzy rules in the fuzzy rule base according to each membership degree. The activation strength of each rule is calculated by taking the smaller value, and the output conclusions of all activated rules are synthesized by taking the larger value to obtain an aggregated fuzzy set about the comprehensive confidence. The aggregated fuzzy set is defuzzified using the centroid method, and the centroid of the fuzzy set is calculated to obtain the comprehensive confidence level.
6. The indoor and outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering according to claim 1, characterized in that, The preset threshold includes the outdoor switching threshold. Indoor / outdoor switching threshold When the GNSS signal quality assessment value is greater than the outdoor handover threshold, the current system is in the outdoor sure state. When the GNSS signal quality assessment value is less than the indoor handover threshold, the current system is in the indoor sure state. When the GNSS signal quality assessment value is greater than or equal to the indoor handover threshold and less than or equal to the outdoor handover threshold, the current system is in the transition buffer state.
7. The indoor and outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering according to claim 1, characterized in that, The process of updating EKF measurements and calculating positioning based on the current system status includes: If the current system is in an outdoor certainty state, then based on the measured noise covariance matrix, GNSS observation data and IMU motion state data, calculate the Kalman gain, update the state estimate and covariance estimate, and complete the GNSS single-source positioning solution; If the current system is in an indoor certain state, then based on the measured noise covariance matrix, UWB ranging value and IMU motion state data, calculate the Kalman gain, update the state estimate and covariance estimate, and complete the UWB single-source positioning solution. If the current system is in a transition buffer state, an asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and the measurement noise covariance matrix corresponding to the UWB observations. A multi-source observation joint update strategy is then executed to perform multi-source fusion positioning solution.
8. The seamless indoor and outdoor positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering according to claim 7, characterized in that, The process of implementing the multi-source observation joint update strategy and performing multi-source fusion positioning calculation includes: A joint measurement vector is constructed based on the collected GNSS observation data and UWB ranging values; An asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and the measurement noise covariance matrix corresponding to UWB observations. Based on the joint measurement vector, a block measurement matrix adapted to it is constructed; Based on the joint measurement vector, asymmetric block diagonal matrix, block measurement matrix, and IMU motion state data, the Kalman gain is calculated, the state estimate and covariance estimate are updated, and the multi-source fusion positioning solution is completed.
9. A seamless indoor and outdoor positioning system based on dynamic entropy value fuzzy evaluation and adaptive filtering, characterized in that, The system includes: Data acquisition module: It collects and caches GNSS observation data, UWB ranging values and IMU motion status data in real time through a sliding window, and transmits the processed standardized data packets to other modules in real time through a high-speed serial bus. Dynamic entropy evaluation module: Based on the GNSS observation data and UWB ranging values, the time-varying entropy method is used to dynamically assign weights to multiple signal quality evaluation indicators, and calculate the environmental adaptive GNSS signal quality evaluation value and UWB signal quality evaluation value at the current moment; Fuzzy logic control module: Based on the GNSS signal quality assessment value and the UWB signal quality assessment value, the fuzzy logic controller calculates the comprehensive confidence level that characterizes the credibility of GNSS observations; The adaptive fusion positioning module: utilizes a nonlinear mapping function to adaptively adjust the measurement noise covariance matrix based on the comprehensive confidence level; determines the current system state by comparing the GNSS signal quality assessment value with a preset threshold, and performs EKF measurement updates and positioning calculations based on the current system state and IMU motion state data to obtain the current positioning; repeats data acquisition and positioning calculations to achieve smooth handover of positioning trajectories; wherein, The system states include outdoor certainty state, indoor certainty state, and transition buffer state. If the current system is in the transition buffer state, an asymmetric block diagonal matrix is constructed based on the fixed noise covariance matrix and measurement noise covariance matrix corresponding to the UWB observation, and a multi-source observation joint update strategy is executed to achieve smooth handover of positioning trajectories.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the indoor and outdoor seamless positioning method based on dynamic entropy value fuzzy evaluation and adaptive filtering as described in any one of claims 1-8.