A bearing measurement system and method under a complex environment of a scenic spot

Abstract

The application provides a direction measurement system and method in a complex scenic spot environment, relates to the technical field of direction measurement, and deploys multiple fixed reference points in a target scenic spot, establishes distance measurement interaction with a user terminal, obtains original distance data of the user terminal relative to each reference point, generates a relative motion trajectory segment by using a sensor built-in the user terminal to perceive inertia change in the user motion process, generates a candidate space sequence according to the original distance data and the relative motion trajectory segment, performs abnormal identification and elimination on the candidate space sequence, and outputs a corrected space position in combination with a scenic spot passing constraint atlas, calculates the direction of the user terminal according to the corrected space position and known coordinates of the reference points, and outputs a measurement result. The application can realize suppression of multipath and non-line-of-sight pollution in a complex scenic spot environment, and improve the accuracy and output reliability of direction measurement.

Description

Technical Field

[0001] This application relates to the field of orientation measurement technology, and more specifically, to an orientation measurement system and method for complex environments in scenic areas. Background Technology

[0002] Currently, orientation measurement in scenic areas mainly relies on technologies such as Global Navigation Satellite Systems (GNSS), inertial navigation, wireless ranging and positioning, geomagnetic positioning, and map matching-assisted positioning. GNSS can provide meter-level accuracy positioning services in open areas; inertial navigation utilizes the accelerometers and gyroscopes built into user terminals to achieve autonomous dead reckoning; wireless ranging and positioning technology uses ultra-wideband (UWB) and Bluetooth signals to measure the distance between the user terminal and a fixed reference point for multilateral positioning; geomagnetic positioning infers location by matching local geomagnetic fingerprints; and map matching technology constrains the positioning results to a road network to optimize the output.

[0003] In complex environments such as scenic canyons, dense forests, and ancient building complexes, existing global navigation satellite system signals are easily blocked or reflected, leading to positioning interruptions or significant errors. While inertial navigation is unaffected by external signals, it suffers from long-term integration drift. Ultra-wideband (UWB) and Bluetooth wireless ranging and positioning technologies are generally severely affected by multipath effects and non-line-of-sight (NLS) propagation. Signals are reflected by obstacles such as mountains, buildings, and trees before reaching the user terminal, resulting in measured distances significantly greater than the true distance, causing significant deviations or even divergence in positioning results. Therefore, suppressing multipath and NLS contamination in complex scenic environments and improving the accuracy and reliability of orientation measurements has become a major challenge for the industry. Summary of the Invention

[0004] This application provides a system and method for azimuth measurement in complex environments in scenic areas, which can suppress multipath and non-line-of-sight pollution in complex environments in scenic areas and improve the accuracy and reliability of azimuth measurement output.

[0005] Firstly, this application provides a method for azimuth measurement in complex environments within scenic areas, the method comprising the following steps:

[0006] Multiple fixed reference points are deployed within the target scenic area, and distance measurement interaction is established with the user terminal to obtain the original distance data of the user terminal relative to each reference point. The inertial changes during the user's movement are perceived by the built-in sensors of the user terminal, and relative motion trajectory segments are generated.

[0007] A candidate spatial sequence for the user terminal is generated based on the original distance data and the relative motion trajectory segments;

[0008] The candidate spatial sequences are anomaly identified and eliminated, and the corrected spatial positions are output by combining the scenic area access constraint map.

[0009] Based on the corrected spatial position and the known coordinates of the reference point, the orientation of the user terminal is calculated, and the measurement results are output.

[0010] In this embodiment, the process of using the built-in sensors of the user terminal to sense changes in inertia during the user's movement and generating a relative motion trajectory segment specifically includes:

[0011] The raw data of acceleration and angular velocity during pedestrian movement are continuously collected using the three-axis accelerometer and three-axis gyroscope built into the user terminal.

[0012] The raw data is subjected to low-pass filtering to extract the main frequency component of pedestrian motion;

[0013] Based on the periodic changes in the filtered acceleration and angular velocity magnitudes, the pedestrian motion is divided into a support phase and a swing phase in real time.

[0014] Within the support phase interval, the virtual contact moment between the foot and the ground is determined, and zero-velocity reset and angular velocity zeroing calibration are performed at the virtual contact moment;

[0015] Between two adjacent virtual contact moments, each displacement increment is determined, and all displacement increments are spliced ​​together in time sequence to generate a relative motion trajectory segment starting from the starting point.

[0016] In this embodiment, the segmentation of the support phase and the swing phase using a dual threshold hysteresis comparator specifically includes:

[0017] Set an entry threshold and an exit threshold, where the entry threshold is greater than the exit threshold;

[0018] When the acceleration modulus rises and crosses the entry threshold, it is determined that the phase has entered the support phase; when the acceleration modulus falls and crosses the exit threshold, it is determined that the phase has exited the support phase and entered the swing phase.

[0019] The difference between the entry threshold and the exit threshold forms the hysteresis interval.

[0020] In this embodiment, generating a candidate spatial sequence for the user terminal based on the original distance data and the relative motion trajectory segment specifically includes:

[0021] The time interval between adjacent sampling points in the relative motion trajectory segment is used as the basic advance step size, and a state forward deduction is triggered whenever a new inertial sampling point arrives.

[0022] The original distance data is used as a discrete observation event at the arrival time to trigger a state correction, and the correction magnitude is determined based on the residual between the original distance data and the current inferred position.

[0023] The time difference between the inertial sampling point and the original distance data is processed using a motion trend backtracking compensation method;

[0024] The results of all state deductions and state corrections are output sequentially in chronological order to generate a candidate space sequence for the user terminal.

[0025] In this embodiment, the motion trend backtracking compensation specifically includes:

[0026] The transmission time T1 is the time when the reference point is embedded in the ranging frame, and the user terminal records the reception time T2.

[0027] Starting from the current state of the user terminal at time T2, retrieve the inertial motion trajectory segments within the time interval from T1 to T2, and determine the displacement vector increment of the user terminal within the time interval;

[0028] The backtracking estimate of the user terminal's position at time T1 is determined based on the state deduction position at time T2;

[0029] The theoretical distance between the backtracking estimate and the coordinates of the reference point is compared with the measured distance, and this is used as a reference benchmark for calculating the distance residual.

[0030] In this embodiment, the anomaly identification and elimination of the candidate spatial sequences, combined with the output of the scenic area access constraint map to correct the spatial location, specifically includes:

[0031] Extract the ranging residual sequence between the current candidate position and each reference point from the candidate spatial sequence, and construct a cross-comparison matrix;

[0032] Determine the sequence of singular values ​​in the cross-comparison matrix and detect whether there are any singular value mutations that deviate from the normal distribution;

[0033] When the anomalous value change amplitude corresponding to the reference point exceeds a preset threshold, it is determined that the corresponding ranging channel is contaminated, and dynamic channel silencing is performed on the ranging channel.

[0034] A detection window is reopened for the silenced ranging channel, and its current ranging value is compared with the theoretical distance derived from the candidate spatial position. If the deviation between the two is less than the recovery threshold in multiple consecutive detection cycles, then the silence is lifted.

[0035] The candidate spatial locations after silent processing are spatially consistent with the scenic area access constraint map, and the corrected spatial locations that meet the constraint conditions are output.

[0036] In this embodiment, the scenic area access constraint map includes:

[0037] The first layer is the impenetrable boundary layer of the pedestrian topology network, which defines the physical boundaries that tourists cannot cross within the scenic area;

[0038] The second layer is the elevation-driven motion cost field, which determines the motion energy consumption cost in different directions based on digital elevation data.

[0039] The third layer is a probability map of signal traps based on historical abandoned positioning points, which statistically analyzes abandoned positioning point data generated during the historical positioning process.

[0040] In this embodiment, the calculation of the user terminal's orientation based on the corrected spatial position and the known coordinates of the reference point, and the output of the measurement results, specifically include:

[0041] Obtain the known coordinates of the corrected spatial position and each reference point, and determine the geometric azimuth angle of the corrected spatial position pointing to each reference point;

[0042] The geometric azimuth angle is weighted and fused with the health status of the ranging channel of each reference point and the geometric configuration weight to obtain the absolute azimuth angle of the user terminal.

[0043] The confidence interval envelope of the absolute azimuth is determined based on the number of reference points involved in the calculation, the geometric configuration, and the residual error of each ranging channel.

[0044] The absolute azimuth angle and the confidence interval envelope are output together as the final azimuth measurement result.

[0045] Secondly, this application provides a orientation measurement system for complex environments in scenic areas to perform an orientation measurement method in such environments. The orientation measurement system includes:

[0046] The data acquisition module is used to deploy multiple fixed reference points within the target scenic area and establish distance measurement interaction with the user terminal to obtain the raw distance data of the user terminal relative to each reference point. It also uses the built-in sensors of the user terminal to sense the inertial changes during the user's movement and generate relative motion trajectory segments.

[0047] The sequence generation module is used to generate a candidate spatial sequence for the user terminal based on the original distance data and the relative motion trajectory segment;

[0048] The position correction module is used to identify and eliminate anomalies in the candidate spatial sequence, and output the corrected spatial position in combination with the scenic area access constraint map;

[0049] The result output module is used to calculate the orientation of the user terminal based on the corrected spatial position and the known coordinates of the reference point, and output the measurement results.

[0050] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device performs the above-described method for directional measurement in a complex environment in a scenic area.

[0051] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0052] Multiple fixed reference points are deployed within the target scenic area, and distance measurement interaction is established with the user terminal to obtain the original distance data of the user terminal relative to each reference point. The inertial changes during the user's movement are sensed by the built-in sensors of the user terminal, and relative motion trajectory segments are generated. A candidate spatial sequence of the user terminal is generated based on the original distance data and the relative motion trajectory segments. Anomalies are identified and eliminated from the candidate spatial sequence, and the corrected spatial position is output in combination with the scenic area access constraint map. Based on the corrected spatial position and the known coordinates of the reference points, the orientation of the user terminal is calculated, and the measurement result is output.

[0053] Therefore, this application firstly establishes a ranging link by deploying fixed benchmarks within the scenic area and continuously collects pedestrian movement data using user terminals, providing two heterogeneous information sources for subsequent fusion positioning: raw distance data and relative motion trajectory fragments. This lays the data foundation for the accuracy and reliability of the final orientation measurement. Secondly, by adopting an asynchronous injection mechanism dominated by inertial update rhythm, the interference of signal interruptions and non-line-of-sight contamination on fusion positioning in the complex environment of the scenic area is suppressed, improving the continuity and accuracy of candidate spatial sequences and providing location sequence input for subsequent anomaly identification. Then, by constructing... A cross-comparison matrix is ​​used to identify the geometric antisymmetric features of non-line-of-sight contaminated channels. A dynamic channel silencing mechanism is employed to cut off the participation of contaminated data, preventing the continuous penetration of outliers. This suppresses multipath and non-line-of-sight contamination in complex scenic environments, eliminates the interference of abnormal ranging on positioning results, and improves the accuracy and reliability of azimuth measurement. Finally, by adopting a confidence-weighted multi-reference point joint solution mechanism, the influence of non-line-of-sight contaminated channels on the final solution results is suppressed, improving the calculation accuracy of absolute azimuth and enhancing the overall reliability and user experience of azimuth measurement in complex scenic environments.

[0054] In summary, the technical solution adopted in this application can suppress multipath and non-line-of-sight pollution in complex environments of scenic areas, and improve the accuracy and reliability of orientation measurement. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this embodiment of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is an exemplary flowchart of a method for azimuth measurement in complex environments in scenic areas, provided in this application.

[0057] Figure 2 This is a schematic diagram of a location measurement system in a complex environment of a scenic area, based on the application provided herein.

[0058] Figure 3 This is a modular structure diagram of the orientation measurement system in complex scenic environments provided in this application;

[0059] Figure 4 This is a schematic diagram of the structure of a computer device that implements a method for directional measurement in a complex environment of a scenic area, according to the present application. Detailed Implementation

[0060] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0061] This application provides a system and method for azimuth measurement in complex scenic environments. The core of the system involves deploying multiple fixed reference points within the target scenic area and establishing distance measurement interaction with a user terminal to acquire raw distance data of the user terminal relative to each reference point. The system utilizes sensors built into the user terminal to sense changes in inertia during user movement, generating relative motion trajectory segments. Based on the raw distance data and relative motion trajectory segments, a candidate spatial sequence for the user terminal is generated. Anomalies in the candidate spatial sequence are identified and eliminated, and a corrected spatial position is output based on the scenic area traffic constraint map. The azimuth direction of the user terminal is calculated based on the corrected spatial position and the known coordinates of the reference points, and the measurement result is output. This approach can suppress multipath and non-line-of-sight contamination in complex scenic environments, improving the accuracy and reliability of azimuth measurement.

[0062] Example 1: To better understand the above technical solution, the following will provide a detailed description of the technical solution in conjunction with the accompanying drawings and specific implementation methods. (Refer to...) Figure 1As shown in the figure, this is an exemplary flowchart of a method for azimuth measurement in a complex environment of a scenic area according to this embodiment of the present application. The method for azimuth measurement includes the following steps:

[0063] In step S1, multiple fixed reference points are deployed within the target scenic area, and a distance measurement interaction is established with the user terminal to obtain the original distance data of the user terminal relative to each reference point. The inertial changes during the user's movement are perceived by the built-in sensors of the user terminal, and a relative motion trajectory segment is generated.

[0064] In practical implementation, multiple fixed reference points are deployed within the target scenic area, and distance measurement interactions are established with user terminals to obtain raw distance data of the user terminal relative to each reference point. Specifically, multiple fixed locations are pre-selected within the target scenic area to deploy reference nodes. The locations of the reference nodes should cover the main pedestrian paths and complex areas prone to getting lost. The distance between adjacent reference points should be controlled within the range of 50 to 200 meters, and the reference points should be placed in locations that are not easily touched or obstructed by tourists. Each reference node integrates a spread spectrum ranging transceiver, a local clock source, a pseudo-random phase disturbance generator, and a solar-powered supplementary power supply unit. After deployment, the reference nodes undergo initial calibration, recording their latitude and longitude coordinates and local geomagnetic offset values. After entering the scenic area, the user terminal scans for nearby available reference nodes via wireless broadcast. Once the user terminal establishes a link with at least one reference node, the two parties interact according to a predetermined distance measurement frame structure. The pseudo-random phase perturbation generator at the reference node generates a set of pseudo-random phase perturbation templates bound to the current timeslot number. This pseudo-random phase perturbation template is a predefined phase sequence with a length equal to the spreading code length of the ranging signal. Different reference nodes use mutually orthogonal perturbation code sets to reduce mutual interference. The reference node multiplies the baseband ranging signal with the phase perturbation templates chip-by-chip to achieve phase modulation. The modulated signal is then up-converted and transmitted via an antenna. A data frame is also embedded in the transmitted signal. This data frame contains: the unique identifier of the reference node, the current transmission time, the coordinate information of the reference node, and the index of the currently used phase perturbation template. The user terminal continuously monitors the ranging channel, and when it receives a ranging frame, it records the local reception time.

[0065] In addition, in specific implementation, the user terminal generates a phase perturbation template locally that is identical to that of the reference node based on the received phase perturbation template index. The received complex baseband signal is then input into a matched descrambling filter, whose impulse response matches the conjugate time reversal of the phase perturbation template. After matched descrambling, the output waveform of the signal exhibits the following characteristics: the direct path signal, due to the perfect match between the phase perturbation template and the local template, has highly concentrated correlation energy, forming a sharp correlation peak. The user terminal detects the position of the maximum peak in the descrambled correlation waveform; this position corresponds to the arrival time of the direct path. To eliminate spurious peaks caused by noise, the user terminal also calculates the peak-to-average power ratio (PAPR) and peak width of the correlation waveform. When the PAPR is greater than a preset threshold and the peak width is less than a preset width, the currently detected peak is determined to be the arrival time of the direct wave; otherwise, the ranging measurement is deemed severely contaminated by multipath interference, and the ranging data is discarded. Through the above phase descrambling process, the user terminal extracts a reliable time-of-arrival estimate from the received signal. The user terminal calculates the raw distance measurement value based on the speed of light and the time of arrival, thereby obtaining the raw distance data between the user terminal and each visible reference node. Each raw distance data includes: reference node identifier, distance measurement value, measurement timestamp, and peak-to-average ratio obtained from the descrambling process.

[0066] In this embodiment, the inertial changes during the user's movement are detected by the sensors built into the user terminal, and a relative motion trajectory segment is generated. Specifically, this can be achieved through the following steps:

[0067] The raw data of acceleration and angular velocity during pedestrian movement are continuously collected using the three-axis accelerometer and three-axis gyroscope built into the user terminal.

[0068] The raw data is subjected to low-pass filtering to extract the main frequency component of pedestrian motion;

[0069] Based on the periodic changes in the filtered acceleration and angular velocity magnitudes, the pedestrian motion is divided into a support phase and a swing phase in real time.

[0070] Within the support phase interval, the virtual contact moment between the foot and the ground is determined, and zero-velocity reset and angular velocity zeroing calibration are performed at the virtual contact moment;

[0071] Between two adjacent virtual contact moments, each displacement increment is determined, and all displacement increments are spliced ​​together in time sequence to generate a relative motion trajectory segment starting from the starting point.

[0072] In practical implementation, firstly, the user terminal's built-in three-axis accelerometer and three-axis gyroscope continuously collect raw data on the acceleration and angular velocity of the pedestrian during movement; that is, the user terminal's built-in three-axis accelerometer and three-axis gyroscope continuously collect raw data on the acceleration and angular velocity of the pedestrian during movement. Secondly, the raw data is low-pass filtered to extract the dominant frequency component of the pedestrian's motion; that is, a Butterworth low-pass filter with a cutoff frequency of 5Hz is used to filter the raw data to extract the dominant frequency component of the pedestrian's motion, and the magnitude of acceleration and angular velocity are calculated after filtering. Next, based on the periodic changes in the filtered acceleration and angular velocity magnitudes, the pedestrian motion is divided into a support phase and a swing phase in real time. This process will be detailed in subsequent steps. Then, within the support phase, the virtual contact moment between the foot and the ground is determined, and zero-velocity reset and angular velocity zeroing calibration are performed at the virtual contact moment. Specifically, within the support phase, local minimum points of the acceleration and angular velocity magnitudes are detected, and the intersection of these two points (i.e., when the time difference between the two minimum points is less than a preset window width) is taken as the weighted average moment, which is used as the virtual contact moment. If there is no effective intersection between the two points within the current gait cycle, the local minimum point of acceleration is used as the virtual contact moment. At the detected virtual contact moment, both zero-velocity reset and angular velocity zeroing calibration are performed simultaneously. The zero-velocity reset adopts a damped reset strategy that is not completely zeroed, i.e., the velocity integral value is multiplied by a decay factor to partially zero it rather than completely zeroing it. The attenuation factor is dynamically adjusted based on the estimated slope of the current terrain in the scenic area. For example, the attenuation factor is set to 0.3 to 0.5 when going uphill to preserve the climbing speed trend, 0.5 to 0.7 when going downhill, and 0.7 to 0.9 on flat surfaces to strengthen zero-velocity constraints. The angular velocity zeroing calibration uses a time window mean estimation method. That is, taking the angular velocity samples within a 0.1-second time window before and after the virtual contact moment as the center, the arithmetic mean of these samples is calculated as the zero-bias estimate of the gyroscope. Before the subsequent oscillation phase integration, the original angular velocity value is subtracted from this zero-bias estimate. Finally, between two adjacent virtual contact moments, each displacement increment is determined, and all displacement increments are time-sequentially spliced ​​to generate a relative motion trajectory segment starting from the starting point. That is, between two adjacent virtual contact moments, the acceleration data is integrated twice to obtain the displacement increment. Before integration, the gravity component needs to be subtracted, and the velocity value after zero-velocity reset is used as the initial velocity condition for each integration segment. After each number of gait cycles, residual drift compensation is performed. This involves statistically analyzing the residual velocity error sequence at each zero-velocity reset moment within the sliding window, fitting the low-frequency trend term of this sequence, and then using this low-frequency trend term as the velocity drift compensation function to inversely superimpose it into the subsequent inertial integration process. All displacement increments are accumulated and stitched together in chronological order to generate a continuous relative motion trajectory segment starting from the initial point.

[0073] In this embodiment, the segmentation of the support phase and the swing phase is achieved using a dual threshold hysteresis comparator, which can be implemented through the following steps:

[0074] Set an entry threshold and an exit threshold, where the entry threshold is greater than the exit threshold;

[0075] When the acceleration modulus rises and crosses the entry threshold, it is determined that the phase has entered the support phase; when the acceleration modulus falls and crosses the exit threshold, it is determined that the phase has exited the support phase and entered the swing phase.

[0076] The difference between the entry threshold and the exit threshold forms the hysteresis interval.

[0077] In practical implementation, firstly, an entry threshold and an exit threshold are preset, with the entry threshold being greater than the exit threshold. These thresholds can be preset based on experimental data analysis. Secondly, when the acceleration modulus rises above the entry threshold, it is determined that the system has entered the support phase; when the acceleration modulus falls below the exit threshold, it is determined that the system has exited the support phase and entered the swing phase. That is, a current phase variable is maintained, with the initial state set as the swing phase (foot off the ground). Changes in the acceleration modulus are monitored in real time. When the current phase is the swing phase, and the acceleration modulus gradually rises from below and crosses the entry threshold (i.e., changes from less than the entry threshold to greater than the entry threshold), it is determined that the system has entered the support phase. The initial phase is the support phase, and the acceleration modulus gradually decreases from above and crosses the exit threshold (i.e., changes from being greater than the exit threshold to being less than the exit threshold). At this point, the support phase is exited, and the swing phase begins. Finally, the difference between the entry and exit thresholds forms a hysteresis interval. That is, after the acceleration modulus first impacts and crosses the entry threshold, the support phase begins. Even if the acceleration modulus falls back between two impacts, as long as its value remains above the exit threshold, the support phase remains unchanged, and the foot will not erroneously exit due to a temporary drop. Only when the acceleration modulus truly falls below the exit threshold—that is, the foot completes the entire support phase and begins to leave the ground—is the foot considered to have exited the support phase and entered the swing phase. Similarly, during the swing phase, the acceleration modulus may experience minor fluctuations due to foot swing, but as long as these fluctuations do not reach the entry threshold, the swing phase remains unchanged.

[0078] In step S2, a candidate spatial sequence for the user terminal is generated based on the original distance data and the relative motion trajectory segment.

[0079] In this embodiment, the candidate spatial sequence of the user terminal is generated based on the original distance data and the relative motion trajectory segment, which can be achieved by the following steps:

[0080] The time interval between adjacent sampling points in the relative motion trajectory segment is used as the basic advance step size, and a state forward deduction is triggered whenever a new inertial sampling point arrives.

[0081] The original distance data is used as a discrete observation event at the arrival time to trigger a state correction, and the correction magnitude is determined based on the residual between the original distance data and the current inferred position.

[0082] The time difference between the inertial sampling point and the original distance data is processed using a motion trend backtracking compensation method;

[0083] The results of all state deductions and state corrections are output sequentially in chronological order to generate a candidate space sequence for the user terminal.

[0084] In practice, firstly, the time interval between adjacent sampling points in the relative motion trajectory segment is used as the basic propagation step size. Each time a new inertial sampling point arrives, a state forward calculation is triggered. That is, the time interval between adjacent sampling points in the relative motion trajectory segment is used as the basic propagation step size. Since the sampling frequency of inertial sensors is usually high, the time interval between adjacent sampling points is 5 to 10 milliseconds, and this time interval is used as the basic rhythm for operation. Each time a new inertial sampling point arrives, a state forward calculation is automatically triggered. The state includes: the user terminal's current position, current speed, and current heading angle. The state derivation employs standard inertial navigation mechanical arrangement equations. This involves using the current acceleration and angular velocity data, combined with the state value from the previous moment, to deduce the new state value through integration. Secondly, the arrival of the original distance data triggers a state correction as a discrete observation event. The correction magnitude is determined by the residual between the original distance data and the current derivation position. Specifically, the theoretical distance is calculated based on the coordinates of the current derivation position and the corresponding reference point. The difference between the theoretical and measured distances is then used as the residual, which is used to correct the current state through a Kalman filter update equation, yielding the corrected state value. Next, the time difference between the inertial sampling point and the original distance data is compensated using a motion trend backtracking method. This process will be detailed in subsequent steps. Finally, all state derivation and correction results are output sequentially in chronological order, generating a candidate spatial sequence for the user terminal. Each output moment corresponds to a state update moment, and each output state includes the user terminal's position coordinates, velocity vector, heading angle, and an uncertain ellipsoidal parameter at that moment. The uncertainty ellipsoid parameters characterize the uncertainty of the current state estimate. During the pure inertial extrapolation phase, the ellipsoid gradually expands over time according to the inertial error accumulation model. After receiving distance measurement data and performing state corrections, the ellipsoid contracts according to the geometric constraints of the distance observations, and the direction and intensity of the contraction depend on the geometric configuration of the reference point. This outputs a sequence of candidate spatial positions starting from the initial moment.

[0085] In this embodiment, the motion trend backtracking compensation can be implemented using the following steps:

[0086] The transmission time T1 is the time when the reference point is embedded in the ranging frame, and the user terminal records the reception time T2.

[0087] Starting from the current state of the user terminal at time T2, retrieve the inertial motion trajectory segments within the time interval from T1 to T2, and determine the displacement vector increment of the user terminal within the time interval;

[0088] The backtracking estimate of the user terminal's position at time T1 is determined based on the state deduction position at time T2;

[0089] The theoretical distance between the backtracking estimate and the coordinates of the reference point is compared with the measured distance, and this is used as a reference benchmark for calculating the distance residual.

[0090] In the specific implementation, firstly, the transmission time T1 of the reference point is embedded in the ranging frame, and the user terminal records the reception time T2. That is, when the reference node transmits the ranging signal, it embeds the current transmission time T1 into the data payload of the ranging frame, with T1 based on the local clock of the reference node. Upon receiving the ranging frame, the user terminal immediately records the local reception time T2, with T2 based on the user terminal's local clock. Next, starting from the user terminal's current state projection position at time T2, it retrieves inertial motion trajectory segments within the time interval from T1 to T2 to determine the displacement vector increment of the user terminal within that time interval. Specifically, within the time interval from T1 to T2, it searches for all inertial sampling points whose timestamps fall within this interval. Since this time interval is short, it may contain only 1 to 2 inertial sampling points. Linear interpolation is used to fit the motion trajectory within this interval, calculating the displacement vector increment from the start to the end of the interval. Then, using the state projection position at time T2, it determines the retrospective estimate of the user terminal's position at time T1. Specifically, it subtracts the displacement vector increment from the state projection position at time T2 to obtain the retrospective estimate of the user terminal's position at time T1. The physical meaning of the backtracking estimate is: based on the predicted position at time T2, the most likely position of the user terminal at time T1 is inferred. It should be noted that the backtracking estimate is not directly output as the positioning result, but only used as a reference benchmark for calculating the distance residual. Finally, the theoretical distance between the backtracking estimate and the benchmark coordinates is compared with the measured distance, and this is used as the reference benchmark for calculating the distance residual. That is, the Euclidean distance between the backtracking estimate and the known coordinates of the benchmark is taken as the theoretical distance. The measured distance is then compared with the theoretical distance to calculate the distance residual, which is equal to the measured distance minus the theoretical distance.

[0091] In step S3, anomaly identification and elimination are performed on the candidate spatial sequence, and the corrected spatial position is output in combination with the scenic area access constraint map.

[0092] In this embodiment, the candidate spatial sequence is anomaly identified and eliminated, and the spatial position is corrected by combining the output of the scenic area access constraint map. Specifically, this can be achieved through the following steps:

[0093] Extract the ranging residual sequence between the current candidate position and each reference point from the candidate spatial sequence, and construct a cross-comparison matrix;

[0094] Determine the sequence of singular values ​​in the cross-comparison matrix and detect whether there are any singular value mutations that deviate from the normal distribution;

[0095] When the anomalous value change amplitude corresponding to the reference point exceeds a preset threshold, it is determined that the corresponding ranging channel is contaminated, and dynamic channel silencing is performed on the ranging channel.

[0096] A detection window is reopened for the silenced ranging channel, and its current ranging value is compared with the theoretical distance derived from the candidate spatial position. If the deviation between the two is less than the recovery threshold in multiple consecutive detection cycles, then the silence is lifted.

[0097] The candidate spatial locations after silent processing are spatially consistent with the scenic area access constraint map, and the corrected spatial locations that meet the constraint conditions are output.

[0098] In the specific implementation, firstly, the distance residual sequence between the current candidate position and each reference point is extracted from the candidate spatial sequence, and a cross-comparison matrix is ​​constructed. That is, let the current candidate position be P, and there are m visible reference points. For the i-th reference point, its known coordinates are Ri, the measured distance is ri, and the theoretical distance is di, which is equal to the modulus of the difference between P and Ri. Then, the distance residual ei is equal to ri minus di. A cross-comparison matrix M is constructed, which is used to characterize the geometric consistency between the theoretical distance and the actual distance. The cross-comparison matrix M is an m-row, 2-column matrix, and each row contains the residual information of the current reference point and the sum of the theoretical distance and the measured distance. Secondly, the singular value sequence of the cross-comparison matrix is ​​determined, and the existence of singular value abrupt changes that deviate from the normal distribution is detected. That is, the singular value sequence of the cross-comparison matrix M is calculated. For an m-row, 2-column matrix, the number of singular values ​​is 2, which are the first singular value and the second singular value. Another historical sliding window is maintained, storing the sequence of singular values ​​for the most recent N moments. The mean and standard deviation of each singular value within the window are calculated. The singular value at the current moment is compared with the historical mean, and the standardized deviation is calculated. When the deviation of a singular value from the historical mean exceeds a preset threshold, a singular value mutation is identified. Furthermore, when the magnitude of the singular value mutation corresponding to a reference point exceeds a preset threshold, the corresponding ranging channel is identified as contaminated, and dynamic channel silencing is implemented for that ranging channel. That is, after a singular value mutation is detected, the ranging data of each reference point is temporarily removed in sequence, the cross-comparison matrix and its singular values ​​are recalculated, and it is observed whether the singular value mutation disappears. If the singular values ​​return to normal after removing a reference point, the ranging channel corresponding to that reference point is identified as contaminated. Once a ranging channel is identified as contaminated, dynamic channel silencing is implemented for that channel, that is, the permission for its ranging data to enter subsequent processing is temporarily cut off.

[0099] In practice, a new detection window is reopened for the silenced ranging channel. Its current ranging value is compared with the theoretical distance derived from the candidate spatial location. If the deviation is less than the recovery threshold for multiple consecutive detection cycles, the silence is lifted. Specifically, a new detection window is reopened for the silenced ranging channel, and in each detection cycle, the current ranging value is compared with the theoretical distance derived from the current candidate spatial location, calculating the deviation. If the deviation is less than the preset recovery threshold for multiple consecutive detection cycles, the channel is considered to have recovered, the silence is lifted, and its ranging data participation is restored. If any detection fails during the detection window (i.e., the deviation exceeds the recovery threshold), the consecutive success count is reset, and the silence period is extended. Simultaneously, the pollution credit score of this benchmark point is recorded, accumulating 1 point for each silent event. When the credit score reaches 5 points, an operation and maintenance alarm is triggered, prompting scenic area management personnel to inspect the benchmark point. The recovery threshold can be set to 10% of the theoretical distance, and the length of the detection window can be 5 to 10 consecutive ranging cycles, which can be preset by expert advice. Finally, the candidate spatial locations after silent processing are spatially consistentally projected onto the scenic area access constraint map, outputting the corrected spatial locations that meet the constraints. That is, the scenic area access constraint map contains a three-layer structure: the first layer is the impenetrable boundary layer of the walking topology network, the second layer is the elevation-driven motion cost field, and the third layer is the signal trap probability map based on historical abandoned positioning points. The spatial consistency projection adopts a progressive constraint method. In the first layer, the candidate spatial location is projected onto the impenetrable boundary layer with a penalty function. If the candidate location falls into the impassable area, i.e., outside the boundary, it is pulled back to the inside of the boundary along the gradient direction. The second layer searches for a local minimum cost point within the neighborhood of the current location after boundary constraints, using the cumulative movement cost from the current location to the candidate location in the motion cost field as the path cost function. This ensures the corrected location conforms to the terrain's motion energy consumption patterns. The third layer, if the minimum cost point falls into a high-probability region of the signal trap probability map, shifts it to the grid cell with the lowest probability value in its neighborhood, with the shift distance not exceeding one grid cell. The location after these three layers of processing is output as the final corrected spatial location.

[0100] In this embodiment, the scenic area access constraint map includes:

[0101] The first layer is the impenetrable boundary layer of the pedestrian topology network, which defines the physical boundaries that tourists cannot cross within the scenic area;

[0102] The second layer is the elevation-driven motion cost field, which determines the motion energy consumption cost in different directions based on digital elevation data.

[0103] The third layer is a probability map of signal traps based on historical abandoned positioning points, which statistically analyzes abandoned positioning point data generated during the historical positioning process.

[0104] In its implementation, the scenic area access constraint map is a pre-built multi-layered prior information database used to impose physical, kinematic, and statistical constraints on candidate spatial locations. First, the first layer is the impenetrable boundary layer of the pedestrian topology network, defining the physical boundaries that tourists cannot cross within the scenic area. This is achieved by extracting vector lines of the pedestrian road network and various boundaries from the scenic area's GIS data, and buffering these vector lines inwards by a set distance to form prohibited crossing boundaries. In the raster map, grids within a set distance inside the boundary are marked as passable, while grids outside the boundary are marked as impassable. Impassable grids are assigned an infinite penalty function value during location correction to ensure that candidate locations do not fall into restricted areas. Then, the second layer is an elevation-driven motion cost field, which determines the energy consumption cost of movement in different directions based on digital elevation data. Specifically, it involves acquiring the scenic area's digital elevation model data and calculating the energy consumption per unit distance traveled in eight directions (east, south, west, north, northeast, southeast, northwest, and southwest) at each grid point. Energy consumption in each direction is stored as an eight-dimensional cost vector attached to each grid. Finally, the third layer is a signal trap probability map based on historical abandoned positioning points. The abandoned positioning point data generated during the historical positioning process are statistically analyzed. That is, the scenic area is divided into grids of fixed size, and the frequency of occurrence of abandoned positioning points in each grid is counted. The ratio of this frequency to the total number of positioning times is used as the initial trap probability of the grid. Then, a continuously distributed signal trap probability map is generated after Gaussian kernel smoothing.

[0105] In step S4, the orientation of the user terminal is calculated based on the corrected spatial position and the known coordinates of the reference point, and the measurement result is output.

[0106] In this embodiment, the orientation of the user terminal is calculated based on the corrected spatial position and the known coordinates of the reference point, and the measurement result is output. This can be achieved through the following steps:

[0107] Obtain the known coordinates of the corrected spatial position and each reference point, and determine the geometric azimuth angle of the corrected spatial position pointing to each reference point;

[0108] The geometric azimuth angle is weighted and fused with the health status of the ranging channel of each reference point and the geometric configuration weight to obtain the absolute azimuth angle of the user terminal.

[0109] The confidence interval envelope of the absolute azimuth is determined based on the number of reference points involved in the calculation, the geometric configuration, and the residual error of each ranging channel.

[0110] The absolute azimuth angle and the confidence interval envelope are output together as the final azimuth measurement result.

[0111] In practical implementation, the known coordinates of the corrected spatial position and each reference point are obtained. The geometric azimuth angles from the corrected spatial position to each reference point are then determined. Specifically, assuming the current corrected spatial position is P, and there are multiple visible reference points, for each reference point, the components of the vector pointing from the corrected spatial position to that reference point in the east and north directions are calculated. This is achieved by subtracting the east coordinates of the corrected spatial position from the east coordinates of the reference point, and by subtracting the north coordinates of the corrected spatial position from the north coordinates of the reference point. The geometric azimuth angle of this vector is then calculated using the four-quadrant arctangent function. This geometric azimuth angle represents the angle between the direction from the corrected spatial position to the reference point and true north. This yields the geometric azimuth angles from the corrected spatial position to each reference point. It should be noted that since there is a fixed angular difference between the absolute azimuth angle of the user terminal and the aforementioned geometric azimuth angles (i.e., the angle between the user terminal's orientation and true north), the azimuth angle of the user terminal cannot be directly determined using only a single reference point. Therefore, it is necessary to jointly calculate the geometric azimuth angles of multiple reference points to determine the absolute azimuth angle of the user terminal. In this embodiment, the azimuth angle of the user terminal is inferred by using the geometric relationship between the geometric azimuth angles of multiple reference points. Secondly, the geometric azimuth angle is weighted and fused using the ranging channel health status and geometric configuration weights of each reference point to obtain the absolute azimuth angle of the user terminal. Specifically, the ranging channel health status is comprehensively evaluated by the following factors: the number of times the ranging channel is silenced during anomaly identification and elimination, the duration of the most recent successful recovery check, and the historical variance of the current ranging residual. That is, the fewer times a ranging channel is silenced, the better its health status; the shorter the duration of the most recent successful recovery check, the higher its reliability; and the smaller the historical variance, the better its stability. These three factors are quantified into a health factor through weighted summation, with a value ranging from 0 to 1, where 1 indicates complete health and 0 indicates complete unavailability. The geometric configuration weight is determined by the uniformity of the angle between the corrected spatial location and each reference point. Specifically, it calculates the uniformity of the distribution of the azimuth angles of each reference point relative to the corrected spatial location; the more uniform the azimuth angle distribution, the higher the weight. When there are narrow angles, the weight of the corresponding reference point is reduced. The absolute azimuth angle of the user terminal is obtained through a weighted average formula: the estimated azimuth angle of the user terminal calculated individually for each reference point is multiplied by its corresponding weight, summed, and then divided by the sum of the weights. The weight of each reference point is equal to its health factor multiplied by its geometric configuration weight. When the health factor of a reference point is too low, its weight is reset to zero, meaning it does not participate in the fusion calculation.

[0112] Furthermore, in the specific implementation, the confidence interval envelope of the absolute azimuth is determined based on the number of reference points involved in the calculation, the geometric configuration, and the residual errors of each ranging channel. That is, the confidence interval envelope adopts an asymmetric form, with its upper and lower limits calculated independently. The upper limit depends on the spatial spread of the reference point geometry and the maximum ranging residual, while the lower limit depends on the convergence margin of the inertial sensor's zero-bias estimator and the minimum ranging residual. The spatial spread is the weighted product of the minimum opening angle between each reference point and the line connecting the corrected spatial position, and the uniformity entropy. When the number of reference points involved in the calculation is less than three, the confidence interval envelope is automatically expanded, with the specific multiplier inversely proportional to the number of reference points: doubled when there are two reference points, tripled when there is one reference point, and a low-confidence marker is added to the output. When the confidence interval envelope of the absolute azimuth exceeds 15 degrees, the current positioning conditions are considered poor; when it exceeds 30 degrees, the positioning conditions are considered severe, triggering a corresponding user prompt. Finally, the absolute azimuth and the confidence interval envelope are output together as the final azimuth measurement result. The output is visual, with the absolute azimuth displayed as a digital pointer on the user's screen. When the confidence interval envelope exceeds 15 degrees, a sector of the confidence interval is displayed on the interface, with color gradients indicating different levels of reliability in different directions. The darkest color in the center of the sector indicates the highest reliability, while the color gradually fades at the edges, indicating decreasing reliability. When the confidence interval envelope exceeds 30 degrees or a low-confidence marker is triggered, an automatic voice prompt is output.

[0113] like Figure 2 The diagram shows a schematic of the orientation measurement system for complex environments in scenic areas provided in this application. The system includes a user terminal, a group of fixed reference points, and a scenic area access constraint map database. The user terminal interacts with the group of fixed reference points wirelessly to obtain raw distance data. The user terminal integrates an inertial sensor to collect motion data. The scenic area access constraint map database provides prior constraint information for correcting the positioning results.

[0114] Therefore, this application firstly establishes a ranging link by deploying fixed benchmarks within the scenic area and continuously collects pedestrian movement data using user terminals, providing two heterogeneous information sources for subsequent fusion positioning: raw distance data and relative motion trajectory fragments. This lays the data foundation for the accuracy and reliability of the final orientation measurement. Secondly, by adopting an asynchronous injection mechanism dominated by inertial update rhythm, the interference of signal interruptions and non-line-of-sight contamination on fusion positioning in the complex environment of the scenic area is suppressed, improving the continuity and accuracy of candidate spatial sequences and providing location sequence input for subsequent anomaly identification. Then, by constructing... A cross-comparison matrix is ​​used to identify the geometric antisymmetric features of non-line-of-sight contaminated channels. A dynamic channel silencing mechanism is employed to cut off the participation of contaminated data, preventing the continuous penetration of outliers. This suppresses multipath and non-line-of-sight contamination in complex scenic environments, eliminates the interference of abnormal ranging on positioning results, and improves the accuracy and reliability of azimuth measurement. Finally, by adopting a confidence-weighted multi-reference point joint solution mechanism, the influence of non-line-of-sight contaminated channels on the final solution results is suppressed, improving the calculation accuracy of absolute azimuth and enhancing the overall reliability and user experience of azimuth measurement in complex scenic environments.

[0115] In summary, the technical solution adopted in this application can suppress multipath and non-line-of-sight pollution in complex environments of scenic areas, and improve the accuracy and reliability of orientation measurement.

[0116] Example 2: This application provides a reference system for orientation measurement in complex environments such as scenic areas. Figure 3 As shown in the figure, this is a modular structure diagram of a orientation measurement system in a complex scenic environment according to this embodiment of the present application. The orientation measurement system includes:

[0117] The data acquisition module 100 is used to deploy multiple fixed reference points within the target scenic area, establish distance measurement interaction with the user terminal, acquire the original distance data of the user terminal relative to each reference point, and use the built-in sensor of the user terminal to sense the inertial changes during the user's movement to generate relative motion trajectory segments.

[0118] The sequence generation module 200 is used to generate a candidate spatial sequence for the user terminal based on the original distance data and the relative motion trajectory segment;

[0119] The position correction module 300 is used to identify and eliminate anomalies in the candidate spatial sequence and output the corrected spatial position in combination with the scenic area access constraint map.

[0120] The result output module 400 is used to calculate the orientation of the user terminal based on the corrected spatial position and the known coordinates of the reference point, and output the measurement results.

[0121] The foregoing detailed an example of a location measurement system and method in a complex scenic environment provided by embodiments of this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] In embodiment three, this application also provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-described method for directional measurement in a complex environment in a scenic area.

[0123] In this embodiment, reference Figure 4 The dashed lines in the figure indicate that the unit or module is optional. This figure is a structural schematic diagram of a computer device for a location measurement system in a complex scenic environment according to an embodiment of this application. The location measurement method described above in the above embodiment for a complex scenic environment can be... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 501, a memory 502 and at least one communication unit 505. The computer device may be a terminal device, a server or a chip.

[0124] Processor 501 can be a general-purpose processor or a special-purpose processor. For example, processor 501 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 505 to realize signal input (reception) and output (transmission).

[0125] For example, the computer device may be a chip, and the communication unit 505 may be the input and / or output circuit of the chip, or the communication unit 505 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0126] For example, the computer device may be a terminal device or a server, and the communication unit 505 may be a transceiver of the terminal device or the server, or the communication unit 505 may be a transceiver circuit of the terminal device or the server.

[0127] The computer device may include one or more memories 502 storing a program 504. The program 504 can be executed by a processor 501 to generate instructions 503, causing the processor 501 to perform the methods described in the above method embodiments according to the instructions 503. Optionally, the memory 502 may also store data (such as a target audit model). Optionally, the processor 501 may also read data stored in the memory 502, which may be stored at the same storage address as the program 504, or the data may be stored at a different storage address than the program 504.

[0128] The processor 501 and memory 502 can be configured separately or integrated together, for example, integrated on the system-on-chip (SOC) of the terminal device.

[0129] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in processor 501. Processor 501 can be a central processing unit, digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA), or other programmable logic device, such as discrete gate, transistor logic device, or discrete hardware component.

[0130] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0131] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0132] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for azimuth measurement in complex environments of scenic areas, characterized in that, The orientation measurement method includes: Multiple fixed reference points are deployed within the target scenic area, and distance measurement interaction is established with the user terminal to obtain the original distance data of the user terminal relative to each reference point. The inertial changes during the user's movement are perceived by the built-in sensors of the user terminal, and relative motion trajectory segments are generated. A candidate spatial sequence for the user terminal is generated based on the original distance data and the relative motion trajectory segments; The candidate spatial sequences are anomaly identified and eliminated, and the corrected spatial positions are output by combining the scenic area access constraint map. Based on the corrected spatial position and the known coordinates of the reference point, the orientation of the user terminal is calculated, and the measurement results are output.

2. The orientation measurement method in a complex scenic environment as described in claim 1, characterized in that, The process of using built-in sensors in the user terminal to detect changes in inertia during the user's movement and generating relative motion trajectory segments specifically includes: The raw data of acceleration and angular velocity during pedestrian movement are continuously collected using the three-axis accelerometer and three-axis gyroscope built into the user terminal. The raw data is subjected to low-pass filtering to extract the main frequency component of pedestrian motion; Based on the periodic changes in the filtered acceleration and angular velocity magnitudes, the pedestrian motion is divided into a support phase and a swing phase in real time. Within the support phase interval, the virtual contact moment between the foot and the ground is determined, and zero-velocity reset and angular velocity zeroing calibration are performed at the virtual contact moment; Between two adjacent virtual contact moments, each displacement increment is determined, and all displacement increments are spliced ​​together in time sequence to generate a relative motion trajectory segment starting from the starting point.

3. The orientation measurement method in a complex scenic environment as described in claim 2, characterized in that, The segmentation of the support phase and the swing phase using a dual threshold hysteresis comparator specifically includes: Set an entry threshold and an exit threshold, where the entry threshold is greater than the exit threshold; When the acceleration modulus rises and crosses the entry threshold, it is determined that the phase has entered the support phase; when the acceleration modulus falls and crosses the exit threshold, it is determined that the phase has exited the support phase and entered the swing phase. The difference between the entry threshold and the exit threshold forms the hysteresis interval.

4. The orientation measurement method in a complex environment of a scenic area as described in claim 1, characterized in that, The specific steps for generating a candidate spatial sequence for the user terminal based on the original distance data and relative motion trajectory segments include: The time interval between adjacent sampling points in the relative motion trajectory segment is used as the basic advance step size, and a state forward deduction is triggered whenever a new inertial sampling point arrives. The original distance data is used as a discrete observation event at the arrival time to trigger a state correction, and the correction magnitude is determined based on the residual between the original distance data and the current inferred position. The time difference between the inertial sampling point and the original distance data is processed using a motion trend backtracking compensation method; The results of all state deductions and state corrections are output sequentially in chronological order to generate a candidate space sequence for the user terminal.

5. The orientation measurement method in a complex environment of a scenic area as described in claim 4, characterized in that, The motion trend retrospective compensation specifically includes: The transmission time T1 is the time when the reference point is embedded in the ranging frame, and the user terminal records the reception time T2. Starting from the current state of the user terminal at time T2, retrieve the inertial motion trajectory segments within the time interval from T1 to T2, and determine the displacement vector increment of the user terminal within the time interval; The backtracking estimate of the user terminal's position at time T1 is determined based on the state deduction position at time T2; The theoretical distance between the backtracking estimate and the coordinates of the reference point is compared with the measured distance, and this is used as a reference benchmark for calculating the distance residual.

6. The orientation measurement method in a complex environment of a scenic area as described in claim 1, characterized in that, The process of identifying and eliminating anomalies in the candidate spatial sequences, and then correcting the spatial location based on the scenic area access constraint map, specifically includes: Extract the ranging residual sequence between the current candidate position and each reference point from the candidate spatial sequence, and construct a cross-comparison matrix; Determine the sequence of singular values ​​in the cross-comparison matrix and detect whether there are any singular value mutations that deviate from the normal distribution; When the anomalous value change amplitude corresponding to the reference point exceeds a preset threshold, it is determined that the corresponding ranging channel is contaminated, and dynamic channel silencing is performed on the ranging channel. A detection window is reopened for the silenced ranging channel, and its current ranging value is compared with the theoretical distance derived from the candidate spatial position. If the deviation between the two is less than the recovery threshold in multiple consecutive detection cycles, then the silence is lifted. The candidate spatial locations after silent processing are spatially consistent with the scenic area access constraint map, and the corrected spatial locations that meet the constraint conditions are output.

7. The orientation measurement method in a complex environment of a scenic area as described in claim 6, characterized in that, The scenic area access constraint map includes: The first layer is the impenetrable boundary layer of the pedestrian topology network, which defines the physical boundaries that tourists cannot cross within the scenic area; The second layer is the elevation-driven motion cost field, which determines the motion energy consumption cost in different directions based on digital elevation data. The third layer is a probability map of signal traps based on historical abandoned positioning points, which statistically analyzes abandoned positioning point data generated during the historical positioning process.

8. The orientation measurement method in a complex environment of a scenic area as described in claim 1, characterized in that, Based on the corrected spatial position and the known coordinates of the reference point, the orientation of the user terminal is calculated, and the measurement results are output, specifically including: Obtain the known coordinates of the corrected spatial position and each reference point, and determine the geometric azimuth angle of the corrected spatial position pointing to each reference point; The geometric azimuth angle is weighted and fused with the health status of the ranging channel of each reference point and the geometric configuration weight to obtain the absolute azimuth angle of the user terminal. The confidence interval envelope of the absolute azimuth is determined based on the number of reference points involved in the calculation, the geometric configuration, and the residual error of each ranging channel. The absolute azimuth angle and the confidence interval envelope are output together as the final azimuth measurement result.

9. A orientation measurement system for complex environments in scenic areas, used to perform the orientation measurement method for complex environments in scenic areas as described in any one of claims 1 to 8, characterized in that, The orientation measurement system includes: The data acquisition module is used to deploy multiple fixed reference points within the target scenic area and establish distance measurement interaction with the user terminal to obtain the raw distance data of the user terminal relative to each reference point. It also uses the built-in sensors of the user terminal to sense the inertial changes during the user's movement and generate relative motion trajectory segments. The sequence generation module is used to generate a candidate spatial sequence for the user terminal based on the original distance data and the relative motion trajectory segment; The position correction module is used to identify and eliminate anomalies in the candidate spatial sequence, and output the corrected spatial position in combination with the scenic area access constraint map; The result output module is used to calculate the orientation of the user terminal based on the corrected spatial position and the known coordinates of the reference point, and output the measurement results.

10. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device performs the orientation measurement method in a complex environment of a scenic area as described in any one of claims 1 to 8.

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