Multipath error suppression method and system in high-precision positioning navigation

Abstract

The application provides a multi-path error suppression method and system in high-precision positioning navigation, and relates to the technical field of positioning navigation. The method comprises the following steps: after multi-dimensional abnormal feature extraction is performed on multi-path satellite signals, signal pollution probability value quantization is performed, and multi-path pollution observation values are output to screen K pollution channels; direct wave and reflected wave parameter joint solving based on bionic optimization search is performed to generate K multi-path error compensation values; error correction is performed to obtain K purified observation values; robust weighted positioning calculation is performed to obtain high-precision position estimation results; and dynamic constraint is performed on the bionic optimization search parameter space of subsequent epochs to output real-time navigation positioning information. The application solves the technical problem that the prior art is mostly based on single feature to judge signal abnormality, which leads to insufficient multi-path pollution identification capability in complex environments, and further leads to difficulty in accurately distinguishing normal signals from pollution signals, and finally leads to high false detection rate and high missed detection rate.

Description

Technical Field

[0001] This invention relates to the field of positioning and navigation technology, and specifically to a method and system for suppressing multipath errors in high-precision positioning and navigation. Background Technology

[0002] With the widespread application of global satellite navigation systems in autonomous driving, drone navigation, intelligent surveying and mapping, and high-precision positioning terminals, users have placed higher demands on the real-time positioning accuracy and stability in complex environments.

[0003] However, in urban canyons, under overpasses, densely built-up areas, and partially obstructed environments, satellite signals are easily affected by reflections from buildings, the ground, and metallic reflective surfaces during propagation, resulting in multipath propagation. Because the receiver simultaneously receives both direct and reflected signals, the received observations are distorted, affecting the accuracy of pseudorange and carrier phase observations, ultimately leading to increased positioning errors, positioning drift, or even positioning failure.

[0004] In existing technologies, carrier-to-noise ratio threshold detection, correlation peak distortion detection, or error smoothing based on Kalman filtering are commonly used to suppress multipath errors. Most of these methods rely on a single feature to determine signal anomalies, resulting in insufficient multipath contamination identification capabilities in complex environments. Consequently, it becomes difficult to accurately distinguish between normal and contaminated signals, ultimately leading to high false detection and false negative rates. Summary of the Invention

[0005] This application provides a method and system for suppressing multipath errors in high-precision positioning and navigation, aiming to solve the technical problem that most existing technologies rely on a single feature to judge signal anomalies, resulting in insufficient multipath pollution identification capabilities in complex environments, making it difficult to accurately distinguish between normal and polluted signals, and ultimately leading to high false detection and false detection rates.

[0006] The first aspect disclosed in this application provides a method for suppressing multipath errors in high-precision positioning and navigation. The method includes: extracting multi-dimensional anomaly features from received multi-channel satellite signals, quantifying the signal contamination probability value, and outputting multi-channel contamination observations to filter K contaminated channels that do not meet a preset contamination probability threshold; performing a joint solution of direct and reflected wave parameters based on biomimetic optimization search on the original observations of the K contaminated channels to generate K multipath error compensation quantities; using the K multipath error compensation quantities to correct the errors in the original observations of the K contaminated channels to obtain K purified observations; performing robust weighted positioning calculations on the K purified observations and the original observations of the remaining M normal channels in the multi-channel satellite signals to obtain a high-precision position estimation result; using the high-precision position estimation result as spatial position prior information to apply dynamic constraints to the biomimetic optimization search parameter space in subsequent epochs, and outputting real-time navigation and positioning information.

[0007] The second aspect of this application discloses a multipath error suppression system for high-precision positioning and navigation. The system is used in the aforementioned multipath error suppression method for high-precision positioning and navigation. The system includes: a probability value quantization module, used to perform multi-dimensional anomaly feature extraction on received multi-channel satellite signals, then perform signal contamination probability value quantization, and output multi-channel contamination observations to filter K contamination channels that do not meet a preset contamination probability threshold; and a joint solution module, used to perform a biomimetic optimization search-based joint solution of direct wave and reflected wave parameters on the original observations of the K contamination channels to generate K... The system includes: a multipath error compensation module; an error correction module for correcting the original observations of the K contaminated channels using the K multipath error compensation quantities to obtain K purified observations; a positioning solution module for performing robust weighted positioning solution by combining the K purified observations with the original observations of the remaining M normal channels in the multipath satellite signals to obtain a high-precision position estimation result; and a dynamic constraint module for using the high-precision position estimation result as prior spatial position information to apply dynamic constraints to the biomimetic optimization search parameter space in subsequent epochs, and outputting real-time navigation and positioning information.

[0008] One or more technical solutions provided in this application have at least the following beneficial effects:

[0009] By extracting multi-dimensional anomaly features from received multi-channel satellite signals and quantifying contamination probability values, rapid identification and screening of contaminated multipath channels are achieved, providing reliable input for subsequent multipath error compensation. By jointly solving for direct and reflected wave parameters based on biomimetic optimization search on the original observations of contaminated channels, adaptive inversion of the multipath propagation state is achieved, more accurately approximating the true multipath propagation state, thereby improving the accuracy of multipath error estimation and enhancing global search capability and parameter convergence stability in complex environments. By using multipath error compensation to correct errors in the original observations of contaminated channels, the contaminated observations are purified, improving the reliability and positioning availability of the purified observations. By performing robust weighted positioning calculations between the purified observations and normal channel observations, high-precision positioning results are output in complex environments, improving overall positioning accuracy, positioning stability, and anti-interference capability in complex obstructed environments. By using the high-precision position estimation result of the current epoch as prior spatial position information for subsequent epochs and applying dynamic constraints to the biomimetic optimization search parameter space, real-time adaptive positioning optimization in continuous epochs is achieved, thereby improving continuous high-precision navigation capability in dynamic environments.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0011] Figure 1 This is a schematic flowchart of a multipath error suppression method for high-precision positioning and navigation provided in this application embodiment.

[0012] Figure 2 This is a schematic diagram of the structure of a multipath error suppression system for high-precision positioning and navigation provided in an embodiment of this application.

[0013] Figure labeling: 10 probability value quantization module, 20 joint solution module, 30 error correction module, 40 location solution module, 50 dynamic constraint module. Detailed Implementation

[0014] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0015] Example 1, as Figure 1 As shown in the embodiments of this application, a multipath error suppression method is provided in high-precision positioning and navigation. The method includes:

[0016] A100: After performing multi-dimensional anomaly feature extraction on the received multi-channel satellite signals, the signal contamination probability value is quantified, and multi-channel contamination observation values ​​are output to filter out K contamination channels that do not meet the preset contamination probability threshold.

[0017] The received multi-channel satellite RF signals undergo down-conversion, carrier stripping, and pseudo-code despreading to obtain the corresponding baseband in-phase and quadrature branch signals. Instantaneous carrier-to-noise ratio (CNR) is calculated based on these signals, and a dynamically set CNR threshold is used to eliminate unlocked or weak signal channels, resulting in effective signal channels. Anomalies such as phase abrupt change rate, Doppler shift standard deviation, correlation peak symmetry, and reflection correlation are extracted from each effective signal channel and assembled into corresponding feature vectors. These feature vectors are then input into a pre-trained contamination probability classification model to predict the contamination probability, yielding a contamination probability value for each channel. The contamination probability values ​​are compared with a preset contamination probability threshold to select K contaminant channels.

[0018] A200: Perform a joint solution of direct and reflected wave parameters based on biomimetic optimization search on the original observations of the K pollution channels to generate K multipath error compensation quantities.

[0019] A joint parameter space is established based on the propagation characteristics of direct and reflected waves. The joint parameters include code phase offset, signal attenuation factor, relative time delay, reflection loss rate, phase jump variable, and incident angle. Multiple joint parameter vectors are initialized within this space using a dispersion constraint-based approach. Corresponding model observations are calculated based on the signal propagation model, and fitness results are obtained by error matching with the original observations from the contaminated channel. An adjoint solution is generated using a combination of elite screening and Gaussian perturbation. Parameter optimization is performed through a population evolution process involving iterative shrinking of the search radius to obtain the target joint parameter vector. Based on the relative time delay, reflection loss rate, and phase jump variable in the target joint parameter vector, the corresponding pseudorange compensation and phase compensation are calculated, thereby generating the corresponding multipath error compensation.

[0020] A300: The K multipath error compensation values ​​are used to correct the error of the original observation values ​​of the K pollution channels to obtain the K purified observation values.

[0021] The pseudorange compensation and phase compensation for each contaminated channel are applied to the original pseudorange and original carrier phase observations, respectively. Multipath error is eliminated through algebraic compensation to obtain preliminary purified observations. Cycle slip detection is performed on the preliminary purified observations, and phase continuity is repaired by combining carrier Doppler information to reduce positioning errors caused by abnormal carrier phase jumps. Finally, the corresponding purified observations are output.

[0022] A400: Perform robust weighted positioning calculations on the K purified observation values ​​and the original observation values ​​of the remaining M normal channels in the multi-channel satellite signals to obtain high-precision position estimation results.

[0023] The K purified observations are assigned fixed high weights to improve the utilization of the contaminated channels after error compensation in the positioning calculation. Simultaneously, the remaining M normal channels are assigned dynamic weights based on their signal-to-noise ratio and phase-locked loop bandwidth. The purified and normal channel observations are input into a robust positioning model. The impact of abnormal observations on the positioning results is mitigated through standardized residual iteration, and the weights of each channel are dynamically adjusted, ultimately outputting a high-precision position estimation result.

[0024] A500: The high-precision position estimation result is used as prior information of spatial position. Dynamic constraints are applied to the biomimetic optimization search parameter space of subsequent epochs, and real-time navigation and positioning information is output.

[0025] The high-precision position estimate obtained in the current epoch is used as prior spatial position information for subsequent epochs. Combined with satellite geometry, receiver motion, and historical optimization results, the search range for joint parameters in subsequent epochs is dynamically narrowed to reduce invalid search areas and improve parameter convergence speed. Multipath error compensation and positioning calculation are then performed within the updated parameter space, thus achieving real-time high-precision navigation and positioning across continuous epochs.

[0026] Furthermore, the original observations of the K contaminated channels are subjected to a joint solution of direct and reflected wave parameters based on biomimetic optimization search to generate K multipath error compensation quantities. The method includes:

[0027] A210: Construct a multidimensional parameter space based on the parameter boundaries of direct and reflected waves; A220: Initialize N initial joint parameter vectors in the multidimensional parameter space using a preset minimum Euclidean distance as a dispersion constraint; A230: Calculate N model observations of the N initial joint parameter vectors using a signal propagation model, compare them with the first original observation of the first contaminated channel, and calculate N initial fitnesss; A240: After selecting W initial elite solutions according to the ascending order of the N initial fitnesss, introduce Gaussian perturbation to generate W initial adjoint solutions; A250: Using the W initial elite solutions and W initial adjoint solutions as the current population, and using the first original observation as the fitness benchmark, perform a population evolution process with iterative shrinking of the search radius in the multidimensional parameter space to obtain the target joint parameter vector; A260: Introduce an error mapping function to calculate the first multipath error compensation amount of the target joint parameter vector.

[0028] Based on the propagation characteristics of direct and reflected waves during satellite signal propagation, a joint parameter model is established. The joint parameters include the code phase offset and signal attenuation factor of the direct wave, and the relative time delay, reflection loss rate, phase jump, and incident angle of the reflected wave. According to the receiver hardware characteristics, satellite signal modulation features, and environmental reflection conditions, upper and lower boundaries are set for each joint parameter, thereby constructing a multi-dimensional parameter space for multipath parameter optimization to limit the subsequent population search range.

[0029] Multiple joint parameter vectors are randomly generated within the multidimensional parameter space, and the Euclidean distance between each joint parameter vector is calculated. When the distance between any two joint parameter vectors is less than a preset minimum Euclidean distance, the corresponding joint parameter vectors are randomly regenerated to avoid excessive aggregation of the initial population. Through this dispersion constraint, the N generated initial joint parameter vectors are evenly distributed within the multidimensional parameter space, thereby improving the subsequent global search capability.

[0030] Each initial joint parameter vector is input into a preset signal propagation model to simulate the received signal after the direct wave and reflected wave are superimposed under the corresponding parameter conditions, obtaining the corresponding model observation values. The model observation values ​​are compared with the first original observation values ​​of the first contaminated channel to calculate the initial fitness value corresponding to each initial joint parameter vector. The smaller the fitness value, the closer the corresponding joint parameter vector is to the actual direct wave and reflected wave parameter state at the time the current contaminated observation value was formed.

[0031] The N initial fitness values ​​are sorted in ascending order, and the W joint parameter vectors with the smallest fitness values ​​are selected as the initial elite solutions. Centered on the W initial elite solutions, random perturbations satisfying a Gaussian distribution are introduced into each parameter dimension to generate corresponding W initial adjoint solutions. The Gaussian perturbations are used to form local search regions near the elite solutions, thereby improving the search accuracy for the optimal parameter region while maintaining population search diversity.

[0032] The W initial elite solutions and W initial adjoint solutions are used together as the current iterative population, and the fitness results corresponding to each joint parameter vector are continuously calculated based on the first original observation. The current population is sorted by fitness, and the updated elite solutions with better fitness are retained. At the same time, a Gaussian random walk is performed within the current search radius, with the optimal elite solution as the main perturbation center and the other elite solutions as auxiliary perturbation centers, to generate a new round of adjoint solutions. After each round of population iteration, the search radius is gradually reduced according to a preset shrinkage factor, so that the population search process gradually changes from a large-scale global search to a local fine search, until the preset convergence condition is met, and the target joint parameter vector is output. The target joint parameter vector corresponds to the optimal combination of direct wave parameters and reflected wave parameters when generating the current original observation of the contaminated channel.

[0033] The relative time delay, reflection loss rate, and phase jump variable of the reflected wave are extracted from the target joint parameter vector. The direct wave parameters corresponding to the target joint parameter vector are then compared with preset ideal direct wave parameters to determine the propagation offset caused by multipath propagation. The relative time delay and phase jump variable are input into an error mapping function, and the corresponding pseudorange error compensation and carrier phase error compensation are calculated through the error mapping relationship. The error mapping function is used to establish a mapping relationship between the propagation characteristics of the reflected wave and navigation observation errors, thereby obtaining the first multipath error compensation amount used to correct the original observation values ​​of the contaminated channel.

[0034] Furthermore, using the W initial elite solutions and W initial adjoint solutions as the current population, and the first original observation value as the fitness benchmark, a population evolution process of iterative shrinkage of the search radius is performed in the multidimensional parameter space to obtain the target joint parameter vector. The method includes:

[0035] S1: Calculate the W model observations of the W initial adjoint solutions using the signal propagation model, compare them with the first original observations, and calculate the W first adjoint fitnesss; S2: By merging and sorting the W initial elite solutions and the W initial adjoint solutions, select the top W updated elite solutions in ascending fitness order; S3: Based on the descending fitness sorting of the W updated elite solutions, divide them into the optimal guiding solution and W-1 secondary elite solutions; S4: After dynamic search radius decay using a preset shrinkage factor, with the optimal guiding solution as the core perturbation center and the W-1 secondary elite solutions as auxiliary perturbation centers, perform a Gaussian random walk with standard deviation classification around the W updated elite solutions to generate W first iterative adjoint solutions; A251: Iterate steps S1 to S4, perform a population evolution process of iterative shrinkage of the search radius until the preset convergence condition is met, and output the target joint parameter vector.

[0036] The W initial adjoint solutions are input into the signal propagation model, and the corresponding model observations after the superposition of the direct and reflected waves are calculated. The errors of each model observation are compared with the first original observation, and the corresponding first adjoint fitness is calculated based on pseudorange error, carrier phase error, and correlation peak deviation. The first adjoint fitness characterizes the degree of fit between the current adjoint solution and the original observations of the contaminated channel; a smaller fitness value indicates that the corresponding joint parameter vector is closer to the actual propagation state.

[0037] The W initial elite solutions and W initial adjoint solutions are merged into a population, and the fitness results corresponding to each joint parameter vector in the merged population are recalculated. All joint parameter vectors are sorted in ascending order of fitness value, and the top W joint parameter vectors with the smallest fitness value are selected as updated elite solutions to retain the parameter combinations with better fitting ability in the current iteration.

[0038] The W updated elite solutions are sorted according to their fitness results, and the joint parameter vector with the best fitness value is determined as the optimal guiding solution, while the remaining W-1 joint parameter vectors are determined as secondary elite solutions. The optimal guiding solution is used to determine the main search direction of the current population, and the secondary elite solutions are used to maintain parameter search diversity to avoid the population from prematurely entering a local optimum.

[0039] In the current iteration, the search radius is decayed according to a preset shrinkage factor to gradually narrow the search range in the joint parameter space. The optimal guiding solution is used as the core perturbation center, assigned a 30% standard deviation perturbation weight; the W-1 secondary elite solutions are used as auxiliary perturbation centers, assigned a 70% standard deviation perturbation weight. A Gaussian random walk is performed around the updated elite solutions to generate a new first-iteration adjoint solution. The core perturbation center enhances the population's convergence ability towards the global optimum, while the auxiliary perturbation centers expand the local search range, thereby improving parameter optimization stability.

[0040] Steps S1 to S4 are repeated, continuously updating the elite solution, adjoint solution, and search radius in each iteration, and dynamically adjusting the search range of the population in the joint parameter space. Furthermore, a dual convergence condition is used as the basis for terminating population evolution. The first convergence condition is that the rate of change of fitness of the elite solution within a consecutive preset number of generations is less than a first threshold, indicating that the current population has reached a stable convergence state. The second convergence condition is that the reflected wave parameters meet the validity verification conditions, including the relative time delay value, reflection loss rate, and phase jump variable meeting preset physical propagation constraints. When both dual convergence conditions are simultaneously met, the population evolution process is terminated, and the corresponding target joint parameter vector is output.

[0041] Furthermore, an error mapping function is introduced to calculate the first multipath error compensation amount of the target joint parameter vector, the method comprising:

[0042] A261: Separate the relative delay optimization value, reflection optimization loss rate, and phase jump optimization amount from the target joint parameter vector; A262: If the relative delay optimization value is greater than a preset delay threshold and the reflection optimization loss rate is less than a preset loss threshold, then input the relative delay optimization value and the phase jump optimization amount in parallel into the error mapping sub-function and phase error mapping sub-function of the error mapping function to calculate the first pseudorange compensation amount and the first phase compensation amount, wherein the first pseudorange compensation amount and the first phase compensation amount constitute the first multipath error compensation amount; A263: If the relative delay optimization value is less than a preset delay threshold, and / or the reflection optimization loss rate is greater than a preset loss threshold, then set the target joint parameter vector as an optimization taboo and re-execute population evolution optimization.

[0043] The parameters in the target joint parameter vector are analytically separated to extract the relative delay optimization value, reflection optimization loss rate, and phase jump optimization value corresponding to the reflected wave. The relative delay optimization value characterizes the difference in propagation delay between the reflected wave and the direct wave; the reflection optimization loss rate characterizes the energy attenuation of the reflected wave during propagation; and the phase jump optimization value characterizes the carrier phase change characteristics caused by the reflection path, serving as input parameters for subsequent error compensation calculations.

[0044] When the relative delay optimization value satisfies the effective reflection path propagation condition and the reflection optimization loss rate satisfies the effective reflection energy constraint condition, the current target joint parameter vector is determined to be an effective multipath propagation solution. The relative delay optimization value is input into the error mapping subfunction to establish a mapping relationship between propagation delay and pseudorange deviation, and the corresponding first pseudorange compensation amount is calculated. Simultaneously, the phase jump optimization amount is input into the phase error mapping subfunction to establish a mapping relationship between phase jump and carrier phase error, and the corresponding first phase compensation amount is calculated. The first pseudorange compensation amount and the first phase compensation amount are combined to form the first multipath error compensation amount, which is used for subsequent correction of contaminated channel observations.

[0045] When the relative time delay optimization value is lower than a preset time delay threshold, it indicates that the current reflected wave is too close to the direct wave, making it difficult to form an effective multipath propagation characteristic; or, when the reflection optimization loss rate is higher than a preset loss threshold, it indicates that the energy attenuation of the current reflected wave is too large, failing to meet the effective reflection path condition. In this case, the current target joint parameter vector is marked as a taboo solution, and the population is restricted from re-entering the corresponding parameter region during subsequent population evolution to avoid repeatedly searching the invalid solution space. Subsequently, the population evolution optimization process is re-executed until a target joint parameter vector that meets the validity verification conditions is obtained.

[0046] Furthermore, the K multipath error compensation values ​​are used to correct the errors in the original observations of the K contaminated channels to obtain the K purified observations. The method includes:

[0047] A310: The first pseudorange compensation amount and the first phase compensation amount are mapped to perform algebraic superposition correction of the first original pseudorange observation and the first original carrier phase in the first original observation to obtain the first preliminary purified value; A320: The first preliminary purified value is subjected to carrier Doppler-assisted repair under cycle slip detection to output the first purified observation value.

[0048] The first pseudorange compensation is applied to the first original pseudorange observation to eliminate the propagation delay deviation caused by the reflection path through algebraic compensation. Simultaneously, the first phase compensation is applied to the first original carrier phase to correct carrier phase distortion caused by multipath propagation. The compensated pseudorange and carrier phase observations are combined and output to obtain the first preliminary cleaned value, thereby reducing the impact of multipath errors on the positioning observation results.

[0049] Cycle slip detection is performed on the carrier phase continuity in the first preliminary purified value, and phase change prediction results are constructed by combining the carrier Doppler variation between adjacent epochs. When an abnormal phase jump is detected, the abnormal phase is repaired using the carrier Doppler prediction results to restore the continuity and stability of the carrier phase observations. The repaired carrier phase observations and the compensated pseudorange observations are combined as the first purified observation output for subsequent positioning calculations.

[0050] Furthermore, robust weighted positioning calculations are performed by combining the K purified observations with the original observations of the remaining M normal channels from the multi-channel satellite signals to obtain a high-precision position estimation result. The method includes:

[0051] A410: Assign fixed high weights to the K cleaned observations, assign dynamic weights based on signal-to-noise ratio and phase-locked loop bandwidth to the original observations of the remaining M normal channels in the multi-channel satellite signals, perform standardized residual-driven robust iteration, and output the high-precision position estimation result.

[0052] K-path cleaned observations, after multipath error compensation, are used as high-reliability observation data and assigned fixed high weights to enhance their participation in the positioning calculation. Simultaneously, for the remaining M normal channels' original observations, observation weights are dynamically calculated based on the corresponding channel's signal-to-noise ratio (SNR) and phase-locked loop (PLL) bandwidth. Observation channels with higher SNR and stronger PLL stability are assigned higher weights. The K-path cleaned observations and M normal channel observations are input into the positioning calculation model. The error contribution of each observation channel is calculated using standardized residuals, and weight suppression is applied to observations corresponding to abnormal residuals to reduce the impact of abnormal observations on the positioning results. The weights of each observation channel are continuously updated through multiple rounds of robust iteration until the residuals converge and stabilize, ultimately outputting a high-precision position estimation result.

[0053] Furthermore, after performing multi-dimensional anomaly feature extraction on the received multi-channel satellite signals, the signal contamination probability value is quantified, and multi-channel contamination observation values ​​are output to filter out K contamination channels that do not meet the preset contamination probability threshold. The method includes:

[0054] A110: Perform pseudo-code despreading on the multiple satellite signals after carrier stripping to obtain multiple baseband in-phase signals and multiple orthogonal branch signals; A120: Calculate multiple instantaneous carrier-to-noise ratios based on the multiple baseband in-phase signals and multiple orthogonal branch signals, compare them with dynamically set carrier-to-noise ratio thresholds to eliminate failed channels and obtain P effective signal channels; A130: Perform multi-dimensional feature extraction on the P effective signal channels to obtain P four-dimensional feature vectors, input them into a pollution probability classification model constructed based on gradient boosting decision trees to perform pollution probability prediction calculations, and output P pollution observation values; A140: Compare the P pollution observation values ​​with a preset pollution probability threshold to filter and obtain the K pollution channels.

[0055] The received multi-channel satellite radio frequency signals are down-converted and then synchronized and stripped using a local carrier to eliminate high-frequency carrier components. The intermediate frequency (IF) signal after carrier stripping undergoes pseudo-code despreading. Correlation is then performed between the received signal and a local pseudo-random code to obtain the corresponding baseband in-phase signal and quadrature branch signal. The baseband in-phase signal characterizes the signal amplitude and correlation peak characteristics, while the quadrature branch signal characterizes the carrier phase and frequency variation characteristics.

[0056] Instantaneous signal power and noise power are calculated based on the baseband in-phase signal and quadrature branch signal corresponding to each channel, and the corresponding instantaneous carrier-to-noise ratio (CNR) is also calculated. A CNR threshold is dynamically set based on the current receiving environment, electromagnetic interference intensity, and satellite elevation information, and the instantaneous CNR of each channel is compared with this threshold. When the instantaneous CNR of a channel is lower than the threshold, the corresponding channel is determined to have lost lock, obstruction, or severe interference, and is discarded; the remaining channels that meet the threshold conditions are output as P-channel valid signal channels.

[0057] Four types of anomalous features—phase abrupt change rate, Doppler frequency shift standard deviation, correlation peak symmetry, and reflection correlation degree—are extracted from the P-channel effective signal channels, and these features are assembled into corresponding four-dimensional feature vectors. The phase abrupt change rate reflects carrier phase stability, the Doppler frequency shift standard deviation reflects frequency fluctuation, the correlation peak symmetry reflects correlation function distortion, and the reflection correlation degree reflects the degree of environmental reflection path matching. These four-dimensional feature vectors are input into a pollution probability classification model constructed based on a gradient boosting decision tree, which outputs the pollution probability value for each effective signal channel. The corresponding results are then used as the P-channel pollution observation values.

[0058] The contamination probability value corresponding to each valid signal channel is compared with a preset contamination probability threshold. When the contamination probability value of a channel is higher than the contamination probability threshold, the corresponding channel is determined to have obvious multipath contamination and is marked as a contaminated channel; the remaining channels with a contamination probability value lower than the contamination probability threshold are retained as normal channels. Finally, K contaminated channels are selected for subsequent joint solution of multipath error parameters and error compensation processing.

[0059] Furthermore, multi-dimensional feature extraction is performed on the P effective signal channels to obtain P four-dimensional feature vectors. The method includes:

[0060] A131: Perform carrier phase tracking on the P orthogonal branch signals of the P effective signal channels, calculate the second-order differential variance of the phase, and output P phase abrupt change rates; A132: Perform phase-locked loop processing based on the P orthogonal branch signals and the P baseband in-phase signals, and output P Doppler frequency shift standard deviations based on statistical standard deviations; A133: Use the P baseband in-phase signals of the P effective signal channels to drive three correlators to calculate P correlation peak symmetries; A134: Perform power synthesis based on the P orthogonal branch signals and the P baseband in-phase signals, and combine the carrier attitude matrix and 3D environment map to perform reflection path matching calculation, outputting P reflection correlation degrees; A135: Assemble the P phase abrupt change rates, P Doppler frequency shift standard deviations, P correlation peak symmetries, and P reflection correlation degrees into P four-dimensional feature vectors.

[0061] Carrier phase continuous tracking is performed on the orthogonal branch signals corresponding to each effective signal channel to obtain the carrier phase change between adjacent epochs. Second-order difference operations are performed on the carrier phase of consecutive epochs, and the variance of the corresponding second-order difference results is calculated to characterize the abrupt change in carrier phase. A larger second-order difference variance indicates more severe phase fluctuations in the corresponding signal channel, and a more pronounced phase anomaly caused by multipath reflection or obstruction. The second-order difference variance of each effective signal channel is output as the corresponding phase abrupt change rate.

[0062] The quadrature branch signals and baseband in-phase signals corresponding to each effective signal channel are input into a phase-locked loop (PLL) for carrier tracking, and the corresponding Doppler frequency shift estimates are calculated based on the PLL output. Statistical analysis is performed on the Doppler frequency shift estimates over multiple consecutive epochs, and the corresponding standard deviations are calculated. The Doppler frequency shift standard deviation reflects the signal frequency stability; when multipath reflection causes changes in the propagation path, the Doppler frequency shift fluctuation of the corresponding channel increases, resulting in a larger Doppler frequency shift standard deviation. Finally, the Doppler frequency shift standard deviations for each effective signal channel are output.

[0063] The baseband in-phase signals corresponding to each effective signal channel are input into a lead correlator, a synchronization correlator, and a lag correlator for correlation calculations to obtain the corresponding correlation peak distribution results. The correlation peak symmetry index is calculated based on the amplitude difference between the lead and lag correlation values. When the received signal is affected by multipath reflection, the correlation function waveform is distorted, and the left-right symmetry of the corresponding correlation peak decreases, leading to abnormal changes in the correlation peak symmetry index. Finally, the correlation peak symmetry results for each effective signal channel are output.

[0064] Power synthesis is performed on the orthogonal branch signals and baseband in-phase signals corresponding to each effective signal channel to obtain the comprehensive signal power characteristics of the corresponding channel. The spatial orientation relationship between the receiver and the satellite is determined by combining the current carrier attitude matrix, and the spatial distribution information of surrounding buildings, ground, and obstructions is obtained based on a pre-set 3D environment map. Reflection path matching calculations are performed based on the satellite incident direction, the position of the environmental reflector, and signal power variations to assess the correlation between the current signal channel and the environmental reflection path. Finally, the reflection correlation degree corresponding to each effective signal channel is output.

[0065] The phase abrupt change rate, Doppler frequency shift standard deviation, correlation peak symmetry, and reflection correlation degree corresponding to each effective signal channel are normalized and then combined and encapsulated according to a preset feature order to form corresponding four-dimensional feature vectors. Each four-dimensional feature vector is used to comprehensively characterize the phase stability, frequency stability, correlation peak distortion degree, and environmental reflection correlation characteristics of the corresponding effective signal channel, serving as input data for subsequent pollution probability classification models.

[0066] Furthermore, the initial joint parameter vector includes the code phase offset and signal attenuation factor of the direct wave, as well as the relative time delay, reflection loss rate, phase jump variable, and incident angle of the reflected wave.

[0067] The code phase offset is used to characterize the code phase deviation generated during the propagation of the direct wave; the signal attenuation factor is used to characterize the degree of energy attenuation during the propagation of the direct wave; the relative delay value is used to characterize the additional propagation delay of the reflected wave relative to the direct wave; the reflection loss rate is used to characterize the degree of energy loss of the reflected wave during reflection propagation; the phase jump variable is used to characterize the carrier phase change caused by the reflection path; and the incident angle is used to characterize the spatial incident relationship between the reflected wave and the reflecting surface. These parameters together constitute an initial joint parameter vector describing the propagation state of the direct and reflected waves, which is used for subsequent multipath error joint optimization calculations.

[0068] Example 2, based on the same inventive concept as the multipath error suppression method in high-precision positioning and navigation in the foregoing examples, such as... Figure 2 As shown in the figure, this application provides a multipath error suppression system for high-precision positioning and navigation, the system comprising:

[0069] The probability quantization module 10 is used to extract multi-dimensional abnormal features from the received multi-channel satellite signals, quantize the signal contamination probability value, and output multi-channel contamination observation values ​​to filter out K contamination channels that do not meet the preset contamination probability threshold. The joint solution module 20 is used to perform joint solution of direct wave and reflected wave parameters based on biomimetic optimization search on the original observation values ​​of the K contamination channels to generate K multipath error compensation quantities. The error correction module 30 is used to perform error correction on the original observation values ​​of the K contamination channels using the K multipath error compensation quantities to obtain K cleaned observation values. The positioning solution module 40 is used to perform robust weighted positioning solution on the K cleaned observation values ​​and the original observation values ​​of the remaining M normal channels in the multi-channel satellite signals to obtain a high-precision position estimation result. The dynamic constraint module 50 is used to use the high-precision position estimation result as spatial position prior information to perform dynamic constraints on the biomimetic optimization search parameter space of subsequent epochs and output real-time navigation and positioning information.

[0070] Furthermore, the joint solver module 20 is used to perform the following operation steps:

[0071] A multidimensional parameter space is constructed based on the parameter boundaries of direct and reflected waves. Using a preset minimum Euclidean distance as a dispersion constraint, N initial joint parameter vectors are initialized in the multidimensional parameter space. N model observations of the N initial joint parameter vectors are calculated using a signal propagation model, and compared with the first original observation of the first contaminated channel to calculate N initial fitness values. W initial elite solutions are selected based on the ascending order of the N initial fitness values, and a Gaussian perturbation is introduced to generate W initial adjoint solutions. Using the W initial elite solutions and W initial adjoint solutions as the current population, and the first original observation as the fitness benchmark, a population evolution process with iterative shrinking of the search radius is performed in the multidimensional parameter space to obtain the target joint parameter vector. An error mapping function is introduced to calculate the first multipath error compensation amount of the target joint parameter vector.

[0072] Furthermore, the joint solver module 20 is used to perform the following operation steps:

[0073] S1: Calculate the W model observations of the W initial adjoint solutions using the signal propagation model, compare them with the first original observations, and calculate the W first adjoint fitnesss; S2: By merging and sorting the W initial elite solutions and the W initial adjoint solutions, select the top W updated elite solutions in ascending fitness order; S3: Based on the descending fitness sorting of the W updated elite solutions, divide them into the optimal guiding solution and W-1 secondary elite solutions; S4: After dynamic search radius decay using a preset shrinkage factor, with the optimal guiding solution as the core perturbation center and the W-1 secondary elite solutions as auxiliary perturbation centers, perform a Gaussian random walk with standard deviation classification around the W updated elite solutions to generate W first iterative adjoint solutions; Iteration steps S1 to S4 execute the population evolution process of iterative shrinkage of the search radius until the preset convergence condition is met, and output the target joint parameter vector.

[0074] Furthermore, the joint solver module 20 is used to perform the following operation steps:

[0075] Separate the relative delay optimization value, reflection optimization loss rate, and phase jump optimization amount from the target joint parameter vector; if the relative delay optimization value is greater than a preset delay threshold and the reflection optimization loss rate is less than a preset loss threshold, then input the relative delay optimization value and the phase jump optimization amount in parallel into the error mapping sub-function and phase error mapping sub-function of the error mapping function to calculate the first pseudorange compensation amount and the first phase compensation amount, wherein the first pseudorange compensation amount and the first phase compensation amount constitute the first multipath error compensation amount; if the relative delay optimization value is less than the preset delay threshold, and / or the reflection optimization loss rate is greater than the preset loss threshold, then set the target joint parameter vector as an optimization taboo and re-execute population evolution optimization.

[0076] Furthermore, the error correction module 30 is used to perform the following operation steps:

[0077] The first pseudorange compensation amount and the first phase compensation amount are mapped to perform algebraic superposition correction of the first original pseudorange observation value and the first original carrier phase in the first original observation value to obtain the first preliminary purified value; the first preliminary purified value is subjected to carrier Doppler-assisted repair under cycle slip detection to output the first purified observation value.

[0078] Furthermore, the positioning calculation module 40 is used to perform the following operation steps:

[0079] After assigning fixed high weights to the K purified observations and assigning dynamic weights based on signal-to-noise ratio and phase-locked loop bandwidth to the original observations of the remaining M normal channels in the multi-channel satellite signals, a robust iteration driven by standardized residuals is performed to output the high-precision position estimation result.

[0080] Furthermore, the probability value quantization module 10 is used to perform the following operation steps:

[0081] The pseudo-code despreading of the multiple satellite signals after carrier stripping yields multiple baseband in-phase signals and multiple orthogonal branch signals. Multiple instantaneous carrier-to-noise ratios (CNRs) are calculated based on these signals and compared with dynamically set CNR thresholds to eliminate invalid channels, resulting in P valid signal channels. Multidimensional feature extraction is performed on the P valid signal channels to obtain P four-dimensional feature vectors, which are input into a contamination probability classification model constructed based on a gradient boosting decision tree for contamination probability prediction calculation, outputting P contamination observation values. A preset contamination probability threshold is used to compare the P contamination observation values ​​to filter and obtain the K contamination channels.

[0082] Furthermore, the probability value quantization module 10 is used to perform the following operation steps:

[0083] Carrier phase tracking is performed on the P orthogonal branch signals of the P effective signal channels, the second-order differential variance of the phase is calculated, and P phase change rates are output. Phase-locked loop processing is performed based on the P orthogonal branch signals and the P baseband in-phase signals to output P Doppler frequency shift standard deviations. The P baseband in-phase signals of the P effective signal channels drive a three-channel correlator to calculate P correlation peak symmetries. Power synthesis is performed based on the P orthogonal branch signals and the P baseband in-phase signals, and reflection path matching is calculated using the carrier attitude matrix and a 3D environment map to output P reflection correlation degrees. The P phase change rates, P Doppler frequency shift standard deviations, P correlation peak symmetries, and P reflection correlation degrees are assembled into P four-dimensional feature vectors.

[0084] Furthermore, the initial joint parameter vector includes the code phase offset and signal attenuation factor of the direct wave, as well as the relative time delay, reflection loss rate, phase jump variable, and incident angle of the reflected wave.

[0085] Through the foregoing detailed description of the multipath error suppression method in high-precision positioning and navigation, those skilled in the art can clearly understand the multipath error suppression system in high-precision positioning and navigation in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to the method section.

[0086] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for suppressing multipath errors in high-precision positioning and navigation, characterized in that, The method includes: After performing multi-dimensional anomaly feature extraction on the received multi-channel satellite signals, the signal contamination probability value is quantified, and multi-channel contamination observation values ​​are output to filter out K contamination channels that do not meet the preset contamination probability threshold. The raw observations of the K pollution channels are subjected to a joint solution of direct and reflected wave parameters based on biomimetic optimization search to generate K multipath error compensation quantities. The K multipath error compensation values ​​are used to correct the errors in the original observation values ​​of the K pollution channels to obtain the K purified observation values. The K purified observation values ​​and the original observation values ​​of the remaining M normal channels in the multi-channel satellite signals are subjected to robust weighted positioning calculation to obtain a high-precision position estimation result; The high-precision position estimation result is used as prior information for spatial position. Dynamic constraints are applied to the biomimetic optimization search parameter space in subsequent epochs to output real-time navigation and positioning information.

2. The multipath error suppression method in high-precision positioning and navigation as described in claim 1, characterized in that, The method involves performing a joint solution of direct and reflected wave parameters based on a biomimetic optimization search on the original observations of the K contaminated channels to generate K multipath error compensation quantities. A multidimensional parameter space is constructed based on the parameter boundaries of direct waves and reflected waves; Using a preset minimum Euclidean distance as a dispersion constraint, N initial joint parameter vectors are initialized in the multidimensional parameter space; The N model observations of the N initial joint parameter vectors are calculated using the signal propagation model, and compared with the first original observations of the first contamination channel to obtain N initial fitness values. After selecting W initial elite solutions based on the ascending order of the N initial fitnesss, a Gaussian perturbation is introduced to generate W initial adjoint solutions; Using the W initial elite solutions and W initial adjoint solutions as the current population, and the first original observation value as the fitness benchmark, a population evolution process with iterative shrinkage of the search radius is performed in the multidimensional parameter space to obtain the target joint parameter vector; An error mapping function is introduced to calculate the first multipath error compensation amount of the target joint parameter vector.

3. The multipath error suppression method in high-precision positioning and navigation as described in claim 2, characterized in that, Using the W initial elite solutions and W initial adjoint solutions as the current population, and the first original observation value as the fitness benchmark, a population evolution process with iterative shrinking of the search radius is performed in the multidimensional parameter space to obtain the target joint parameter vector. The method includes: S1: Calculate the W model observations of the W initial adjoint solutions using the signal propagation model, compare them with the first original observations, and calculate the W first adjoint fitnesss; S2: By merging and sorting the W initial elite solutions and the W initial adjoint solutions, the top W updated elite solutions are selected in ascending fitness order; S3: Based on the fitness ranking of the W updated elite solutions in descending order, divide the optimal guiding solution and W-1 secondary elite solutions; S4: After using a preset shrinkage factor to dynamically reduce the search radius, with the optimal guiding solution as the core perturbation center and the W-1 secondary elite solutions as auxiliary perturbation centers, a Gaussian random walk with standard deviation graded is performed around the W updated elite solutions to generate W first iteration adjoint solutions; In iterations S1 to S4, a population evolution process of iterative shrinkage of the search radius is executed until the preset convergence condition is met, and the target joint parameter vector is output.

4. The multipath error suppression method in high-precision positioning and navigation as described in claim 2, characterized in that, The method involves calculating the first multipath error compensation amount of the target joint parameter vector by introducing an error mapping function, and includes: Separate the relative time delay optimization value, reflection optimization loss rate, and phase jump optimization amount from the target joint parameter vector; If the relative delay optimization value is greater than the preset delay threshold and the reflection optimization loss rate is less than the preset loss threshold, then the relative delay optimization value and the phase jump optimization amount are input in parallel into the error mapping sub-function and the phase error mapping sub-function of the error mapping function to calculate the first pseudorange compensation amount and the first phase compensation amount, wherein the first pseudorange compensation amount and the first phase compensation amount constitute the first multipath error compensation amount; If the relative delay optimization value is less than the preset delay threshold, and / or the reflection optimization loss rate is greater than the preset loss threshold, then after setting the target joint parameter vector as the optimization taboo, the population evolution optimization is re-executed.

5. The multipath error suppression method in high-precision positioning and navigation as described in claim 4, characterized in that, The method involves using the K multipath error compensation values ​​to correct the errors in the original observations of the K contaminated channels, thereby obtaining the K purified observations. The first pseudorange compensation amount and the first phase compensation amount are mapped to perform algebraic superposition correction of the first original pseudorange observation value and the first original carrier phase in the first original observation value to obtain the first preliminary purified value. The first preliminary purified value is subjected to carrier Doppler-assisted repair under cycle slip detection, and the first purified observation value is output.

6. The multipath error suppression method in high-precision positioning and navigation as described in claim 1, characterized in that, The method involves performing robust weighted positioning calculations on the K purified observations and the original observations from the remaining M normal channels of the multi-channel satellite signals to obtain a high-precision position estimation result. After assigning fixed high weights to the K purified observations and assigning dynamic weights based on signal-to-noise ratio and phase-locked loop bandwidth to the original observations of the remaining M normal channels in the multi-channel satellite signals, a robust iteration driven by standardized residuals is performed to output the high-precision position estimation result.

7. The multipath error suppression method in high-precision positioning and navigation as described in claim 1, characterized in that, After performing multi-dimensional anomaly feature extraction on the received multi-channel satellite signals, the signal contamination probability value is quantified, and multi-channel contamination observation values ​​are output to filter out K contamination channels that do not meet the preset contamination probability threshold. The method includes: After carrier stripping and pseudo-code despreading of the multiple satellite signals, multiple baseband in-phase signals and multiple quadrature branch signals are obtained. Based on the multiple baseband in-phase signals and multiple quadrature branch signals, multiple instantaneous carrier-to-noise ratios are calculated and compared with dynamically set carrier-to-noise ratio thresholds to eliminate failed channels and obtain P effective signal channels. Multidimensional feature extraction is performed on the P effective signal channels to obtain P four-dimensional feature vectors. These vectors are then input into a pollution probability classification model constructed based on a gradient boosting decision tree to perform pollution probability prediction calculations and output P pollution observation values. The P-path pollution observations are compared using a preset pollution probability threshold to filter and obtain the K-path pollution channels.

8. The multipath error suppression method in high-precision positioning and navigation as described in claim 7, characterized in that, Multidimensional feature extraction is performed on the P effective signal channels to obtain P four-dimensional feature vectors. The method includes: Carrier phase tracking is performed on the P orthogonal branch signals of the P effective signal channels, the second-order differential variance of the phase is calculated, and P phase change rates are output. Phase-locked loop processing is performed based on the P-path orthogonal branch signals and the P-path baseband in-phase signals to output P Doppler frequency shift standard deviations using statistical standard deviations; The symmetry of P correlation peaks is calculated by driving three correlators with the P baseband in-phase signals of the P effective signal channels. After performing power synthesis based on the P-path orthogonal branch signals and the P-path baseband in-phase signals, reflection path matching calculation is performed by combining the carrier attitude matrix and 3D environment map, and P reflection correlation degrees are output. The P phase abrupt change rates, P Doppler frequency shift standard deviations, P correlation peak symmetries, and P reflection correlation degrees are assembled into the P four-dimensional feature vectors.

9. The multipath error suppression method in high-precision positioning and navigation as described in claim 2, characterized in that, The initial joint parameter vector includes the code phase offset and signal attenuation factor of the direct wave, as well as the relative time delay, reflection loss rate, phase jump variable, and incident angle of the reflected wave.

10. A multipath error suppression system for high-precision positioning and navigation, characterized in that, The system is used to implement the high-precision positioning and navigation multipath error suppression method according to any one of claims 1-9, the system comprising: The probability value quantization module is used to extract multi-dimensional abnormal features from the received multi-channel satellite signals, quantize the signal contamination probability value, and output multi-channel contamination observation values ​​to filter out K contamination channels that do not meet the preset contamination probability threshold. The joint solution module is used to perform a joint solution of direct wave and reflected wave parameters based on biomimetic optimization search on the original observation values ​​of the K pollution channels, and generate K multipath error compensation quantities; An error correction module is used to correct the original observations of the K pollution channels using the K multipath error compensation values ​​to obtain K purified observations. The positioning calculation module is used to perform robust weighted positioning calculation on the K purified observation values ​​and the original observation values ​​of the remaining M normal channels in the multi-channel satellite signals to obtain a high-precision position estimation result; The dynamic constraint module is used to use the high-precision position estimation result as prior information of spatial position, apply dynamic constraints to the biomimetic optimization search parameter space of subsequent epochs, and output real-time navigation and positioning information.

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