Real-time dynamic high-precision positioning method based on Beidou and UWB tight coupling

Abstract

The invention relates to the cross technical field of satellite navigation and wireless positioning technology, in particular to a real-time dynamic high-precision positioning method based on Beidou and UWB tight coupling, which solves the problem that in the prior art, error coupling in Beidou and UWB observation data is not fully solved, and especially in a high-speed dynamic scene, the positioning accuracy is greatly improved. The mutual influence of the Beidou receiver clock error, the UWB base station clock error and the multi-path error causes the continuous accumulation of the error and the significant reduction of the positioning precision. The invention discloses a real-time dynamic high-precision positioning method based on Beidou and UWB tight coupling. A data preprocessing and space-time alignment module, an error joint estimation model, an ambiguity fast fixing algorithm, a geometric constraint enhanced positioning engine module and a dynamic coordinate conversion and fusion output module are included. According to the mode provided by the invention, a highly integrated multi-model Kalman filtering architecture is adopted, so that synchronous and accurate compensation of the clock error of the Beidou receiver, the time delay of the UWB base station and the multi-path effect is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation and wireless positioning technology, and particularly relates to a real-time dynamic high-precision positioning method based on tight coupling of Beidou and UWB. BACKGROUND

[0002] In today's positioning technology field, the tight coupling positioning method of Beidou satellite navigation system (BDS) and ultra-wideband (UWB) technology occupies an important position due to its unique advantages. As a key link to achieve high-precision, real-time dynamic positioning, the tight coupling technology of Beidou and UWB aims to provide more accurate and reliable positioning services by integrating the advantages of two different positioning technologies. Especially in dynamic scenarios that require high-precision positioning, such as autonomous driving, intelligent logistics, indoor navigation, etc., this tight coupling technology has a wide application prospect.

[0003] However, in the existing tight coupling positioning method of Beidou and UWB, a series of obvious limitations and technical problems have gradually emerged. Specifically, the existing technology has not effectively solved the coupling problem of clock bias and multipath error in Beidou and UWB observation data. In high-speed dynamic scenarios, due to the accumulation of clock bias and multipath error, the positioning accuracy is significantly reduced, making it difficult to meet the demand for high-precision positioning.

[0004] In addition, the traditional LAMBDA algorithm does not fully utilize the geometric constraints of UWB ranging when ambiguity is fixed. This makes the ambiguity search space large and the computational complexity high, resulting in a long fixing time and insufficient success rate. In real-time positioning application scenarios, this long fixing process and low success rate seriously restrict the real-time performance of the positioning technology.

[0005] When the number of Beidou signals is insufficient due to obstruction, the existing tight coupling method cannot effectively supplement the geometric constraints through UWB ranging. This leads to positioning results prone to jumping and insufficient system robustness. In complex indoor and outdoor environments, this jumping and insufficient robustness problem seriously affects the reliability and stability of the positioning technology.

[0006] At the same time, the method of switching between indoor and outdoor environments relying on signal strength threshold has its shortcomings. Since signal strength is affected by many factors, relying solely on signal strength threshold for judgment can easily lead to misjudgment and inaccurate positioning. In addition, there is a lack of real-time dynamic conversion model between the Beidou Earth-Centered Earth-Fixed (ECEF) coordinate system and the UWB local coordinate system, resulting in a non-smooth positioning trajectory in the transition area with an error of up to decimeters. This non-smoothness and large error seriously affect the continuity and accuracy of the positioning technology. Therefore, in view of the many shortcomings in the existing technology, we urgently need an innovative real-time dynamic high-precision positioning method based on tight coupling of Beidou and UWB to solve these problems. SUMMARY

[0007] The application aims to provide a real-time dynamic high-precision positioning method based on tight coupling of Beidou and UWB, solve the problem that the error coupling in Beidou and UWB observation data is not fully solved in the prior art, especially in a high-speed dynamic scene, the mutual influence of Beidou receiver clock error, UWB base station clock error and multipath error leads to continuous accumulation of error, and the positioning precision is significantly reduced, at the same time, the traditional ambiguity fixing method does not effectively utilize the geometric constraint of UWB ranging, the search space is large, the calculation time is long, and it is difficult to meet the real-time requirement.

[0008] In order to achieve the above-mentioned purpose, the application adopts the following technical scheme:

[0009] A real-time dynamic high-precision positioning method based on tight coupling of Beidou and UWB, comprising a data preprocessing and space-time alignment module, an error joint estimation model, an ambiguity fast fixing algorithm, a geometric constraint enhanced positioning engine module, a dynamic coordinate conversion and fusion output module, in the Beidou and UWB tight coupling positioning method, first, data preprocessing and space-time alignment are performed, the timestamps of Beidou original data and UWB ranging data are aligned through a hardware PPS signal, and linear interpolation is adopted to compensate for sub-millisecond deviation, so as to ensure that the time references of multiple sources are consistent, at the same time, Beidou positioning results and UWB base station coordinates are collected in an open area, an initial coordinate conversion matrix is calculated through the least square method, the accuracy is controlled within 2cm, and based on the 3σ criterion, abnormal values are removed to provide high-quality input for deep fusion, then, an error joint estimation model is constructed, Beidou clock error, UWB clock error and multipath error are estimated and compensated in real time by using an adaptive Kalman filter algorithm, the estimation accuracy is significantly improved, the error coupling in a dynamic environment is inhibited, in the ambiguity fixing aspect, the LAMBDA algorithm is improved, a block search strategy and a Ratio test mechanism are adopted to fix the ambiguity quickly, the efficiency bottleneck is broken through, the real-time requirement in a high dynamic scene is met, the geometric constraint enhanced positioning engine introduces the UWB ranging equation as a supplementary constraint when the number of satellites is small, jointly solves the position increment, adopts singular value decomposition to process the rank-deficient equation, designs a residual feedback mechanism to inhibit the influence of multipath and non-line-of-sight error, and maintains the continuity of positioning, finally, dynamic coordinate conversion and fusion output, dynamically allocate weights in combination with the number of satellites and UWB signal quality, smooth transition in indoor and outdoor transition areas through weighted fusion results, switch error control within centimeter level, trajectory smoothness is improved, and seamless positioning in all scenes is realized.

[0010] Preferably, data preprocessing and space-time alignment, Beidou raw data, including pseudo-range p and carrier phase f, and UWB ranging data d, first need to be timestamped by hardware PPS signal, to ensure the consistency of Beidou and UWB data in time, which provides the basis for subsequent data fusion, because the clock of the hardware device may have a sub-millisecond deviation, linear interpolation method is used to compensate the data to eliminate the subtle time difference, in the open area, collect Beidou positioning results and UWB base station coordinates X UWB , using these data, the initial coordinate conversion matrix R and T are calculated by least squares method, this conversion matrix is used to convert the local coordinates of UWB to the ECEF coordinate system of Beidou, so as to realize the space alignment of two kinds of positioning data, the calibration accuracy is controlled to be ≤2cm, in addition, in order to eliminate the influence of abnormal data on subsequent calculation, the sliding window statistics are carried out on the Beidou pseudo-range and UWB ranging data respectively, and the abnormal values are removed based on 3σ criterion, which effectively avoids the interference of outliers on the positioning results, and provides high-quality input data for deep fusion, wherein the least squares method calculates the initial coordinate conversion matrix R, T as:

[0011]

[0012] Preferably, the error joint estimation model, in order to accurately estimate and compensate the Beidou clock error b GNSS , UWB clock error b UWB and multipath error e MP , the state equation is constructed:

[0013] x k+1 =Fx k +w k

[0014] z k =Hx k +v k

[0015] Wherein, x=[b GNSS ,b UWB ,e MP ] T represents the state vector, F is the state transition matrix, which describes the change rule of the state with time; H is the observation matrix, which maps the state vector to the observation space; w and v are process noise and measurement noise respectively, which reflect the uncertainty of the system and the observation error; the adaptive Kalman filter algorithm is used to iteratively update the Kalman filter gain matrix, to estimate and compensate the clock error and multipath error in real time, by continuously adjusting the filter parameters, the algorithm can adapt to the noise characteristics in different environments, improve the estimation accuracy, the Beidou clock error estimation accuracy can reach nanosecond level, which significantly suppresses the error coupling phenomenon in dynamic environment and improves the long-term stability of the system.

[0016] The preferred ambiguity fast fixing algorithm compresses the integer ambiguity search dimension from 6D to 3D or below by providing geometric constraints from the UWB ranging equation, improving the LAMBDA algorithm:

[0017]

[0018] where Q is the ambiguity covariance matrix, reflecting the correlation between ambiguities; a * The floating-point ambiguity solution is the starting point for ambiguity search, and the block search strategy and Ratio test mechanism are used to prioritize the selection of the optimal ambiguity combination. The search space can be divided into multiple small blocks for search, thereby improving the search efficiency. Meanwhile, the Ratio test mechanism is used to evaluate the reliability of different ambiguity combinations to ensure that the selected ambiguity combination is optimal, breaking the bottleneck of ambiguity fixing efficiency.

[0019] The preferred geometric constraint enhanced positioning engine uses the Beidou observation equation to solve the position when the number of satellites N sat ≥4.

[0020] ρ i =‖X-X i ‖+cb GNSS +e MP,i

[0021] N sat =2 or 3, the UWB ranging equation is introduced as a supplementary constraint to jointly solve the position increment:

[0022] d j =‖X-X j ‖

[0023] During the solving process, singular value decomposition is used to handle rank-deficient equations, avoiding the problem of matrix inversion failure. At the same time, there is a feedback mechanism that dynamically adjusts the observation weight according to the observation residual to suppress the influence of multipath and non-line-of-sight errors on the positioning result. This mechanism can maintain the continuity of positioning in occluded scenarios.

[0024] The preferred dynamic coordinate conversion and fusion output module estimates the conversion parameters of the two coordinate systems in real time to achieve seamless fusion of Beidou and UWB positioning results. It dynamically allocates weights based on the number of satellites and UWB signal quality to ensure accurate positioning results in different environments. When the number of satellites decreases, the weight of UWB gradually increases from 20% to 80% to fully utilize the positioning information of UWB. In the indoor-outdoor transition area, we achieve smooth transition by weighted fusion of results, avoiding the problem of trajectory jumping caused by relying on fixed thresholds. The switching error is controlled within centimeters, and the trajectory smoothness is significantly improved.

[0025] The present invention has at least the following beneficial effects:

[0026] In response to the problem of multi-source error coupling in complex environments, the present invention adopts a highly integrated multi-model Kalman filter architecture to achieve synchronous and accurate compensation of Beidou receiver clock errors, UWB base station delays and multipath effects. This architecture not only takes into account the mutual influence between the various error sources, but also establishes a detailed error transfer model in the time-frequency domain by constructing an advanced error joint estimation module. The model can dynamically decouple the various error terms, effectively avoiding the positioning drift phenomenon caused by error accumulation in traditional solutions. Especially in highly reflective environments, such as challenging scenarios such as urban canyons and tunnels, the system's anti-interference ability is significantly enhanced by introducing a time-varying noise covariance adjustment mechanism. This mechanism can adjust the filter parameters in real time according to environmental changes, ensuring that the positioning system can still maintain stable centimeter-level positioning performance in dynamic scenarios, greatly improving the accuracy and reliability of positioning.

[0027] The present invention has made an innovative fusion in the ambiguity search algorithm, and cleverly embedded the geometric constraints of UWB ranging into it. By establishing a dimensional compression model, the parameter search space is reconstructed, which greatly reduces the computational complexity of the integer least squares problem. This improvement makes the algorithm more efficient when processing large-scale data. At the same time, it is also combined with an improved Ratio test mechanism, and through a parallel processing strategy, the ambiguity parameters are quickly fixed, which not only meets the stringent requirements of high-speed mobile scenarios on positioning update rate, but also ensures the real-time and accuracy of positioning. In addition, the hierarchical computing architecture designed in the algorithm processing unit has been optimized for hardware compatibility, so that these complex algorithms can be deployed on resource-constrained embedded platforms, broadening the application scope of the system.

[0028] The present invention also has at least the following beneficial effects:

[0029] In order to achieve seamless conversion between Beidou and UWB coordinate systems, a dynamic calibration system is constructed based on the extended Kalman filter framework. The present invention uses online parameter identification technology to adjust the conversion parameters between coordinate systems in real time, ensuring the fusion effect of the two positioning technologies. In the fusion module, an adaptive weighting strategy is adopted to dynamically construct a weight function based on the satellite visibility index and the quality parameters of the UWB signal. This strategy breaks through the performance bottleneck of the traditional fixed threshold fusion method when the environment changes suddenly, allowing the system to respond to various environmental changes more flexibly. The specially designed signal quality evaluation circuit provides the system with accurate environmental status perception capabilities through multi-dimensional feature extraction technology, which enables the system to maintain a continuous and smooth positioning trajectory in the transition area between indoors and outdoors, improving the user's positioning experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0031] Figure 1 The overall flowchart of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present application more clear, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0033] Embodiment one

[0034] Reference Figure 1 , including data preprocessing and space-time alignment module, error joint estimation model, ambiguity fast fixing algorithm, geometric constraint enhanced positioning engine module, dynamic coordinate conversion and fusion output module, in the tightly coupled positioning method of Beidou and UWB, first, data preprocessing and space-time alignment, through the hardware PPS signal to align the time stamp of Beidou original data and UWB ranging data, and adopt linear interpolation to compensate the sub-millisecond deviation, ensure the consistency of time reference of multi-source data, at the same time, collect Beidou positioning results and UWB base station coordinates in open area, calculate the initial coordinate conversion matrix by least square method, the accuracy is controlled within 2cm, and based on 3σ criterion, eliminate outliers, provide high quality input for deep fusion, then, construct error joint estimation model, use adaptive Kalman filter algorithm to estimate and compensate Beidou clock error, UWB clock error and multipath error in real time, significantly improve the estimation accuracy, suppress error coupling in dynamic environment, in the aspect of ambiguity fixing, improve LAMBDA algorithm, adopt block search strategy and Ratio test mechanism, fast fix ambiguity, break through the efficiency bottleneck, meet the real-time demand of high dynamic scene, geometric constraint enhanced positioning engine, when the number of satellites is small, introduce UWB ranging equation as supplementary constraint, solve the position increment jointly, and adopt singular value decomposition to process rank defect equation, design residual feedback mechanism to suppress multipath and non-line-of-sight error influence, maintain the continuity of positioning, finally, dynamic coordinate conversion and fusion output, combine the number of satellites, UWB signal quality to dynamically allocate weight, through weighted fusion result to smooth transition in indoor and outdoor transition area, the switching error is controlled within centimeter, the trajectory smoothness is improved, realize seamless positioning in all scenes.

[0035] Firstly, the data preprocessing and space-time alignment module ensures that the Beidou raw data and the UWB ranging data have consistent time reference, and compensates for sub-millisecond level deviation through linear interpolation; secondly, the initial coordinate conversion matrix is calculated by least squares method using data collected in open areas, and the calibration accuracy is controlled within 2 cm, and abnormal values are removed based on the 3σ criterion; then, the error joint estimation model is constructed, and various errors are estimated and compensated in real time by using adaptive Kalman filtering algorithm; then, the improved LAMBDA algorithm is used to quickly fix the ambiguity; the geometric constraint enhances the positioning engine when the number of satellites is small, and introduces the UWB ranging equation as a supplementary constraint; finally, the dynamic coordinate conversion and fusion output module combines the number of satellites and the quality of UWB signal to dynamically allocate weights, realizing seamless positioning in all scenarios. The positioning accuracy is improved, the error coupling in dynamic environment is suppressed, the real-time demand in high dynamic scene is met, the positioning continuity is maintained, and the seamless positioning in all scenarios is realized.

[0036] Embodiment two

[0037] Reference Figure 1 , data preprocessing and space-time alignment, Beidou raw data, including pseudo-range and carrier phase, and UWB ranging data, firstly, Beidou raw data includes pseudo-range ρ and carrier phase φ, and UWB raw data is ranging value d. In order to ensure that the two types of data have unified time reference when fused and calculated, the hardware PPS (Pulse Per Second) signal triggering mechanism is used to sample and align the time stamps of Beidou and UWB modules. Considering that there is a sub-millisecond level clock offset between the devices, linear interpolation method is further used to compensate and correct the data from different sources. Let the time stamp of Beidou data at a certain time be t BDS , and the time stamp of UWB data be t UW B When Δt = |t BDS -t UWB |≤1ms, the linear interpolation estimation compensation value d interp (t BDS ) is estimated and compensated based on the adjacent two UWB sampling points, so as to obtain the UWB ranging result strictly aligned with the Beidou data.

[0038] In order to realize the space alignment of the two types of positioning data, multiple UWB base station coordinates (local coordinate system) and corresponding time Beidou output coordinates (ECEF coordinate system) are collected synchronously in the open area of GNSS signal. Select n≥3 pairs of sample points to establish the following coordinate transformation model:

[0039]

[0040] Among them, R is the rotation matrix, which satisfies the orthogonal constraint R T R = I, and T is the translation vector ∈i The least square method is used for the residual term to derive the rotation matrix R and the translation vector T, and the specific steps are as follows:

[0041] 1. Calculate the centroid of the two groups of point sets:

[0042] 2. Center each point:

[0043] 3. Construct the covariance matrix:

[0044] 4. Singular value decomposition of H: H = UΣV T

[0045] 5. Calculate the rotation matrix: R = VU T

[0046] If det(R) < 0, take the negative of the last column of V to ensure that the rotation matrix satisfies the right-hand system constraint.

[0047] 6. Calculate the translation vector: Finally, the coordinate conversion relationship is obtained:

[0048] P BDS = R·P UWB + T

[0049] The method is verified on the measured data set, and the calibration accuracy is better than 2 cm, which meets the high-precision requirement of coordinate consistency for subsequent fusion positioning. In order to further improve the input data quality, the sliding window statistical method is introduced for Beidou pseudo-range data and UWB ranging data, and the window size is set to w. The mean value μ and the standard deviation σ are calculated in each window, and based on the 3σ criterion, the abnormal value is removed. If |x i - μ | > 3σ, then x i is an abnormal value and is removed. The data preprocessing and space-time alignment module ensures the time synchronization of Beidou and UWB data through hardware alignment and interpolation compensation, realizes spatial registration through the least square method based on coordinate conversion matrix calculation, and guarantees the fusion input quality through the abnormal value elimination mechanism, which provides a prerequisite for high-precision and stable operation of the system.

[0050] Example Three

[0051] Referring to Figure 1 , the error joint estimation model, in order to accurately estimate and compensate the Beidou clock error, the UWB clock error and the multipath error, the state equation is constructed:

[0052] x k = F k x k-1 + w k

[0053] zk = H k x k + v k

[0054] where xk= [b GNSS ,b UWB ,e MP ] T represents the BeiDou clock error, the UWB clock error and the multipath error; F k is the identity matrix (i.e. assuming that the error state changes slowly in a short time), H k is the identity observation matrix, w k ~ N(0, Q k ), v k ~ N(0, R k ) are the system noise and the observation noise respectively, reflecting the uncertainty of the system and the observation error, the Kalman filter gain matrix is iteratively updated by using the adaptive Kalman filter algorithm, the clock error and the multipath error are estimated and compensated in real time, by continuously adjusting the filter parameters, the algorithm can adapt to the noise characteristics in different environments, improve the estimation accuracy, the BeiDou clock error estimation accuracy can reach nanosecond level, significantly suppresses the error coupling phenomenon in dynamic environment, and improves the long-term stability of the system.

[0055] To realize the adaptive filtering capability, the filter parameters Q k , R k are dynamically adjusted, the residual covariance fitting method is used, i.e. according to the variance trend between the actual observation residual and the predicted residual, R k is adaptively updated:

[0056]

[0057] where e is the observation residual, a ∈ [0, 1] is the smoothing coefficient. Through the above mechanism, the adaptive ability of the filter to different time-varying errors is enhanced, and the clock error compensation accuracy is improved.

[0058] Firstly, the state equation is constructed to describe the change rule of the state with time and to map the state vector to the observation space; then, the Kalman filter gain matrix is iteratively updated by using the adaptive Kalman filter algorithm, the clock error and the multipath error are estimated and compensated in real time. The effects of accurately estimating and compensating various errors, adapting to the noise characteristics in different environments, improving the estimation accuracy, significantly suppressing the error coupling phenomenon in dynamic environment and improving the long-term stability of the system are achieved.

[0059] Embodiment Four

[0060] Referring to Figure 1, the UWB ranging equation provides position geometric constraints, which compresses the integer ambiguity search dimension from 6D to 3D or below, and improves the LAMBDA algorithm:

[0061]

[0062] where Q is the ambiguity covariance matrix, reflecting the correlation between ambiguities; a * is the float ambiguity solution, which is the starting point of ambiguity search, adopts the block search strategy and Ratio test mechanism, preferentially selects the optimal ambiguity combination, through block search, the search space can be divided into multiple small blocks for search, thereby improving the search efficiency, at the same time, the Ratio test mechanism is used to evaluate the reliability of different ambiguity combinations, to ensure that the selected ambiguity combination is optimal, and to break through the bottleneck of ambiguity fixing efficiency.

[0063] Firstly, the UWB ranging equation provides position geometric constraints, which compresses the integer ambiguity search dimension; then, the LAMBDA algorithm is improved, and the block search strategy and Ratio test mechanism are adopted to preferentially select the optimal ambiguity combination. The effects of improving the ambiguity search efficiency, evaluating the reliability of different ambiguity combinations, ensuring that the selected ambiguity combination is optimal, and breaking through the bottleneck of ambiguity fixing efficiency are achieved.

[0064] In the process of ambiguity fast fixing, in view of the problem of low search efficiency of the original LAMBDA algorithm, block search and Ratio test mechanism are introduced. Block search strategy: the search space of ambiguity solution is decomposed according to the correlation, and the high correlation dimension is searched preferentially, so as to reduce the dimension of each search space and reduce the amount of calculation. For example, the covariance matrix Q a of ambiguity vector a is decomposed by Cholesky or LDL, and the principal axis direction is extracted. Ratio test mechanism: for candidate integer ambiguity solution The discrimination ratio is defined as:

[0065]

[0066] When r<0.25, it is considered that the solution is unique and reliable, the ambiguity is fixed, otherwise it is considered as unsolvable. This mechanism effectively avoids the problem of false fixing and improves the overall stability.

[0067] Example five

[0068] Referring to Figure 1 , the geometric constraint enhances the positioning engine. When the number of satellites N sat ≥4, the Beidou observation equation is used to solve the position:

[0069] ρ i =‖X-X i ‖+cbGNSS +e MP,i

[0070] N sat = 2 or 3, the UWB ranging equation is introduced as a supplementary constraint to jointly solve the position increment:

[0071] d j = ‖X-X j ‖

[0072] In the solving process, singular value decomposition is used to handle the rank-deficient equation, avoiding the problem of matrix inversion failure, and at the same time having a feedback mechanism to dynamically adjust the observation weight according to the observation residual, which suppresses the influence of multipath and non-line-of-sight errors on the positioning result. This mechanism can maintain the continuity of positioning in the occlusion scene.

[0073] When the number of satellites N sat ≥ 4, the Beidou observation equation is used to solve the position; when N sat = 2 or 3, the UWB ranging equation is introduced as a supplementary constraint to jointly solve the position increment; in the solving process, singular value decomposition is used to handle the rank-deficient equation, and a residual feedback mechanism is designed. The effects of maintaining positioning accuracy when the number of satellites is small, avoiding matrix inversion failure, dynamically adjusting observation weight according to observation residual, suppressing the influence of multipath and non-line-of-sight errors on the positioning result, and maintaining the continuity of positioning in the occlusion scene are achieved.

[0074] To solve the problem of unstable positioning caused by insufficient number of satellites, the embodiment introduces UWB constraint in the observation equation and designs a weight dynamic adjustment mechanism based on residual feedback. A joint observation matrix G = [G BDS ; G UWB ] is constructed, and when the matrix rank is insufficient, SVD decomposition is used for stable calculation to avoid direct inversion failure. The dynamic updating rule of the observation weight matrix W is as follows:

[0075] 1. Calculate the current residual vector

[0076] 2. For each observation residual e i , calculate the standardized residual r i = e i / σ i , where σ i is the standard deviation of the corresponding observation error;

[0077] 3. Update the weight as γ is an adjustment parameter, usually taking a value range of [0.5, 2];

[0078] 4. Construct the weighted least squares system Iterative solution of the final positioning solution.

[0079] By dynamically adjusting the weight through the residual feedback mechanism, the interference of multipath and non-line-of-sight errors on the result is suppressed, and the positioning continuity is maintained in the occlusion scene.

[0080] Embodiment six

[0081] With reference to Figure 1 , the embodiment provides a dynamic coordinate conversion and fusion output module for realizing seamless fusion of Beidou and UWB positioning results, guaranteeing positioning continuity and trajectory smoothness, and being particularly suitable for complex application scenarios such as indoor-outdoor transition areas and semi-open environments. The module comprehensively considers the number of BDS satellites (N sat ) and UWB signal quality indicators, dynamically adjusts the fusion weight, and realizes adaptive smooth transition of multi-source fusion positioning results.

[0082] Specifically, to estimate the conversion parameters between the two coordinate systems in real time, the system first completes initial coordinate conversion matrix solving based on the least squares method, and then unifies the positioning results of Beidou and UWB to the same coordinate system. Subsequently, based on environmental perception information, the fusion weights of BDS and UWB positioning results are dynamically allocated to ensure that the system can obtain stable, continuous and accurate positioning results in different environments.

[0083] To determine the UWB signal quality, the system extracts the following key indicators for comprehensive evaluation: received signal strength (RSSI); first path energy (First Path Index); received energy (Rx Energy). When the RSSI strength is greater than -80 dBm, the first path energy exceeds the set threshold, and the received signal is stable and has no packet loss, it is determined that the UWB signal quality is good. Further, a sliding window residual statistical model is constructed to analyze the mean and standard deviation of the continuous UWB ranging residuals, and if the standard deviation σ is less than 0.1 m, it is considered that the ranging fluctuation is small and the error is stable, which can be used for weighted enhancement.

[0084] The system adopts the following dynamic weight allocation strategy according to the number of available BDS satellites (Nsat) and the UWB signal quality:

[0085] If N sat ≥ 6 and the UWB signal quality is general, the weight of the BDS result W GNSS = 0.8, and the weight of the UWB result W UWB = 0.2; if N sat ≤ 3 and the UWB signal quality is good, the weight of the BDS is reduced to 0.2, and the weight of the UWB is increased to 0.8; in other cases, a linear interpolation method is used to smoothly transition the fusion weight according to the Nsat value and the UWB quality score factor.

[0086] The final fusion positioning result X fusion is calculated by weighted average as follows:

[0087] X fusion = W BDS · X BDS + W UWB · X UWB

[0088] wherein X GNSS , X UWB represent the coordinate positions estimated by the Beidou system and the UWB system at the current moment, respectively, W GNSS , W UWB are the corresponding fusion weights, satisfying W GNSS + W UWB = 1 To avoid trajectory jump caused by sudden changes in the weights, the system is designed with a weight change mechanism, that is, the weight variation range of the BDS and the UWB in each fusion cycle should not exceed 10%, ensuring the continuity and stability of the weight change process.

[0089] In addition, to further improve the continuity and robustness of the positioning trajectory, the fusion result X fusion obtained in each cycle is processed by a sliding window weighted average, effectively suppressing the influence of incidental single-point errors on the overall trajectory, achieving centimeter-level error control and trajectory smoothness improvement.

[0090] Embodiment seven also has the following alternatives:

[0091] Alternative of the error joint estimation module:

[0092] Alternative 1: Replace the multi-model Kalman filter with a deep learning-based error prediction network, train the network parameters through historical error data, and realize dynamic compensation of clock error and multipath error.

[0093] Alternative 2: Use a federated filtering architecture, design independent filters for Beidou clock error and UWB base station time delay, respectively, and then output the joint estimation result through information fusion algorithm.

[0094] Alternative of the ambiguity fixing algorithm:

[0095] Alternative 1: Introduce lattice reduction technology in the LAMBDA algorithm to further reduce the computational complexity by transforming the search space.

[0096] Alternative 2: Use fuzzy clustering algorithm to classify floating ambiguity solutions, and preferentially search for integer combinations corresponding to the cluster centers.

[0097] Alternative of coordinate conversion and fusion:

[0098] Alternative 1: Replace the least squares calibration with a robust registration algorithm based on RANSAC to improve calibration accuracy by rejecting outliers.

[0099] Alternative 2: Introduce signal multipath characteristics in dynamic weight allocation, construct multi-dimensional weight decision model.

[0100] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection required by the present application is defined by the appended claims and their equivalents.

Claims

1. A real-time dynamic high-precision positioning method based on the tight coupling of Beidou and UWB, including data preprocessing and spatiotemporal alignment module, error joint estimation model, ambiguity fast fixation algorithm, geometric constraint enhanced positioning engine module, dynamic coordinate transformation and fusion output module. In the Beidou and UWB tight coupling positioning method, data preprocessing and spatiotemporal alignment are first performed, and the timestamps of Beidou original data and UWB ranging data are aligned through hardware PPS signal, and linear interpolation is used to compensate for sub-millisecond deviation to ensure the consistency of multi-source data time base. At the same time, Beidou positioning results and UWB base station coordinates are collected in an open area, and the initial coordinate transformation matrix is ​​calculated by the least squares method. The calibration accuracy is controlled within 2cm, and outliers are eliminated based on the 3σ criterion to provide high-quality input for deep fusion. Then, an error joint estimation model is constructed and an adaptive Kalman filter algorithm is used. Real-time estimation and compensation of Beidou clock error, UWB clock error and multipath error can significantly improve estimation accuracy and suppress error coupling in dynamic environments. In terms of ambiguity fixation, the LAMBDA algorithm is improved, and a block search strategy and ratio test mechanism are adopted to quickly fix the ambiguity, break through the efficiency bottleneck, and meet the real-time requirements of high-dynamic scenes. The positioning engine is enhanced with geometric constraints. When the number of satellites is small, the UWB ranging equation is introduced as a supplementary constraint, the position increment is jointly solved, and the rank-deficient equation is processed using singular value decomposition. A residual feedback mechanism is designed to suppress the influence of multipath and non-line-of-sight errors and maintain positioning continuity. Finally, dynamic coordinate transformation and fusion output are performed, and weights are dynamically allocated based on the number of satellites and UWB signal quality. Smooth transition is achieved in the indoor and outdoor transition areas through weighted fusion results, and the switching error is controlled at the centimeter level, the trajectory smoothness is improved, and seamless positioning in all scenarios is achieved.

2. The real-time dynamic high-precision positioning method based on tight coupling of Beidou and UWB according to claim 1 is characterized in that: The data preprocessing and time-space alignment, Beidou original data, including pseudorange ρ and carrier phase φ, and UWB ranging data d, first need to be timestamped by hardware PPS signal to ensure the temporal consistency of Beidou and UWB data, which provides a basis for subsequent data fusion. Since the clock of the hardware device may have sub-millisecond deviation, the linear interpolation method is used to compensate the data to eliminate this subtle time difference. In an open area, the Beidou positioning results and the UWB base station coordinates X are collected. UWB , using these data, the initial coordinate transformation matrix R and T are calculated by the least squares method. This transformation matrix is ​​used to transform the local coordinates of UWB into the ECEF coordinate system of Beidou, thereby achieving spatial alignment of the two positioning data. The calibration accuracy is controlled at ≤2cm. In addition, in order to eliminate the influence of abnormal data on subsequent solutions, sliding window statistics are performed on Beidou pseudorange and UWB ranging data respectively, and outliers are eliminated based on the 3σ criterion, which effectively avoids the interference of outliers on the positioning results and provides high-quality input data for deep fusion. The initial coordinate transformation matrix R, T calculated by the least squares method is:

3. The real-time dynamic high-precision positioning method based on tight coupling of Beidou and UWB according to claim 1 is characterized in that: The error joint estimation model is used to accurately estimate and compensate the Beidou clock error b GNSS 、UWB clock error b UWB and multipath error e MP , the state equation is constructed: x k+1 =Fx k +w k z k =Hx k +v k in, represents the state vector, F is the state transfer matrix, which describes the change of the state over time; H is the observation matrix, which maps the state vector to the observation space; w and v are process noise and measurement noise, respectively, reflecting the uncertainty of the system and the observation error. The adaptive Kalman filter algorithm is used to iteratively update the Kalman filter gain matrix to estimate and compensate for the clock error and multipath error in real time. By continuously adjusting the filter parameters, the algorithm can adapt to the noise characteristics in different environments and improve the accuracy of the estimation. The Beidou clock error estimation accuracy can reach nanosecond level, which significantly suppresses the error coupling phenomenon in dynamic environment and improves the long-term stability of the system.

4. The real-time dynamic high-precision positioning method based on tight coupling of Beidou and UWB according to claim 1 is characterized in that: The proposed fast ambiguity fixation algorithm uses the UWB ranging equation to provide position geometry constraints, compressing the integer ambiguity search dimension from 6 to less than 3 dimensions, and improving the LAMBDA algorithm: Among them, Q is the ambiguity covariance matrix, which reflects the correlation between ambiguities; a * The floating-point ambiguity solution is the starting point of the ambiguity search. The block search strategy and ratio test mechanism are used to prioritize the screening of the optimal ambiguity combination. Through block search, the search space can be divided into multiple small blocks, which are searched separately, thereby improving the search efficiency. At the same time, the ratio test mechanism is used to evaluate the reliability of different ambiguity combinations to ensure that the selected ambiguity combination is the optimal one, breaking through the bottleneck of ambiguity fixation efficiency.

5. The real-time dynamic high-precision positioning method based on tight coupling of Beidou and UWB according to claim 1, characterized in that: The geometric constraint enhances the positioning engine when the number of satellites N sat When ≥4, the Beidou observation equation is used to solve the position: ρ i =‖X-X i ‖+cb GNSS +e MP,i N sat = 2 or 3, the UWB ranging equation is introduced as a supplementary constraint to jointly solve the position increment: d j =‖X-X j ‖ During the solution process, singular value decomposition is used to deal with the rank-deficient equation, avoiding the problem of matrix inversion failure. At the same time, it has a feedback mechanism to dynamically adjust the observation weight according to the observation residual, suppressing the impact of multipath and non-line-of-sight errors on the positioning results. This mechanism can maintain the continuity of positioning in occlusion scenarios.

6. The real-time dynamic high-precision positioning method based on tight coupling of Beidou and UWB according to claim 1, characterized in that: To achieve seamless fusion of Beidou and UWB positioning results, the dynamic coordinate conversion and fusion output module estimates the conversion parameters of the two coordinate systems in real time. It dynamically assigns weights based on the number of satellites and UWB signal quality to ensure accurate positioning results in different environments. As the number of satellites decreases, the weight of UWB is gradually increased from 20% to 80% to fully utilize UWB positioning information. In the indoor-outdoor transition zone, a smooth transition is achieved through weighted fusion results, avoiding trajectory jumps caused by relying on fixed thresholds. The switching error is kept within the centimeter level, significantly improving trajectory smoothness.

Images