Unmanned aerial vehicle multi-mode navigation positioning method, device and equipment

Abstract

The invention provides an unmanned aerial vehicle multi-mode navigation positioning method, device and equipment, and the method comprises the steps: obtaining GPS, inertia and visual navigation mode positioning data through determining a target positioning precision index, and carrying out the evaluation to generate a multi-mode navigation state set; performing performance competition on each navigation mode based on the state set, establishing an inter-mode negotiation mechanism, and determining a mode priority sequence; establishing a navigation mode credibility matrix in combination with the priority sequence, and establishing an adaptive weight distribution sequence; analyzing a weight change trend and mode switching resource overhead, carrying out navigation mode dynamic screening through switching cost quantitative evaluation, and determining a dominant navigation mode and an auxiliary navigation mode; and performing frequency characteristic analysis on the dominant and auxiliary modes, determining a frequency domain fusion strategy based on a frequency coherence coefficient, generating a fusion positioning track, and outputting a high-precision positioning result through precision evaluation to realize intelligent multi-mode navigation positioning.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) navigation technology, and in particular to a multi-mode navigation and positioning method, device and equipment for an UAV. Background Art

[0002] With the rapid development of drone technology and the continuous expansion of its application areas, the requirements for drone navigation and positioning accuracy and reliability are increasing. Multi-mode drone navigation and positioning technology, by integrating multiple positioning modes such as GPS, inertial navigation, and visual navigation, can provide continuous and stable position information in complex environments. This technology has become a key technical support for autonomous drone flight.

[0003] However, existing multi-mode navigation and positioning methods suffer from numerous technical limitations: each navigation mode often operates independently, lacking effective performance evaluation and comparison mechanisms, making it difficult to select the optimal mode based on actual conditions; multi-mode data fusion often uses preset fixed parameters and cannot dynamically adjust to environmental conditions and mode performance; mode switching decisions are crude and simplistic, failing to consider the time delay and computational overhead of the switching process; data processing methods are simplistic, focusing primarily on certain common dimensions and failing to fully exploit the complementary characteristics of different modes; and the overall system lacks intelligence and adaptability, resulting in a sharp decline in performance under complex conditions such as signal obstruction and environmental interference. These issues severely restrict the navigation performance of drones in complex scenarios such as urban canyons, mountainous terrain, and marine environments. Summary of the Invention

[0004] The present invention provides a multi-mode navigation and positioning method, device and equipment for unmanned aerial vehicles (UAVs), aiming to achieve high-precision intelligent navigation and positioning and autonomous task execution of UAVs in complex environments. The method integrates key technologies such as navigation mode performance competition, credibility matrix establishment, switching cost evaluation, and frequency domain fusion processing to perform all-round intelligent management and control of the multi-mode navigation process, realize adaptive selection of navigation modes, switching decision optimization, complementary frequency domain advantages, and autonomous optimization of positioning accuracy, and form an intelligent multi-mode navigation and positioning system with learning ability, decision-making ability, autonomous adaptation, and dynamic optimization.

[0005] The first aspect of the present invention provides a multi-mode navigation and positioning method for an unmanned aerial vehicle, comprising the following steps: Receive an accuracy requirement request for a UAV navigation mission, and determine a target positioning accuracy index based on the accuracy requirement request; Acquire positioning data of each navigation mode, wherein each navigation mode includes a GPS navigation mode, an inertial navigation mode, and a visual navigation mode, and perform real-time validity evaluation on the positioning data of each navigation mode to generate a multi-mode navigation state set; Performing performance competition among the navigation modes based on the multi-mode navigation state set to generate performance competition results of the navigation modes, including: analyzing the signal strength and satellite visibility parameters of the GPS navigation mode to generate a GPS mode performance evaluation index; analyzing the drift error and stability parameters of the inertial navigation mode to generate an inertial navigation mode performance evaluation index; analyzing the feature matching degree and illumination adaptability parameters of the visual navigation mode to generate a visual navigation mode performance evaluation index; generating performance competition results of the navigation modes based on the GPS mode performance evaluation index, the inertial navigation mode performance evaluation index, and the visual navigation mode performance evaluation index, establishing an inter-mode negotiation mechanism according to the performance competition results of the navigation modes, and determining a navigation mode priority sequence through the inter-mode negotiation mechanism; establishing a navigation mode credibility matrix based on the navigation mode priority sequence and the multi-mode navigation state set, and generating an adaptive weight allocation sequence according to the navigation mode credibility matrix; Analyzing weight change trends of each navigation mode based on the adaptive weight allocation sequence, obtaining system resource overhead required for mode switching based on the weight change trend, quantitatively evaluating the system resource overhead to generate a switching cost index, and dynamically screening navigation modes based on the switching cost index to determine a dominant navigation mode and an auxiliary navigation mode at the current moment; Performing frequency characteristic analysis on the dominant navigation mode and the auxiliary navigation mode to generate a mode characteristic spectrum, determining a frequency coherence coefficient based on the mode characteristic spectrum, determining a frequency domain fusion strategy based on the frequency coherence coefficient, and generating a fused positioning trajectory based on the frequency domain fusion strategy; Obtain the accuracy prediction evaluation result of the fused positioning trajectory, perform trajectory optimization adjustment based on the accuracy prediction evaluation result and the target positioning accuracy index, output a high-precision positioning result, and complete the multi-mode navigation positioning of the UAV.

[0006] A second aspect of the present invention provides a multi-mode navigation and positioning device for an unmanned aerial vehicle, comprising: A demand receiving module is used to receive the accuracy requirement request of the UAV navigation task and determine the target positioning accuracy index according to the accuracy requirement request; A data acquisition module is used to acquire positioning data of each navigation mode, and perform real-time validity evaluation on the positioning data of each navigation mode to generate a multi-mode navigation state set; a performance competition negotiation module, configured to perform performance competition among the navigation modes based on the multi-mode navigation state set and generate performance competition results for the navigation modes, including: analyzing the signal strength and satellite visibility parameters of the GPS navigation mode to generate a GPS mode performance evaluation index; analyzing the drift error and stability parameters of the inertial navigation mode to generate an inertial navigation mode performance evaluation index; analyzing the feature matching degree and illumination adaptability parameters of the visual navigation mode to generate a visual navigation mode performance evaluation index; generating performance competition results for the navigation modes based on the GPS mode performance evaluation index, the inertial navigation mode performance evaluation index, and the visual navigation mode performance evaluation index; establishing an inter-mode negotiation mechanism based on the performance competition results of the navigation modes, and determining a navigation mode priority sequence through the inter-mode negotiation mechanism; a weight allocation module, configured to establish a navigation mode credibility matrix based on the navigation mode priority sequence and the multi-mode navigation state set, and generate an adaptive weight allocation sequence according to the navigation mode credibility matrix; a decision screening module, configured to analyze a weight change trend of each navigation mode based on the adaptive weight allocation sequence, obtain a system resource overhead required for mode switching based on the weight change trend, quantitatively evaluate the system resource overhead to generate a switching cost index, and dynamically screen the navigation modes based on the switching cost index to determine a dominant navigation mode and an auxiliary navigation mode at the current moment; a data fusion module, configured to perform frequency characteristic analysis on the dominant navigation mode and the auxiliary navigation mode to generate a mode characteristic spectrum, determine a frequency coherence coefficient based on the mode characteristic spectrum, determine a frequency domain fusion strategy based on the frequency coherence coefficient, and generate a fused positioning trajectory based on the frequency domain fusion strategy; The optimization output module is used to obtain the accuracy prediction evaluation result of the fusion positioning trajectory, perform trajectory optimization adjustment according to the accuracy prediction evaluation result and the target positioning accuracy index, output a high-precision positioning result, and complete the multi-mode navigation positioning of the UAV.

[0007] The third aspect of the present invention proposes a computer device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the steps of a multi-mode navigation and positioning method for an unmanned aerial vehicle disclosed in the first aspect are implemented.

[0008] The beneficial effects of the present invention are reflected in the following points: 1. By establishing a multi-mode navigation state management mechanism based on real-time effectiveness evaluation and performance competition analysis, combined with inter-mode negotiation and priority sorting technology, the deep mining of the advantages and characteristics of each navigation mode and the accurate identification of the collaborative relationship are achieved, thereby improving the accuracy, collaborative efficiency and environmental adaptability of navigation mode selection, enabling multi-mode navigation to quickly respond to environmental changes and maintain the optimal configuration state. 2. By building a credibility matrix and adaptive weight allocation technology, a complete weight management framework including basic weight determination, dynamic correction factor generation and weight sequence optimization is established. Combined with weight change trend analysis and switching cost evaluation mechanism, intelligent decision-making for mode switching is achieved, and the transformation of navigation fusion from the traditional fixed weight mode to the intelligent adaptive mode is realized, thereby improving fusion accuracy, data utilization efficiency and system resource utilization. 3. Through frequency characteristic analysis and frequency domain fusion strategy, the complementary advantages of different navigation modes in the frequency domain are achieved. A differentiated fusion scheme is designed according to the frequency coherence coefficient, solving the key technical problem that traditional navigation methods only fuse in the time domain and ignore frequency characteristics. A multi-mode navigation positioning scheme with full-band optimization capability is formed, which improves the positioning accuracy and trajectory smoothness of drones in dynamic and complex environments.

[0009] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings herein illustrate specific examples of the technical solutions described in the present invention, and together with the specific implementation methods constitute a part of the specification, and are used to explain the technical solutions, principles and effects of the present invention.

[0011] Unless otherwise specified or defined, the same reference numerals in different drawings represent the same or similar technical features, and the same or similar technical features may also be represented by different reference numerals.

[0012] Figure 1 It is a flow chart of a multi-mode navigation and positioning method for an unmanned aerial vehicle of the present invention.

[0013] Figure 2 This is a structural block diagram of a multi-mode navigation and positioning device for an unmanned aerial vehicle of the present invention.

[0014] Figure 3 It is a structural schematic diagram of a computer device of the present invention. DETAILED DESCRIPTION

[0015] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0016] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0017] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0018] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0019] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0020] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0021] The technical solutions of the embodiments of this application are introduced below.

[0022] like Figure 1As shown, an embodiment of the present invention provides a multi-mode navigation and positioning method for a UAV, including the following steps S110 to S170: Step S110: receiving an accuracy requirement request for a UAV navigation mission, and determining a target positioning accuracy index according to the accuracy requirement request.

[0023] Specifically, the mission management platform receives precision requirement requests for UAV navigation missions through a multi-level interface architecture. This establishes a standardized precision requirement description protocol, supporting the expression of multi-dimensional requirements such as position accuracy, velocity accuracy, attitude accuracy, and time synchronization accuracy. These precision requirement requests utilize a hierarchical and categorized description framework, including key information such as mission type identification, required accuracy level, environmental constraints, and performance indicator ranges. Mission types cover diverse application scenarios such as reconnaissance and surveillance, cargo transport, search and rescue, agricultural plant protection, power inspection, and marine surveying and mapping, with each mission type corresponding to different benchmark accuracy requirements. Accuracy levels are categorized into four levels: ultra-high accuracy (position accuracy better than 0.5 meters), high accuracy (position accuracy 0.5-2 meters), standard accuracy (position accuracy 2-10 meters), and coarse accuracy (position accuracy 10-50 meters). Velocity accuracy requirements correspond to error ranges of 0.05 m / s, 0.1 m / s, 0.5 m / s, and 1.0 m / s, respectively. Environmental constraints include parameters such as flight altitude range, meteorological conditions, electromagnetic interference intensity, geographic complexity, obstruction density, and signal availability. For express delivery missions in urban environments, the accuracy requirement request specifies position accuracy of 1.5 meters, velocity accuracy of 0.15 meters per second, and attitude accuracy of 0.8 degrees. It also specifies environmental constraints such as high-rise building obstruction, medium multipath interference intensity, and 80% GPS signal availability. Through a standardized protocol interface and a multi-dimensional requirements description framework, comprehensive acceptance of accuracy requirement requests for drone navigation missions is achieved.

[0024] The target positioning accuracy index is determined based on the received accuracy requirement request. The target positioning accuracy index determination process utilizes a multi-criteria decision-making approach, comprehensively considering multiple constraints such as mission requirements, hardware capabilities, and environmental limitations to generate the optimal achievable accuracy target. The accuracy index system includes five dimensions: absolute positioning accuracy, relative positioning accuracy, dynamic tracking accuracy, long-term stability accuracy, and real-time response accuracy. Each dimension has clear numerical targets and confidence intervals. Absolute positioning accuracy represents the deviation range of the UAV's position from its true geographic coordinates. It is quantified using statistical metrics such as circular error probable (CEP) and spherical error probable (SEP). CEP50% and CEP95% represent the position error range at 50% and 95% confidence levels, respectively. Relative positioning accuracy describes the consistency and continuity of the UAV's position changes over a continuous period of time. It is evaluated using position drift rate and trajectory smoothness metrics. The drift rate is controlled to ensure that the error increase per kilometer of flight distance does not exceed a set threshold. Dynamic tracking accuracy assesses the UAV's positioning stability during maneuvering flight, including parameters such as turning radius error, climb rate error, velocity tracking error, and angular velocity tracking error. Through multi-dimensional accuracy index quantification and reachability analysis, the target positioning accuracy indicators that meet the mission requirements are determined.

[0025] Step S120: Acquire positioning data of each navigation mode, including GPS navigation mode, inertial navigation mode, and visual navigation mode, and perform real-time validity evaluation on the positioning data of each navigation mode to generate a multi-mode navigation state set.

[0026] Specifically, a parallel data acquisition architecture is used to obtain the positioning data of each navigation mode, and the real-time positioning information of the GPS navigation mode, inertial navigation mode and visual navigation mode are collected simultaneously through independent data acquisition channels. GPS navigation mode data acquisition relies on a high-precision GNSS receiver, equipped with L1 / L2 / L5 triple-frequency reception capabilities, supporting the reception of multiple constellation signals such as GPS, GLONASS, BDS, Galileo, and the positioning accuracy reaches the sub-meter level. The GPS data acquisition frequency is set to 20Hz, continuously recording the three-dimensional position coordinates, velocity vector, timestamp and satellite geometric distribution factor (GDOP) of the drone, while monitoring signal quality parameters such as signal strength, carrier-to-noise ratio, pseudorange residual and multipath error. Inertial navigation mode data acquisition is achieved through a high-precision inertial measurement unit (IMU), which uses a combination of fiber optic gyroscope and quartz flexible accelerometer. The angular velocity measurement accuracy reaches 0.01° / h and the acceleration measurement accuracy reaches 10 -5The inertial data acquisition frequency is set at 100Hz, acquiring the drone's three-axis angular velocity, three-axis acceleration, attitude angle, and angular velocity integral position information in real time. The Kalman filter algorithm is used to calculate the optimal estimates of position, velocity, and attitude in real time. Visual navigation mode data acquisition is based on a combined architecture of binocular stereo cameras and a monocular visual odometry. The binocular camera has a baseline distance of 12 cm, a resolution of 1920×1080, and a frame rate of 30 fps. The monocular camera has a resolution of 2048×1536 and features automatic exposure and white balance. Visual data processing utilizes the ORB feature extraction algorithm, extracting 500-1000 feature points from each frame. Stereo matching and optical flow tracking are used to calculate relative displacement and rotation angles, generating a visual odometry trajectory. A parallel data acquisition architecture and multi-frequency and multi-mode reception technology enable comprehensive acquisition of positioning data for GPS, inertial, and visual navigation modes.

[0027] The real-time validity assessment of positioning data from each navigation mode is conducted. A multi-mode navigation state set is generated through multiple verification mechanisms, including data quality analysis, accuracy assessment, and reliability testing. This real-time validity assessment utilizes a multi-metric evaluation system, encompassing five dimensions: data integrity, temporal consistency, spatial continuity, accuracy stability, and anomaly detection. Data integrity is quantified using metrics such as packet loss rate, data update frequency, and data format validation. For GPS mode, the data loss rate must be less than 1%, for inertial mode, the data update frequency must be maintained above 95Hz, and for visual mode, the image quality score must be above 0.8. Temporal consistency assessment analyzes the timestamp alignment of data from each mode, calculating time synchronization error and latency jitter. The time synchronization error between GPS and inertial data must be less than 10 milliseconds, and the processing latency of visual data must be controlled within 50 milliseconds. Spatial continuity assessment verifies the rationality of positioning data through trajectory smoothness, velocity jump detection, and position jump recognition. The position jump threshold is set at 1.5 times the maximum possible velocity, and the velocity jump threshold is set at 2 times the maximum acceleration. Accuracy stability assessment quantifies the stability performance of each mode through methods such as standard deviation calculation within a sliding window, accuracy change rate analysis, and confidence interval estimation. Anomaly detection utilizes a hybrid approach based on statistical analysis, the 3σ criterion (|x - μ|>3σ) and machine learning. The anomaly score S = α·S_statistical + β·S_ML, where S_statistical = (x - μ) / σ and S_ML is the output of the machine learning model. Anomalies are identified when S>T_threshold. Anomaly modes such as outliers, drift, oscillation, and failure are identified, and an anomaly detection threshold matrix is ​​established. During marine inspection missions, the GPS mode achieved an effectiveness score of 0.95 in open sea conditions, the inertial mode achieved an effectiveness score of 0.88 under the influence of ship vibration, and the visual mode's effectiveness score dropped to 0.72 due to the influence of sea reflections. Through real-time effectiveness evaluation and a multi-verification mechanism, a multi-mode navigation status set is generated that reflects the current operating status and performance of each navigation mode.

[0028] Step S130 : performing performance competition among navigation modes based on the multi-mode navigation state set, generating performance competition results of each navigation mode, establishing an inter-mode negotiation mechanism according to the performance competition results of each navigation mode, and determining a navigation mode priority sequence through the inter-mode negotiation mechanism.

[0029] Specifically, the performance competition of each navigation mode is carried out by analyzing the comprehensive performance of GPS navigation mode, inertial navigation mode and visual navigation mode under current environmental conditions, establishing a dynamic competition evaluation and negotiation decision-making mechanism, and finally determining the priority ranking of each navigation mode.

[0030] In some embodiments, the performance competition of each navigation mode is performed based on the multi-mode navigation state set to generate the performance competition results of each navigation mode, including: analyzing the signal strength and satellite visibility parameters of the GPS navigation mode to generate a GPS mode performance evaluation index; analyzing the drift error and stability parameters of the inertial navigation mode to generate an inertial navigation mode performance evaluation index; analyzing the feature matching degree and illumination adaptability parameters of the visual navigation mode to generate a visual navigation mode performance evaluation index; generating the performance competition results of each navigation mode based on the GPS mode performance evaluation index, the inertial navigation mode performance evaluation index and the visual navigation mode performance evaluation index.

[0031] First, the GPS navigation mode signal strength and satellite visibility parameters in the multi-mode navigation state set are analyzed to generate GPS mode performance evaluation metrics. GPS mode performance evaluation focuses on two core elements: signal quality and geometric configuration. The current performance level of the GPS mode is quantified by real-time monitoring of key parameters such as carrier-to-noise ratio (C / N0), signal strength indicator (RSSI), satellite elevation angle distribution, and geometric dilution of precision (GDOP). Signal strength analysis utilizes multi-frequency signal power measurement. The carrier-to-noise ratio thresholds for the L1 band, L2 band, and L5 band are set at 35dB-Hz, 32dB-Hz, and 38dB-Hz, respectively. Signal quality warnings are triggered when the signal strength in any band falls below the threshold. Satellite visibility parameters include the total number of visible satellites, the number of effectively tracked satellites, satellite elevation angle distribution, and azimuth coverage. The requirement is that there must be at least six visible satellites, at least four of the effectively tracked satellites must have elevation angles above 15 degrees, and azimuth coverage must exceed 270 degrees. Geometric configuration quality is assessed using the dilution of precision factor, where GDOP = √(σ² X + σ²ᵧ + σ²ᵧ + σ²), PDOP = √(σ² X + σ²ᵧ + σ²ᵧ), HDOP = √(σ² X + σ²ᵧ), VDOP = σᵧ. These parameters are derived from the diagonal elements of the (AᵀA)⁻¹ matrix, where A is the observation matrix. A GDOP less than 6 is considered a good configuration, less than 3 is an excellent configuration, and greater than 10 is considered a poor configuration, requiring the GPS mode weight to be reduced. Multipath effects are assessed through pseudorange residual analysis and carrier phase observation quality verification. GPS performance metrics are degraded when multipath errors exceed 2 meters. In urban express delivery missions, GPS signals are obstructed by tall buildings, reducing the number of visible satellites from 12 to 7. The GDOP value increases from 2.1 to 5.8, while the carrier-to-noise ratio generally decreases by 3-5 dB, causing the GPS mode performance metric to drop from 0.92 to 0.73. Signal strength monitoring and geometric configuration analysis are used to generate GPS mode performance metrics that comprehensively reflect the real-time performance of the GPS navigation mode.

[0032] Furthermore, the drift error and stability parameters of the inertial navigation mode in the multi-mode navigation state set were analyzed to generate inertial navigation mode performance evaluation metrics. The inertial navigation mode performance evaluation primarily examined key parameters such as gyroscope drift, accelerometer bias, random walk noise, and temperature stability. Error propagation analysis and statistical characterization were used to quantify the inertial mode's accuracy degradation. Gyroscope drift error is divided into deterministic drift and random drift. Deterministic drift is determined through bias stability testing, requiring the angle random walk coefficient to be less than 0.1° / √h, and bias stability to be better than 1° / h. Accelerometer bias stability is required to be better than 50μg, and the velocity random walk coefficient to be less than 0.01m / s / √h. The temperature sensitivity of inertial components is determined through temperature coefficient calibration, requiring the gyroscope temperature coefficient to be less than 0.01° / h / °C, and the accelerometer temperature coefficient to be less than 10μg / °C. The time-dependent degradation of inertial navigation accuracy is described using the Schuler oscillation model. The position error growth rate is approximately 1 nautical mile per hour without external correction. Short-term stability assessment uses the Allan variance analysis method to calculate stability indicators at different integration times. Position drift is controlled within 0.5 meters within 1 minute and within 15 meters within 10 minutes. The impact of environmental vibration on inertial measurement accuracy is verified using a vibration test platform, requiring accuracy degradation to not exceed 20% under conditions of 0.5g vibration acceleration within the 1-100Hz frequency range. Through drift error analysis and stability parameter evaluation, inertial navigation mode performance evaluation indicators are generated to quantify the current level of accuracy and reliability of the inertial navigation mode.

[0033] The visual navigation mode's feature matching and illumination adaptability parameters within the multimodal navigation state set are analyzed to generate visual navigation mode performance evaluation metrics. This performance evaluation focuses on core elements such as image quality, feature extraction, matching accuracy, and environmental adaptability. Computer vision algorithms are used to analyze the visual sensor's operating status and navigation performance in real time. Feature matching evaluation utilizes feature descriptors such as ORB, SIFT, and SURF. The number of successfully extracted feature points, the uniformity of their distribution, and the discriminability of the feature descriptors are calculated for each image frame. Requirements are that each image frame must have at least 300 feature points, feature point coverage must exceed 80% of the image, and the Hamming distance matching success rate for feature descriptors must exceed 85%. Illumination adaptability parameters include image brightness distribution, contrast level, exposure time stability, and white balance accuracy. Image brightness histograms must be uniform to avoid overexposure and underexposure. Contrast evaluation utilizes both Weber and Michelson contrast criteria, requiring contrast values ​​to remain within a range of 0.3-0.8. Motion blur detection is achieved through image gradient analysis and frequency domain energy distribution evaluation. Visual navigation performance degrades significantly when the motion blur index exceeds 0.6. Depth estimation accuracy is assessed using a binocular stereo matching algorithm, with a requirement for depth estimation error of less than 0.3 meters at a distance of 10 meters and less than 2 meters at a distance of 50 meters. Visual odometry trajectory accuracy is verified using closed-loop testing and a global optimization algorithm, with the cumulative trajectory error growth rate required to be controlled within 1%. Feature matching analysis and illumination adaptability assessment are used to generate performance evaluation metrics for the visual navigation mode that comprehensively reflect its image processing capabilities and navigation accuracy.

[0034] Finally, performance competition results for each navigation mode are generated based on GPS, inertial, and visual navigation performance evaluation indicators. This performance competition is generated using a weighted comprehensive scoring method, normalizing and weighting the performance evaluation indicators of each navigation mode to produce directly comparable competitive ranking results. The competitive scoring algorithm considers the differentiated weighting of mission requirements, increasing the weight of accuracy indicators for high-precision missions, stability indicators for long-duration missions, and adaptability indicators for missions in complex environments. The GPS mode competitive scoring comprehensively considers three key factors: signal quality, satellite geometry, and multipath mitigation capability, and calculates the overall score through a weighted summation method. The inertial mode competitive scoring focuses on drift stability, noise level, and temperature compensation, using a time-weighted average method to reflect long-term stability. The visual mode competitive scoring primarily considers feature quality, illumination adaptability, and motion robustness, adjusting the weighting for different illumination conditions using an environmental adaptability coefficient. The comprehensive competitive ranking uses a ranking algorithm to determine the relative merits of each mode and generate a dynamically updated priority ranking result. The competition results also include recommendations for collaborative operation and complementary enhancement strategies for each mode. When the GPS mode scores highly, it is recommended as the dominant mode; when the inertial mode scores well, it is recommended to take on short-term high-precision navigation tasks; and when the visual mode scores well, it is recommended to take on relative positioning and environmental perception tasks. In the urban express delivery scenario, the GPS mode ranked first in open areas with a score of 0.94, the inertial mode ranked second with a score of 0.82, and the visual mode ranked third with a score of 0.75. A weighted comprehensive score and a dynamic ranking algorithm were used to generate competitive results reflecting the performance of each navigation mode under the current environmental conditions.

[0035] In some embodiments, establishing an inter-mode negotiation mechanism based on the performance competition results of the navigation modes includes: establishing a performance comparison matrix based on the performance competition results of the navigation modes; performing an inter-mode complementarity analysis based on the performance comparison matrix to obtain a synergistic enhancement relationship between the navigation modes; performing an inter-mode resource allocation demand analysis based on the synergistic enhancement relationship to obtain a resource configuration strategy; and initiating an inter-mode negotiation process based on the resource configuration strategy to establish an inter-mode negotiation mechanism.

[0036] A performance comparison matrix was constructed based on the performance competition results of each navigation mode. This performance comparison matrix adopts a three-dimensional structure, with GPS navigation mode, inertial navigation mode, and visual navigation mode as row and column indices. Matrix elements represent the performance differences and advantages between different modes. The matrix construction process compares the scoring data from the performance competition results for each navigation mode pairwise, calculating the performance difference and degree of complementarity between any two modes. Performance difference is calculated using the normalized score difference Dᵢ = (Sᵢ - S) / (Sᵢ + S), where Sᵢ and S are the performance scores of modes i and j. A positive value indicates that the row mode outperforms the column mode, while a negative value indicates that the column mode outperforms the row mode. The absolute value reflects the degree of advantage. The complementarity assessment considers the performance differences between modes under different environmental conditions and mission scenarios. Variance analysis and correlation calculations are used to determine the potential for complementarity between modes. The matrix also includes time dimension information, recording the dynamic performance trends of each mode at different times, supporting time-series performance prediction and dynamic adjustment. In urban express delivery tasks, GPS mode has an advantage of 0.12 over inertial mode in open areas and 0.19 over visual mode. In tunnel environments, inertial mode has an advantage of 0.76 over GPS mode and 0.05 over visual mode, demonstrating significant differences in environmental adaptability. Through paired comparisons and differential analysis of performance competition results, a performance comparison matrix was established that reflects the relative performance of each navigation mode.

[0037] Inter-mode complementarity analysis is conducted based on the performance comparison matrix to identify the synergistic enhancement relationships among navigation modes. Complementarity analysis utilizes covariance analysis and principal component analysis to identify the complementary characteristics and synergistic potential of each navigation mode across different performance dimensions. The synergistic enhancement relationship determines the synergistic effect and enhancement factor of each mode combination by analyzing the off-diagonal elements and temporal variation patterns in the performance comparison matrix. The synergistic relationship between GPS and inertial modes is primarily reflected in the complementarity of long-term and short-term accuracy. GPS provides a long-term stable absolute positioning reference, while the inertial mode provides short-term, high-frequency relative position updates. The synergistic relationship between GPS and visual modes is reflected in the complementarity of global and local positioning. GPS provides a global coordinate framework, while the visual mode provides local environmental perception and relative positioning capabilities. The synergistic relationship between inertial and visual modes is reflected in the complementarity of continuity and discreteness. The inertial mode provides continuous motion state estimation, while the visual mode provides discrete position correction and environmental constraints. Through complementarity analysis and synergistic effect evaluation, a synergistic enhancement relationship is derived that describes the collaborative potential and enhancement effect of each navigation mode.

[0038] Based on synergistic enhancement relationships, resource allocation requirements between modes are analyzed to derive resource allocation strategies. This resource allocation analysis considers multiple dimensions, including computing resources, storage resources, communication resources, and power consumption. The optimal resource allocation solution is determined based on the performance improvement data from the synergistic enhancement relationships. Computing resource allocation determines the processor time slice allocation ratio based on the algorithm complexity and real-time requirements of each mode. The GPS mode requires relatively few computing resources, primarily for signal processing and positioning. The inertial mode requires moderate computing resources for filtering and error compensation. The vision mode requires substantial computing resources for image processing and feature matching. Storage resource allocation considers the data caching and historical data retention requirements of each mode. The vision mode requires the largest storage space for image data and feature map storage. Communication resource allocation determines network resource allocation based on the data transmission frequency and bandwidth requirements of each mode. The GPS mode has a small data volume but is sensitive to latency. The inertial mode has a moderate data volume and a high update frequency. The vision mode has a large data volume but a high latency tolerance. Power resource allocation considers the energy consumption characteristics and task duration requirements of each mode. A dynamic power management strategy is established to optimize energy allocation while ensuring performance. For example, in a forest fire monitoring mission, the resource allocation strategy allocates 40% of computing resources to the visual mode for smoke detection, 35% to the inertial mode for maintaining stable flight, and 25% to the GPS mode for providing a position reference. Through resource demand analysis and optimization algorithms, a resource allocation strategy is developed that balances performance requirements and resource constraints.

[0039] An inter-mode negotiation process is initiated based on the resource allocation strategy, and an inter-mode negotiation mechanism is established. This negotiation process utilizes a distributed decision-making architecture, with each navigation mode acting as an independent negotiating entity, competing for resources and making performance commitments based on the resource allocation strategy. The negotiation mechanism consists of four phases: resource application, performance evaluation, solution coordination, and agreement. Each phase has clear negotiation rules and decision-making criteria. During the resource application phase, each mode submits a resource request based on its current performance status and mission requirements, including minimum resource requirements and desired resource allocation. During the performance evaluation phase, each mode commits to corresponding performance indicators and service quality based on the allocated resources, establishing a resource-performance mapping. During the solution coordination phase, resource and performance conflicts are resolved through multiple rounds of negotiation and strategy adjustments, employing game theory to identify Pareto optimal solutions. During the agreement phase, the final resource allocation plan and performance responsibility division are determined, and a dynamic adjustment mechanism is established to address environmental changes. The negotiation mechanism also includes breach detection and penalty mechanisms to ensure that each mode fulfills its performance commitments according to the agreement. In the complex terrain of mountain search and rescue missions, the GPS mode proactively reduces resource requests due to signal obstruction and provides limited positioning services. The inertial mode requests additional computing resources for high-precision short-term navigation, and the visual mode requests additional storage resources for terrain matching and obstacle detection. A resource allocation agreement is reached through three rounds of negotiation. Based on a distributed negotiation process and dynamic adjustment mechanism, an inter-mode negotiation mechanism is established to support coordinated work among navigation modes and optimize resource allocation.

[0040] A navigation mode priority sequence is determined through an inter-mode negotiation mechanism. The priority determination process primarily considers two key factors: each mode's current performance and environmental adaptability. By analyzing each mode's actual performance, stability characteristics, and responsiveness to environmental changes during negotiation, a framework for evaluating the relative strengths of each mode is established. GPS mode priority evaluation focuses on signal quality stability, positioning accuracy, and satellite geometry. It typically receives a higher priority in open environments with good signal quality, while its priority decreases in obstructed environments. Inertial mode priority evaluation primarily considers its short-term accuracy retention, drift error control, and independent operation characteristics. Its priority increases significantly in environments with limited or no GPS signal. Visual mode priority evaluation focuses on feature extraction quality, illumination adaptability, and environmental texture richness. It receives a higher priority in environments with good lighting and distinct texture features, while its priority decreases in inclement weather or monotonous environments. Priority sequence generation utilizes a weighted ranking method, dynamically ranking each mode based on its overall performance to ensure that the priority sequence accurately reflects the relative strengths of each mode under the current environmental conditions. In open areas of urban express delivery missions, GPS mode receives first priority thanks to its stable signal quality and excellent absolute positioning accuracy. Inertial mode receives second priority due to its good short-term stability and high-frequency update characteristics. Visual mode receives third priority due to its limited environmental dependence. In the signal-blocked environment of tunnel inspection missions, the priority sequence undergoes significant adjustments. Inertial mode jumps to first priority due to its independence from external signals and stable performance in closed environments. Visual mode rises to second priority due to its effective recognition of tunnel wall texture features and relative positioning capabilities. GPS mode is relegated to third priority due to severe signal blockage. Through the intelligent decision-making process of the negotiation mechanism, a navigation mode priority sequence is generated that accurately reflects the current performance status, environmental adaptability, and relative advantages of each navigation mode.

[0041] Step S140 : establishing a navigation mode credibility matrix based on the navigation mode priority sequence and the multi-mode navigation state set, and generating an adaptive weight allocation sequence according to the navigation mode credibility matrix.

[0042] Specifically, the navigation mode credibility matrix is ​​established by fusing the navigation mode priority sequence and the performance information in the multi-mode navigation state set to quantify the credibility of each navigation mode under the current environmental conditions, and generates a dynamically adjusted weight distribution scheme based on the credibility evaluation results.

[0043] In some embodiments, the establishing of a navigation mode credibility matrix based on the navigation mode priority sequence and the multimodal navigation state set includes: determining a basic credibility weight of each navigation mode according to the navigation mode priority sequence; analyzing a real-time performance deviation of each navigation mode based on the multimodal navigation state set; generating a dynamic credibility correction factor according to the real-time performance deviation; and establishing a navigation mode credibility matrix based on the basic credibility weight and the dynamic credibility correction factor.

[0044] The basic credibility weight for each navigation mode is determined based on the navigation mode priority sequence. This credibility weight is determined based on the ranking results and relative performance differences within the navigation mode priority sequence. A hierarchical weighting method is used to establish a baseline credibility level for each mode. Weighting is normalized to ensure that the sum of all mode weights equals 1 while maintaining the relative dominance of each mode. The highest-ranked navigation mode receives the highest basic credibility weight, typically set between 0.5 and 0.7, reflecting its dominant position in the current environment. The second-ranked mode receives a medium credibility weight, set between 0.2 and 0.4, serving as an important auxiliary support. The third-ranked mode receives a lower credibility weight, set between 0.1 and 0.3, primarily serving as a supplementary and verification function. The performance gap between modes is also considered in the weighting process. When performance differences between modes are significant, the weight difference is increased; when performance is similar, a more balanced weighting is adopted. The basic credibility weight serves as a stable reference benchmark, remaining relatively stable over a long period of time to prevent frequent weight fluctuations from impacting navigation stability. In an open area of ​​an urban express delivery mission, GPS mode ranked first and received a basic credibility weight of 0.65, inertial mode ranked second and received a weight of 0.25, and visual mode ranked third and received a weight of 0.10. Through hierarchical weight allocation and normalization, a basic credibility weight reflecting the baseline credibility level of each navigation mode was determined.

[0045] The real-time performance deviation of each navigation mode is analyzed based on a multi-mode navigation state set. By comparing the current performance of each navigation mode with its historical average performance level, the degree of performance fluctuation and deviation range of each mode are quantified. Performance deviation calculation uses a sliding window statistical method to calculate the performance mean, variance, and trend of each navigation mode within a time window to establish a performance baseline and deviation threshold. Real-time performance deviation in the GPS mode is primarily reflected in changes in positioning accuracy, signal quality fluctuations, and changes in satellite geometry. The degree of performance deviation is assessed by monitoring changes in GDOP values, carrier-to-noise ratio fluctuations, and increases and decreases in multipath errors. Real-time performance deviation in the inertial mode focuses on factors such as drift error growth, temperature effects, and vibration interference. The degree of deviation is quantified using Allan variance analysis and error propagation calculations. Real-time performance deviation in the visual mode is primarily reflected in changes in feature extraction quality, illumination adaptation fluctuations, and motion blur. The degree of deviation is assessed by changes in the number of feature points, matching success rate fluctuations, and image quality scores. The performance deviation analysis also considers the impact of environmental changes on the performance of each mode, establishing an environment-performance correlation model to predict performance trends. In a forest fire monitoring mission, increased smoke concentration caused the visual mode performance deviation to drop from a baseline of 0.88 to 0.54, a 38.6% deviation. The GPS mode was less affected by smoke, with the performance deviation dropping from 0.92 to 0.87, a 5.4% deviation. The inertial mode, affected by thermals, saw its performance deviation drop from 0.82 to 0.75, an 8.5% deviation. Through real-time performance monitoring and statistical analysis, the real-time performance deviation of each navigation mode relative to the baseline was determined.

[0046] Exemplarily, generating a dynamic credibility correction factor based on the real-time performance deviation includes: analyzing the numerical distribution range and changing trend of the real-time performance deviation; determining a performance deviation grade classification threshold based on the numerical distribution range; classifying the real-time performance deviation based on the performance deviation grade classification threshold; and generating a dynamic credibility correction factor based on the grade classification result.

[0047] First, the numerical distribution range and variation trends of real-time performance deviations were analyzed. Statistical analysis methods were used to determine the distribution characteristics and temporal variation patterns of performance deviations for each navigation mode. The numerical distribution range analysis employed quantile statistics and extreme value analysis to calculate the minimum, maximum, median, and quartiles of the performance deviations and establish a statistical description of the deviation distribution. The variation trend analysis employed regression analysis and time series analysis to identify the linear trend, cyclical variation, and random fluctuation components of the performance deviations and predict the direction and rate of variation. The GPS mode deviation distribution exhibited a normal distribution with a small variance, typically within ±15%, and a relatively stable variation trend. The inertial mode deviation distribution exhibited a gradual increase, with small deviations in the short term but accumulating over time, exhibiting an exponential growth trend. The visual mode deviation distribution was significantly affected by environmental factors, exhibiting a wide distribution range and significant environmental correlation, with variation trends closely related to lighting conditions and scene complexity.

[0048] Next, performance deviation grading thresholds are determined based on the numerical distribution range. This threshold setting converts continuous deviation values ​​into discrete grading categories. This grading system utilizes a five-level classification system: Excellent (deviation less than 5%), Good (deviation 5%-15%), Fair (deviation 15%-30%), Poor (deviation 30%-50%), and Failed (deviation greater than 50%). Each level corresponds to a different confidence correction factor. The threshold setting takes into account the unique characteristics of each navigation mode. The GPS mode threshold is relatively strict, reflecting its high-precision requirements. The inertial mode threshold takes into account time accumulation characteristics. The visual mode threshold adapts to environmental sensitivity.

[0049] The real-time performance deviations of each navigation mode are then classified based on the performance deviation grading threshold, and the analyzed real-time performance deviation values ​​are mapped to corresponding grading categories. The grading process uses a fuzzy classification method to handle boundary conditions. When the deviation value approaches the threshold boundary, a weighted average method is used to determine the grading. The classification results include the current grading, grading stability, and grading trend of each navigation mode, forming a dynamically updated grading status record. For example, in an offshore oil platform inspection mission, a real-time deviation of 8.2% in the GPS mode is classified as good, a deviation of 12.5% ​​in the inertial mode is classified as good, and a deviation of 35.8% in the visual mode is classified as poor.

[0050] Finally, a dynamic credibility correction factor is generated based on the classification results. The classification is converted into a quantitative correction coefficient through a grade-correction factor mapping relationship. The correction factor uses a nonlinear mapping function: Excellent corresponds to a correction factor of 1.1-1.2, Good corresponds to 0.9-1.0, Fair corresponds to 0.7-0.8, Poor corresponds to 0.4-0.6, and Failed corresponds to 0.1-0.3. Through deviation analysis, grade classification, and factor mapping, a dynamic credibility correction factor is generated that reflects the real-time credibility changes of each navigation mode.

[0051] A navigation mode credibility matrix is ​​established based on basic credibility weights and dynamic credibility correction factors. This credibility matrix is ​​constructed using a weighted fusion approach, with basic credibility weights serving as stable baseline components and dynamic credibility correction factors serving as real-time adjustment components. Mathematical operations are used to generate comprehensive credibility assessment results. The matrix is ​​constructed using a three-row, three-column square matrix, with the rows and columns corresponding to the GPS navigation mode, inertial navigation mode, and visual navigation mode, respectively. Diagonal elements represent the inherent credibility of each mode, while off-diagonal elements represent the cross-credibility and synergy between modes. Credibility is calculated using the fusion formula C(i,j) = w_base(i) × f_dynamic(i) × α_env(i,j), where w_base(i) is the base weight of mode i, f_dynamic(i) is the dynamic correction factor, and α_env(i,j) is the adaptability coefficient for environment j. The diagonal elements C(i,i) represent the individual credibility, while the off-diagonal elements C(i,j) = ρ(i,j)√(C(i,i)×C(j,j)) represent the cross-credibility. ρ(i,j) is the coordination coefficient. The environmental adaptability coefficient is adjusted based on the impact of the current environmental conditions on each mode. The GPS mode coefficient is higher in clear, open environments, the inertial mode coefficient is increased in tunnels, and the visual mode coefficient is enhanced in textured environments. The credibility matrix also incorporates a time decay mechanism, which gradually decays credibility assessments that have not been updated for a long time, ensuring the timeliness of the credibility assessment. The matrix update frequency is set to 1Hz, synchronized with the navigation data update frequency, enabling real-time credibility monitoring and assessment. Under the complex terrain conditions of mountain search and rescue missions, the GPS mode's credibility is significantly reduced due to terrain obstruction. The inertial mode maintains a high degree of credibility in the short term, while the visual mode's credibility is at a moderate level due to the influence of lighting and terrain texture. This results in a dynamic credibility distribution that reflects the credibility of each mode in the current environment. Through the fusion calculation of basic weights and dynamic corrections, a navigation mode credibility matrix is ​​established that reflects the credibility and collaborative relationships of each navigation mode in real time.

[0052] In some embodiments, generating an adaptive weight allocation sequence based on the navigation mode credibility matrix includes: analyzing the credibility distribution characteristics of each mode in the navigation mode credibility matrix; determining a weight allocation reference value based on the credibility distribution characteristics; performing adaptive weight adjustment based on the weight allocation reference value to generate an initial weight allocation sequence; and normalizing the initial weight allocation sequence to generate an adaptive weight allocation sequence.

[0053] The credibility distribution characteristics of each mode in the navigation mode credibility matrix were analyzed. Statistical analysis was used to extract the numerical distribution patterns and characteristic parameters of each navigation mode in the credibility matrix, including statistical characteristics such as the credibility mean, variance, skewness, and kurtosis. Distribution analysis employed a sliding time window statistical method to calculate the statistical distribution parameters of each mode's credibility within a specified time window, identifying the central tendency, dispersion, and distribution shape of the credibility. The time window length was set based on the responsiveness of the navigation mode: a 30-second window was used for the GPS mode, a 60-second window for the inertial mode, and a 15-second window for the visual mode to accommodate rapid environmental changes. The GPS mode credibility distribution characteristics generally exhibit a relatively stable normal distribution pattern, with a small mean fluctuation range and a low variance, reflecting its stable performance in open environments. In an urban express delivery mission, when the drone entered a canyon of tall buildings from an open square, the GPS mode's credibility dropped sharply from 0.92 to 0.35. The distribution analysis immediately captured this dramatic change and updated the distribution parameters. In stark contrast to the GPS mode, the inertial mode's credibility distribution exhibits dynamic characteristics over time, with high and stable credibility in the short term and a slowly declining trend in the long term, with the distribution skewness gradually shifting toward lower values. The visual mode's credibility distribution exhibits significant environmental dependence, with credibility concentrated in the high range in well-lit and textured environments and shifting toward lower values ​​in adverse environmental conditions. Its relatively large variance reflects its environmental sensitivity. The environmental adaptability index is calculated by analyzing the magnitude of credibility changes for each mode under different environmental conditions: A_env = 1 - |C_max - C_min| / C_avg, where C_max and C_min are the maximum and minimum credibility values ​​during environmental changes, respectively, and C_avg is the average credibility. This index reflects each mode's adaptability to environmental changes, with larger values ​​indicating greater adaptability. Through statistical analysis and distribution modeling, the credibility distribution characteristics reflecting the credibility change patterns and distribution patterns of each navigation mode were determined.

[0054] The weight allocation benchmark values ​​are determined based on the credibility distribution characteristics. This is determined using a quantitative mapping method based on the credibility distribution characteristics, converting the credibility distribution parameters of each model into a benchmark value for weight allocation. This mapping process is implemented using the mathematical function W_base = β1·μ_C + β2·(1 / σ_C) + β3·A_env, where μ_C is the credibility mean, σ_C is the credibility standard deviation, A_env is the environmental adaptability index, and β1, β2, and β3 are weight coefficients, with β1+β2+β3=1. This establishes a correspondence between the distribution characteristic parameters and the weight benchmark values. The credibility mean serves as the primary reference factor, reflecting the average performance level of each model. A higher mean corresponds to a larger benchmark weight. A linear function is used in the mapping relationship to ensure the rationality of the weight allocation. The stability index is calculated as the inverse of the credibility variance. A smaller variance indicates greater stability and corresponds to a higher benchmark weight. The stability coefficient is positively correlated with the benchmark weight. Taking offshore oil platform inspections as an example, the GPS mode's high mean (0.89) and low variance (0.02) in open waters give it a baseline weight of 0.55. However, the visual mode's high variance (0.15) due to sea surface reflections reduces its baseline weight to 0.15. The environmental adaptability factor is determined based on the magnitude of the change in the credibility distribution under different environmental conditions. The adaptability level is quantified by calculating the rate of change in the distribution parameters, with modes with smaller rates of change receiving higher adaptability scores. The baseline value calculation also considers the synergy between modes. When the credibility distribution characteristics of multiple modes demonstrate good complementarity, the baseline weights of these modes are increased accordingly. The baseline weight allocation is normalized to ensure that the sum of all mode baseline values ​​equals 1, maintaining mathematical consistency in the weight allocation. Through quantitative analysis and mapping calculations of the credibility distribution characteristics, the baseline weight allocation for each navigation mode is determined.

[0055] Adaptive weight adjustments are performed based on baseline weight allocation values ​​to generate an initial weight allocation sequence. Adaptive weight adjustment utilizes a dynamic adjustment algorithm to adaptively modify baseline weight values ​​based on real-time environmental changes and mission requirements, generating a weight allocation scheme tailored to the current situation. The adjustment algorithm maps the degree of environmental change to the magnitude of weight adjustment, including adjustment strategies corresponding to three levels of change: slight, moderate, and severe. The adjustment algorithm considers the immediate impact of environmental changes on the performance of each mode. When environmental changes are detected, the weight of the corresponding mode is automatically adjusted, with the adjustment response time dynamically set based on the severity of the change. Changing mission requirements also trigger weight adjustments: increasing the weight of the high-precision mode for high-precision missions, increasing the weight of the high-frequency update mode for fast-response missions, and increasing the weight of the stable mode for long-duration missions. For example, in a mountain search and rescue mission, when dense fog suddenly descends, causing visibility to drop dramatically from 8 kilometers to 200 meters, the adaptive adjustment algorithm recognizes the sharp drop in the confidence of the visual mode and adjusts its weight from a baseline value of 0.30 to 0.18. Simultaneously, the weight of the inertial mode is increased to 0.45. The entire adjustment process is completed within 1.5 seconds. Adaptive adjustment utilizes incremental adjustments, limiting the adjustment range to ±30% of the baseline value. A rate limit is set to prevent drastic changes in weights from causing unstable navigation performance. Weight adjustment also incorporates a smoothing filter mechanism, using sliding average and low-pass filtering to eliminate high-frequency noise and sudden changes in weight adjustment. The filter time constant is set to 2-5 seconds. Multi-mode coordinated adjustment ensures consistent weight adjustments across all modes. When the weight of one mode increases, the weights of the other modes are adjusted accordingly to maintain a balanced overall weight. Through the adaptive adjustment algorithm and dynamic balancing mechanism, an initial weight allocation sequence is generated that reflects the current environment and mission requirements.

[0056] The initial weight assignment sequence is normalized to generate an adaptive weight assignment sequence. Normalization employs mathematical standardization to ensure that the sum of the weights of all navigation modes is strictly equal to 1, maintaining the mathematical constraints and physical meaning of the weight assignment. The normalization algorithm first performs a weight validity check to verify the rationality and integrity of the weight values. It then calculates the sum of all mode weights in the initial weight assignment sequence and adjusts each mode weight to a standardized value through scaling. The normalization process also includes a weight floor protection mechanism to ensure that the weight of any mode does not drop to zero or a negative value. A minimum weight threshold of 0.05 is maintained to ensure the basic participation of each mode and prevent the complete failure of any single mode. In the complex scenario of forest fire monitoring, the initial weight sequence showed that the weights of GPS, inertial, and vision modes were 0.28, 0.45, and 0.38, respectively, for a total of 1.11. The normalization process corrected these weights to a precise ratio of 0.25, 0.41, and 0.34 through scaling. A weight cap constraint prevents excessive concentration of weight in a single mode. A maximum weight threshold of 0.80 is set for each mode. This weight dispersion mechanism mitigates the risks of over-reliance on a single navigation mode. The normalized weight allocation sequence includes data integrity checks to ensure the feasibility of the weight allocation scheme. The sequence output uses timestamps and status identifiers to support time-series management and status tracking of weight configurations. Through mathematical normalization and constraint processing, an adaptive weight allocation sequence is generated that meets real-time navigation requirements.

[0057] Step S150: Analyze the weight change trend of each navigation mode based on the adaptive weight allocation sequence, obtain the system resource overhead required for mode switching according to the weight change trend, quantitatively evaluate the system resource overhead to generate a switching cost index, and dynamically screen the navigation modes based on the switching cost index to determine the dominant navigation mode and auxiliary navigation mode at the current moment.

[0058] Based on the adaptive weight allocation sequence, the weight change trends of each navigation mode are analyzed. This weight change trend analysis utilizes time series analysis methods to extract features and identify patterns from the weight time series data of each navigation mode in the adaptive weight allocation sequence. The trend analysis algorithm first establishes a sliding time window with a window length set to an integer multiple of the weight update period. The trend is then identified through statistical analysis of the weight data within the window. The weight change rate is calculated using a differencing method. The first-order difference ΔW(t) = W(t) - W(t-1) reflects the immediate weight change, while the second-order difference Δ²W(t) = ΔW(t) - ΔW(t-1) reflects the acceleration of the change. Multiple-order difference sequences are used to comprehensively determine the direction and speed of weight change. Trend type identification includes four basic patterns: upward trend, downward trend, oscillating trend, and stable trend. The trend direction and strength are determined by the sign and magnitude of the trend slope k = Σ(t·W(t) - t_avg·W_avg) / Σ(t - t_avg)². The GPS mode weight trend shows a stable high weight in open environments, followed by a rapid decline in obscured environments. The inertial mode weight remains stable or rises slowly in the short term, but declines over the long term due to accumulated errors. The visual mode weight change is highly correlated with environmental conditions, with weight fluctuations caused by changes in illumination exhibiting periodic oscillations. Trend prediction uses the autoregressive moving average (ARMA) model (p,q) to predict the direction of future short-term weight changes based on historical weight data, providing forward-looking information for mode switching decisions. In urban canyon navigation scenarios, a rapid decline in GPS weight from a stable high value indicates the need to prepare for a switch to another navigation mode. The trend analysis algorithm can identify this change pattern at the initial stage of the weight decline. Through time series analysis and trend identification, the weight change trend of each navigation mode is determined.

[0059] In some embodiments, obtaining the system resource overhead required for mode switching based on the weight change trend includes: identifying the mode switching trigger point in the weight change trend; determining the source mode and target mode to be switched based on the mode switching trigger point; analyzing the resource requirements for switching from the source mode to the target mode, including computing resource requirements, time delay requirements and data transmission requirements; and calculating the system resource overhead required for mode switching based on the resource requirements.

[0060] Identify mode switch trigger points within the weight change trend. Mode switch trigger point identification utilizes a multi-criteria approach, analyzing features such as weight change rate, weight intersections, and weight mutation points to determine when a mode switch is necessary. The weight change rate trigger criterion is set to indicate a potential switch trigger point when the weight change rate |dW / dt| of any navigation mode exceeds a threshold of 0.1 / s. The weight intersection trigger criterion monitors the intersection of different navigation mode weight curves. A switch trigger point is identified when the weight of the original dominant mode decreases and the weight of the candidate mode increases, and the difference in weight after the intersection exceeds 0.15. The weight mutation trigger criterion identifies mutation points by examining the second-order derivative of the weights. When |d²W / dt²| > 0.5 / s², a switch requirement is determined to be caused by a drastic environmental change. The trigger point verification mechanism uses duration verification to prevent false triggering caused by transient disturbances. Trigger conditions must be met continuously for at least 0.5 seconds for validation to be effective. For example, when entering an urban canyon from an open area, the GPS weight drops from 0.75 to 0.35 within 2 seconds, with a drop rate of 0.2 / s. At the same time, the inertial mode weight increases from 0.20 to 0.55. At t=1.2s, the two weight curves intersect, confirming that this moment is the mode switching trigger point.

[0061] The source and target modes to be switched are determined based on the mode switch trigger point. Source mode identification is determined by analyzing the weight distribution before the trigger point, identifying the navigation mode with the highest weight as the currently dominant source mode. Target mode selection is based on the weight development trend after the trigger point, predicting the mode with the highest weight within a future time window as the switching target. The mode pair determination process also needs to consider the feasibility of the switch. Direct switching between certain modes may have technical limitations and require transitioning through an intermediate mode. Switching path planning utilizes a state transition diagram approach, with GPS, inertial, and vision modes as state nodes and feasible switching paths as directed edges. The optimal switching sequence is determined using a shortest path algorithm. For example, if the vision mode fails due to a sudden change in illumination and needs to be switched, if the current GPS signal is also weak, the switching path is determined to be a two-step switch from vision to inertial to GPS, rather than a direct switch from vision to GPS. Special case handling includes an emergency switching strategy for the simultaneous failure of multiple modes, prioritizing ensuring that at least one mode remains operational.

[0062] The resource requirements for switching from the source mode to the target mode were analyzed, including computational resources, time delay, and data transmission requirements. The computational resource requirements included the CPU cycles and memory usage required for coordinate transformation calculations, filter reinitialization, and parameter reconfiguration during the switching process. The GPS-to-inertial switching process requires a conversion from the ECEF coordinate system to the vehicle coordinate system, with computational complexity of O(n²) and an estimated CPU usage of 15% for 0.8 seconds. The time delay requirement includes four components: switching decision delay, data buffering delay, algorithm convergence delay, and system stabilization delay. The total delay from GPS to inertial is approximately 1.2 seconds, and the total delay from inertial to vision is approximately 2.1 seconds. The data transmission requirements analyzed the differences in data formats and transmission bandwidth requirements for each mode. GPS data packets are approximately 200 bytes per packet with a 20Hz update rate, requiring 4KB / s of bandwidth. Inertial data packets are approximately 500 bytes per packet with a 100Hz update rate, requiring 50KB / s of bandwidth. Visual data, which contains image information, is approximately 100KB per frame after compression, requiring 3MB / s of bandwidth at 30fps. Resource requirements also include the overhead of running the two modes in parallel during the switching process, and the resource usage of both modes needs to be maintained during the transition period.

[0063] Based on the resource requirements, the system resource overhead required for mode switching is calculated. A weighted comprehensive evaluation method is used to unify the computing resource requirements, time delay requirements, and data transmission requirements analyzed above into a comparable cost metric space. Computational resource overhead is quantified using the formula C_compute = (CPU_usage × T_duration) + (Memory_usage × K_memory), where C_compute represents the computing resource overhead, CPU_usage represents processor usage, T_duration represents resource usage duration, Memory_usage represents memory usage, and K_memory represents the memory cost coefficient. The resource requirements for operations such as coordinate transformation calculations and filter reinitialization analyzed above are substituted into this formula to obtain the computational resource overhead. Time cost is evaluated using C_time = T_switch × V_accuracy_loss, where C_time represents the time overhead, T_switch represents the total switching delay determined above, and V_accuracy_loss represents the accuracy loss per unit time coefficient, reflecting the impact of decreased navigation accuracy during the switching period. The data transmission overhead is calculated as C_data = (Bandwidth × T_transfer) / Bandwidth_max, where C_data represents the data transmission overhead, Bandwidth represents the actual bandwidth requirement from the aforementioned analysis, T_transfer represents the data transmission time, and Bandwidth_max represents the maximum available system bandwidth for normalization. The overall system resource overhead is calculated as C_total = α·C_compute + β·C_time + γ·C_data, where C_total represents the total system resource overhead, and α, β, and γ are weight coefficients reflecting the relative importance of compute resources, time delay, and data transmission in the total overhead, respectively. The weights are adjusted dynamically based on the task type and environmental conditions. This yields the system resource overhead required to switch from source mode to target mode.

[0064] System resource overhead is quantitatively evaluated to generate a switching cost metric. This quantitative evaluation of switching cost establishes a comprehensive cost function, unifying resource overheads across different dimensions into a comparable cost metric space. The cost function is designed as C_switch = w1·C_compute + w2·C_time + w2·C_accuracy + w4·C_risk, where C_compute represents the compute resource cost, C_time represents the time delay cost, C_accuracy represents the accuracy loss cost, and C_risk represents the switching risk cost. w1, w2, w3, and w4 are weight coefficients for each cost. The compute resource cost is calculated as the weighted sum of the incremental CPU utilization and incremental memory consumption: C_compute = k_cpu·ΔCPU + k_mem·ΔMemory. Normalization is performed to ensure comparability across different resource types. The time delay cost is modeled using a logarithmic function: C_time = log(1 + T_switch / T_ref), where T_ref is the reference time constant, reflecting the diminishing marginal effect of time delay. The accuracy loss cost reflects the degradation in positioning accuracy during the handover process: C_accuracy = ∫(σ_switch(t) - σ_normal(t))dt. The cumulative accuracy loss during the handover period is calculated by integration. The handover risk cost considers the probability of handover failure and the severity of the consequences: C_risk = P_failure·S_consequence, where P_failure is the probability of handover failure and S_consequence is the severity score of the consequences of failure. The cost metric also incorporates environmental adaptability corrections, increasing the handover cost in adverse conditions and reducing it in stable environments. This dynamic cost assessment is achieved through environmental factor adjustment. During an emergency supply delivery mission in heavy rain, severe visual mode performance degradation necessitated a handover. However, an evaluation of the cost of switching to GPS mode revealed that cloud cover reduced GPS signal quality. After comprehensive consideration of the handover cost metric, it was recommended to maintain the inertial mode as the primary mode. Through multi-dimensional quantification and comprehensive evaluation, a handover cost metric reflecting the true cost of handover was generated.

[0065] Dynamic navigation mode selection is performed based on a switching cost metric to determine the current dominant and auxiliary navigation modes. This dynamic selection utilizes a cost-benefit balance decision-making mechanism, comparing the cost of maintaining the current mode with the cost of switching to a new mode, and selecting the navigation configuration with the lowest total cost. The decision-making algorithm establishes a state transition model, treating the navigation mode configuration as the system state and the switching action as the state transition. Dynamic programming is used to solve the optimal state sequence. The dominant mode selection prioritizes stability. A switch is only executed when the performance benefit of the new mode, minus the switching cost, is significantly better than the current mode. The judgment condition is: Performance_new - C_switch > Performance_current + Δ_threshold, where Δ_threshold is the switching threshold. Auxiliary mode selection considers complementarity with the dominant mode, prioritizing navigation modes that can compensate for the dominant mode's shortcomings. The synergistic effect of different mode combinations is quantified using a complementarity matrix. The selection process also incorporates mode health monitoring to continuously assess the operational status and availability of each navigation mode. Modes with health below the threshold are temporarily excluded from the candidate set. Multi-mode collaborative configuration allows for the simultaneous activation of multiple auxiliary modes, but requires balancing performance gains with resource consumption. The optimal number of auxiliary modes is determined through marginal utility analysis. In the complex transition scenarios of urban express delivery, when a drone enters a densely built-up area from an open area, a dynamic screening algorithm assesses that GPS performance degrades but the cost of switching to pure inertial navigation is too high. The decision is to upgrade the inertial mode to the dominant mode, downgrade GPS to the auxiliary mode, and activate the visual mode as a secondary auxiliary, forming a multi-mode collaborative navigation configuration. Through cost balancing and dynamic optimization, the dominant and auxiliary navigation modes are determined to suit the current environment and mission requirements.

[0066] Step S160 , performing frequency characteristic analysis on the dominant navigation mode and the auxiliary navigation mode to generate a mode characteristic spectrum, determining a frequency coherence coefficient based on the mode characteristic spectrum, determining a frequency domain fusion strategy based on the frequency coherence coefficient, and generating a fused positioning trajectory based on the frequency domain fusion strategy.

[0067] Frequency characteristics of the dominant and auxiliary navigation modes were analyzed to generate mode characteristic spectra. The frequency characteristic analysis used the Fast Fourier Transform (FFT) algorithm to convert the time-domain navigation positioning data into the frequency domain and extract the spectral characteristics of each navigation mode. Data preprocessing included detrending, windowing, and zero-padding. Detrending used the least squares method to fit and eliminate linear trends in the data. Windowing used a Hanning window function to reduce spectral leakage. Zero-padding was used to extend the data length to an integer power of 2 to improve FFT computational efficiency. Spectral calculation used a segmented average power spectral density estimation method, segmenting the long time series into multiple overlapping segments, each with 1024 samples and a 50% overlap. Multi-segment averaging was used to suppress the influence of random noise. The spectral characteristics of the GPS navigation mode show a dominant low-frequency component, with energy concentrated in the 0-0.1 Hz band, reflecting its steady-state positioning characteristics and slow drift. The lower energy in the 0.1-1 Hz band indicates that GPS has limited responsiveness to rapid motion changes. The spectrum of the inertial navigation mode exhibits a broadband characteristic. The low-frequency band (0-0.5Hz) reflects integration drift and bias error, the mid-frequency band (0.5-5Hz) contains information about the vehicle's motion, and the high-frequency band (5-50Hz) is primarily characterized by sensor noise and vibration interference. The spectral characteristics of the visual navigation mode are related to the image processing frame rate, with energy peaks occurring at discrete frequency points. The primary peak occurs at the image update frequency (e.g., 30Hz), while secondary peaks occur at the feature tracking update frequency. Spectral amplitude normalization ensures comparability between different modes. Phase spectrum analysis reveals the time delay characteristics of each mode. GPS has satellite signal propagation delay, while the inertial mode has the lowest delay. The visual mode has a fixed delay due to image processing. During offshore wind farm inspections, the spectrum of the dominant inertial mode is concentrated in the 0.2-2Hz range, reflecting periodic disturbances caused by wind and waves. The auxiliary GPS mode provides a stable reference below 0.05Hz, and the spectral characteristics of the two modes complement each other well. Using FFT transformation and power spectrum analysis, the characteristic spectrum of each mode is generated to reflect the frequency domain characteristics of each navigation mode.

[0068] The frequency coherence coefficient is determined based on the mode characteristic spectrum. Frequency coherence analysis quantifies the degree of correlation between the dominant and auxiliary modes in the frequency domain by calculating the cross-power spectral density and auto-power spectral density at each frequency point. The coherence coefficient is calculated as γ²(f) = |S_xy(f)|² / (S_xx(f)·S_yy(f)), where S_xy(f) is the cross-power spectral density of the dominant mode x and auxiliary mode y, S_xx(f) and S_yy(f) are their respective auto-power spectral densities, and γ²(f) ranges from [0,1]. A coherence coefficient close to 1 indicates that the two modes are highly correlated at that frequency point, with the output signals having a definite amplitude and phase relationship; a coefficient close to 0 indicates that the two modes are independent and suitable for complementary fusion. The frequency resolution is set to 0.01Hz, and the coherence coefficient is calculated for 5000 frequency points in the range of 0-50Hz to form a continuous coherence curve. Coherence analysis of GPS and inertial modes shows that the coherence coefficient exceeds 0.85 in the ultra-low frequency band of 0-0.1 Hz, indicating that both can accurately reflect slow vehicle motion. In the mid-frequency band of 0.5-2 Hz, the coherence drops below 0.3, indicating that GPS is unable to track rapid changes, while the inertial mode responds well. In the 1-10 Hz frequency band, the inertial and visual modes exhibit moderate coherence (0.4-0.6), indicating that both can perceive the vehicle's dynamic motion, but through different mechanisms, leaving room for fusion optimization. The coherence coefficient also needs to consider the influence of signal-to-noise ratio. Coherence coefficients calculated under low signal-to-noise ratio conditions require bias correction using the formula γ_corrected = γ_measured·(1 - 1 / SNR). Time-varying coherence was analyzed using a short-time Fourier transform (STFT) with a window length of 256 samples and a sliding step size of 64 points, generating coherence distribution maps in the time-frequency domain. In urban canyon navigation scenarios, the multipath effect of the GPS signal causes abnormal peaks in the coherence between the GPS signal and the inertial mode in specific frequency bands. These false coherences need to be identified and eliminated through statistical testing. The frequency coherence coefficient between the dominant and auxiliary modes is determined through cross-spectral analysis and coherence calculations.

[0069] In some embodiments, determining the frequency domain fusion strategy based on the frequency coherence coefficient includes: dividing the frequency bands according to the frequency coherence coefficient to identify high coherence frequency bands and low coherence frequency bands; determining the fusion mode of each frequency band based on the distribution characteristics of the high coherence frequency bands and the low coherence frequency bands; designing frequency domain fusion filter parameters according to the fusion mode of each frequency band; and generating a frequency domain fusion strategy based on the frequency domain fusion filter parameters.

[0070] Frequency bands are divided based on the frequency coherence coefficient to identify high-coherence and low-coherence bands. Band division uses an adaptive threshold method, with the division threshold determined by statistically analyzing the distribution characteristics of the coherence coefficient. High-coherence bands are defined as frequency ranges with a coherence coefficient γ²(f) greater than 0.7, indicating strong correlation between the dominant and auxiliary modes in these frequency bands. Low-coherence bands are defined as ranges with γ²(f) less than 0.3, indicating relative independence of the two modes. Moderate-coherence bands are defined as those with 0.3 ≤ γ²(f) ≤ 0.7 and require special processing strategies. Band boundaries are determined by the zero points of the first-order derivative of the coherence coefficient to avoid discontinuities caused by artificial settings. Continuity constraints ensure smooth transitions between adjacent bands, and a 3dB bandwidth criterion is used to define the transition region. The frequency band division results for the GPS-inertial combination show that 0-0.08 Hz is a high-coherence band, where both modes can accurately reflect steady-state information; 0.08-0.5 Hz is a transition band; 0.5-10 Hz is a low-coherence band, where GPS response is insufficient and the inertial mode dominates; and above 10 Hz is a noise-dominated band. Frequency band division also considers signal power distribution; low-power bands, even if highly coherent, are not used as primary fusion bands. In the smoky environment of forest fire monitoring missions, the high-coherence frequency band of the visual-inertial combination is concentrated in the 2-8 Hz range, corresponding to the main flight action frequency of the drone; low-coherence frequency bands are below 0.1 Hz and above 20 Hz, corresponding to slow drift and high-frequency noise, respectively. Adaptive threshold analysis is used to identify high-coherence and low-coherence frequency bands with different coherence characteristics.

[0071] Based on the distribution characteristics of high- and low-coherence frequency bands, the fusion mode for each frequency band is determined. The fusion mode design adopts a differentiated strategy based on the coherence characteristics of the frequency bands. High-coherence frequency bands use weighted average fusion to fully utilize redundant information, while low-coherence frequency bands use complementary fusion to leverage the strengths of each. The fusion mode for high-coherence frequency bands is Y_high(f) = α(f)·X_main(f) + (1-α(f))·X_aux(f), where α(f) = γ²(f) / (γ²(f) + σ²_noise) is the frequency-dependent weighting coefficient, ensuring a more balanced weight distribution with higher coherence. Low-coherence frequency bands use frequency-selective fusion, determining the dominance of each mode based on its signal-to-noise ratio in a specific frequency band. Y_low(f) = X_main(f)·H_main(f) + X_aux(f)·H_aux(f), where H_main and H_aux are complementary transfer functions. A phase compensation mechanism is introduced in medium-coherence frequency bands to eliminate pseudo-coherence effects by estimating and correcting phase differences, thereby improving fusion quality. A gradual fusion mode is used in transition frequency bands, with an S-shaped transition function ensuring smooth switching of fusion strategies and avoiding discontinuities at frequency band boundaries. GPS-inertial fusion uses a fixed weight ratio of 0.6:0.4 in high-coherence low-frequency bands to fully leverage the long-term stability of GPS. In low-coherence medium-frequency bands, the inertial mode weight is increased to 0.9, with GPS providing only auxiliary corrections. Noise bands are suppressed through band-stop filtering. An abnormality handling mechanism automatically adjusts the fusion mode upon detecting sudden changes in coherence, preventing erroneous data from contaminating the fusion results. During emergency rescue missions in heavy rain, GPS signal attenuation degrades the previously high-coherence frequency bands. The fusion mode is adaptively adjusted to inertial-dominated, while increasing the weight in frequency bands where vision is still effective. Through differentiated design and adaptive adjustment, a fusion mode tailored to the characteristics of each frequency band is determined.

[0072] Frequency-domain fusion filter parameters are designed based on the fusion mode for each frequency band. Filter design utilizes a frequency response matching method, with transfer functions designed based on the requirements of each fusion mode. A low-pass filter, designed as a fourth-order Butterworth filter, is used in the high-coherence band. The cutoff frequency is set at the boundary between the high- and low-coherence bands, with a passband ripple less than 0.5 dB and a stopband attenuation greater than 40 dB. A bandpass filter is used for selective fusion, with its center frequency aligned with the target frequency band. The bandwidth is adaptively adjusted based on the coherence distribution, and the Q value is adjustable from 2 to 10. A complementary filter bank design ensures that H_main(f) + H_aux(f) = 1 consistently holds, preventing signal distortion. A linear-phase FIR structure is used to ensure consistent delay across frequency bands. The filter order is determined by the required frequency resolution and transition band width. Typical configurations are 128th-order FIR or 8th-order IIR, balancing performance and computational complexity. Filter parameters for GPS-inertial fusion: A low-pass cutoff frequency of 0.1Hz is used to extract GPS steady-state information, and a high-pass cutoff frequency of 0.1Hz is used to extract inertial dynamic information. A 3dB gain reduction at the crossover frequency ensures a smooth transition. An adaptive parameter adjustment mechanism dynamically modifies the filter coefficients based on real-time coherence changes, with the adjustment rate limited to less than 10% per second to prevent filter instability. Digital filter implementation considers fixed-point precision and uses a 16-bit fixed-point representation to ensure real-time performance in embedded systems. In the complex terrain conditions of mountain search and rescue, intermittent GPS signals can cause drastic changes in coherence. The adaptive filter ensures continuity and stability of the fusion process by adjusting the cutoff frequency and filter order in real time. Through transfer function design and parameter optimization, frequency-domain fusion filter parameters are generated to meet the fusion requirements of each frequency band.

[0073] A frequency-domain fusion strategy is generated based on the frequency-domain fusion filter parameters. The fusion strategy integrates frequency band division, fusion mode, and filter parameters to form a complete frequency-domain fusion process. The strategy implementation first performs an FFT transform on the input primary and auxiliary navigation data to obtain frequency-domain representations X_main(f) and X_aux(f). Then, according to the preset frequency band division scheme, the spectral data is allocated to the corresponding processing channels. High-coherence frequency band data is fed into the weighted fusion channel, and low-coherence frequency band data is fed into the selective fusion channel. Each channel performs processing according to the designed fusion mode and filter parameters. The weighted fusion channel implements Y_weighted(f) = W(f) · [α · X_main(f) + (1-α) · X_aux(f)], while the selective fusion channel implements Y_selective(f) = X_main(f) · H_LP(f) + X_aux(f) · H_HP(f). The channel outputs are superimposed in the frequency domain to form a complete spectrum, Y_fused(f) = Y_weighted(f) + Y_selective(f) + Y_transition(f), where Y_transition is the result of smoothing the transition frequency band. The inverse FFT transform converts the fused frequency domain data back to the time domain, generating a fused positioning data sequence. Post-processing includes phase continuity checking, outlier removal, and delay compensation to ensure the integrity and real-time nature of the output data. The strategy also includes a performance monitoring mechanism to evaluate the fusion effect in real time and provide feedback to adjust the fusion parameters. In long-duration missions for offshore wind farm inspections, the frequency domain fusion strategy successfully addressed the problems of long-term GPS drift and short-term inertial noise, achieving optimized performance across the entire frequency band through frequency-selective fusion. Through system integration and process optimization, a complete frequency domain fusion strategy based on frequency domain analysis was generated.

[0074] A fused positioning trajectory is generated based on a frequency-domain fusion strategy. This fused positioning trajectory generation utilizes a frequency-domain fusion strategy to optimize the frequency-domain combination of data from the primary and auxiliary navigation modes, producing high-quality positioning results. The trajectory generation process processes the navigation data stream in real time, employing a sliding window mechanism to ensure computational efficiency and real-time performance. The window length is 1024 points, and the update rate is 100Hz. Within each processing cycle, newly incoming navigation data is combined with historical data to form a complete window. After preprocessing, an FFT transform is performed and the data is converted to the frequency domain for fusion. The fusion process strictly adheres to the frequency-domain fusion strategy. High-coherence frequency bands are subjected to weighted averaging to suppress random errors, low-coherence frequency bands are selected to retain valid information, and transition bands are subjected to gradient processing to ensure continuity. The inverse-transformed time-domain data undergoes an overlap-add process to eliminate blocking artifacts. The output points are then tested for continuity with historical trajectories to ensure trajectory smoothness and physical plausibility. Trajectory quality assessment metrics include position accuracy, velocity continuity, and acceleration plausibility. Fusion effectiveness is verified by comparison with a reference trajectory. When an anomaly is detected in the fusion results, the exception handling mechanism automatically switches to an alternative fusion strategy or single-mode output, ensuring system robustness. The fused trajectory output includes complete information such as position coordinates, velocity vector, accuracy estimate, and confidence level, supporting the decision-making needs of downstream applications. Through frequency-domain fusion processing and quality control, a highly accurate and reliable fused positioning trajectory is generated.

[0075] Step S170: Obtain the accuracy prediction evaluation result of the fused positioning trajectory, optimize and adjust the trajectory according to the accuracy prediction evaluation result and the target positioning accuracy index, output a high-precision positioning result, and complete the multi-mode navigation positioning of the UAV.

[0076] Specifically, the fused positioning trajectory is subjected to accuracy prediction and evaluation. Using a combination of statistical analysis and error propagation theory, key metrics such as absolute accuracy, relative accuracy, and temporal stability are calculated. Absolute accuracy evaluation quantifies the trajectory's absolute positioning accuracy by comparing the position deviation of the fused trajectory with a reference datum and calculating statistical parameters such as root mean square error, circular error probable, and maximum error. Relative accuracy evaluation analyzes the trajectory's internal consistency and continuity, assessing the trajectory's relative accuracy performance based on the distance and angular errors between adjacent points. Temporal stability evaluation analyzes the temporal trend of accuracy, identifies periods of deterioration and causes, and calculates the accuracy drift rate and stability coefficient. For example, in urban express delivery missions, when a drone flies within a complex building environment, the accuracy evaluation algorithm analyzes the fused trajectory and finds that absolute positioning accuracy reaches 1.2 meters in open areas, dropping to 2.8 meters in obscured areas. Relative accuracy maintains 0.5 meters in straight flight segments and increases to 1.5 meters during turns. The evaluation process utilizes a sliding window approach, calculating local accuracy metrics within different time windows to form a temporal and spatial distribution map of accuracy. Error propagation analysis calculates the contribution of each navigation mode error to the fusion result, identifying the main error sources and weak links. Through multi-dimensional accuracy analysis and error propagation calculation, the accuracy prediction evaluation results of the fused positioning trajectory are obtained.

[0077] Trajectory optimization and adjustment are performed based on the accuracy prediction assessment results and the target positioning accuracy indicators. An iterative optimization algorithm is used to improve the overall accuracy of the fused positioning trajectory. Trajectory optimization and adjustment utilize a segmented optimization strategy, focusing on optimizing low-accuracy trajectory segments identified during the accuracy assessment while maintaining the accuracy of high-accuracy segments. The optimization algorithm first compares the current trajectory accuracy with the target accuracy indicator. Trajectory segments that meet the required accuracy remain unchanged, while segments with insufficient accuracy undergo an optimization process. Optimization methods include weight redistribution, filter parameter adjustment, and outlier data removal. The appropriate optimization method is selected based on the error source and characteristics. Weight redistribution adjusts the contribution of each navigation mode in the fusion process, reducing the weight of the low-accuracy mode and increasing the influence of the high-accuracy mode. Filter parameter adjustment optimizes the process noise and observation noise parameters of the Kalman filter to improve filtering performance and state estimation accuracy. Outlier data removal utilizes an outlier detection method based on statistical tests to identify and remove observation data that significantly deviates from the normal range. During mountain search and rescue missions, when terrain obstruction causes a period of GPS accuracy degradation, the optimization algorithm automatically increases the fusion weights of the inertial and visual modes, reducing the GPS weight from 0.6 to 0.3, increasing the inertial weight from 0.25 to 0.45, and the visual weight from 0.15 to 0.25. After optimization, trajectory accuracy during this period improved from 3.5 meters to 2.1 meters. The optimization and adjustment process employs an iterative mechanism, repeatedly performing accuracy assessments and parameter adjustments until the trajectory accuracy meets the target requirement or the maximum number of iterations is reached. During a forest fire monitoring mission, after three iterations of optimization, the overall accuracy of the fused positioning trajectory improved from an initial 2.8 meters to 1.9 meters, meeting the target accuracy requirement of 2.5 meters. Through segmented optimization and iterative adjustment, high-precision positioning results that meet the target accuracy specifications are output, completing the multi-mode navigation and positioning of the drone.

[0078] In order to implement the multi-mode navigation and positioning method for unmanned aerial vehicles corresponding to the above method embodiment, to achieve the corresponding functions and technical effects. Figure 2 , Figure 2 The following is a block diagram of a multi-mode navigation and positioning device 200 for a drone provided in an embodiment of the present application. For ease of explanation, only the parts related to this embodiment are shown. The multi-mode navigation and positioning device 200 for a drone provided in an embodiment of the present application includes: The requirement receiving module 201 is used to receive the accuracy requirement request of the UAV navigation task and determine the target positioning accuracy index according to the accuracy requirement request; The data acquisition module 202 is used to acquire positioning data of each navigation mode, and perform real-time validity evaluation on the positioning data of each navigation mode to generate a multi-mode navigation state set; The performance competition negotiation module 203 is configured to perform performance competition among the navigation modes based on the multi-mode navigation state set and generate performance competition results for the navigation modes, including: analyzing the signal strength and satellite visibility parameters of the GPS navigation mode to generate a GPS mode performance evaluation index; analyzing the drift error and stability parameters of the inertial navigation mode to generate an inertial navigation mode performance evaluation index; analyzing the feature matching degree and illumination adaptability parameters of the visual navigation mode to generate a visual navigation mode performance evaluation index; generating performance competition results for the navigation modes based on the GPS mode performance evaluation index, the inertial navigation mode performance evaluation index, and the visual navigation mode performance evaluation index; establishing an inter-mode negotiation mechanism based on the performance competition results of the navigation modes, and determining a navigation mode priority sequence through the inter-mode negotiation mechanism; A weight allocation module 204 is configured to establish a navigation mode credibility matrix based on the navigation mode priority sequence and the multi-mode navigation state set, and generate an adaptive weight allocation sequence according to the navigation mode credibility matrix; A decision screening module 205 is configured to analyze weight change trends of various navigation modes based on the adaptive weight allocation sequence, obtain system resource overhead required for mode switching based on the weight change trends, quantitatively evaluate the system resource overhead to generate a switching cost index, and dynamically screen navigation modes based on the switching cost index to determine the current dominant navigation mode and auxiliary navigation mode; a data fusion module 206 configured to perform frequency characteristic analysis on the dominant navigation mode and the auxiliary navigation mode to generate a mode characteristic spectrum, determine a frequency coherence coefficient based on the mode characteristic spectrum, determine a frequency domain fusion strategy based on the frequency coherence coefficient, and generate a fused positioning trajectory based on the frequency domain fusion strategy; The optimization output module 207 is used to obtain the accuracy prediction evaluation result of the fused positioning trajectory, perform trajectory optimization adjustment based on the accuracy prediction evaluation result and the target positioning accuracy index, output a high-precision positioning result, and complete the multi-mode navigation positioning of the UAV.

[0079] The above-mentioned drone multi-mode navigation and positioning device 200 can implement the drone multi-mode navigation and positioning method of the above-mentioned method embodiment. The optional options in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here. The remaining contents of the embodiment of this application can refer to the contents of the above-mentioned method embodiment and will not be repeated in this embodiment.

[0080] like Figure 3As shown, the third embodiment of the present invention also provides a computer device, including a memory 301, a processor 302, and a computer program stored in the memory 301 and executable on the processor 302, characterized in that when the processor 302 executes the program, the steps of the multi-mode navigation and positioning method for a drone described in the first embodiment of the present invention are implemented.

[0081] The purpose of the above embodiments is to exemplify and deduce the technical solution of the present invention, and to fully describe the technical solution, purpose and effect of the present invention. Its purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosed content of the present invention, and it does not limit the scope of protection of the present invention.

[0082] The above embodiments are not exhaustive and may include many other embodiments not listed above. Any replacements and improvements made without violating the concept of the present invention are within the scope of protection of the present invention.

Claims

1. A multi-mode navigation and positioning method for an unmanned aerial vehicle, characterized in that: include: Receive an accuracy requirement request for a UAV navigation mission, and determine a target positioning accuracy index based on the accuracy requirement request; Acquire positioning data of each navigation mode, wherein each navigation mode includes a GPS navigation mode, an inertial navigation mode, and a visual navigation mode, and perform real-time validity evaluation on the positioning data of each navigation mode to generate a multi-mode navigation state set; Performing performance competition among the navigation modes based on the multi-mode navigation state set to generate performance competition results of the navigation modes, including: analyzing the signal strength and satellite visibility parameters of the GPS navigation mode to generate a GPS mode performance evaluation index; analyzing the drift error and stability parameters of the inertial navigation mode to generate an inertial navigation mode performance evaluation index; analyzing the feature matching degree and illumination adaptability parameters of the visual navigation mode to generate a visual navigation mode performance evaluation index; generating performance competition results of the navigation modes based on the GPS mode performance evaluation index, the inertial navigation mode performance evaluation index, and the visual navigation mode performance evaluation index, establishing an inter-mode negotiation mechanism according to the performance competition results of the navigation modes, and determining a navigation mode priority sequence through the inter-mode negotiation mechanism; establishing a navigation mode credibility matrix based on the navigation mode priority sequence and the multi-mode navigation state set, and generating an adaptive weight allocation sequence according to the navigation mode credibility matrix; Analyzing weight change trends of each navigation mode based on the adaptive weight allocation sequence, obtaining system resource overhead required for mode switching based on the weight change trend, quantitatively evaluating the system resource overhead to generate a switching cost index, and dynamically screening navigation modes based on the switching cost index to determine a dominant navigation mode and an auxiliary navigation mode at the current moment; Performing frequency characteristic analysis on the dominant navigation mode and the auxiliary navigation mode to generate a mode characteristic spectrum, determining a frequency coherence coefficient based on the mode characteristic spectrum, determining a frequency domain fusion strategy based on the frequency coherence coefficient, and generating a fused positioning trajectory based on the frequency domain fusion strategy; Obtain the accuracy prediction evaluation result of the fused positioning trajectory, perform trajectory optimization adjustment based on the accuracy prediction evaluation result and the target positioning accuracy index, output a high-precision positioning result, and complete the multi-mode navigation positioning of the UAV.

2. The multi-mode navigation and positioning method for unmanned aerial vehicles according to claim 1, characterized in that: The establishing of an inter-mode negotiation mechanism according to the performance competition results of the navigation modes includes: Establishing a performance comparison matrix based on the performance competition results of each navigation mode; Performing inter-mode complementarity analysis based on the performance comparison matrix to obtain the synergistic enhancement relationship of each navigation mode; Based on the collaborative enhancement relationship, perform resource allocation demand analysis between modes to obtain resource configuration strategies; An inter-mode negotiation process is initiated for the resource allocation strategy, and an inter-mode negotiation mechanism is established.

3. The multi-mode navigation and positioning method for unmanned aerial vehicles according to claim 1, characterized in that: The establishing of a navigation mode credibility matrix based on the navigation mode priority sequence and the multi-mode navigation state set includes: Determining a basic credibility weight of each navigation mode according to the navigation mode priority sequence; Analyzing the real-time performance deviation of each navigation mode based on the multi-mode navigation state set; generating a dynamic credibility correction factor according to the real-time performance deviation; A navigation mode credibility matrix is ​​established based on the basic credibility weight and the dynamic credibility correction factor.

4. The multi-mode navigation and positioning method for unmanned aerial vehicles according to claim 1, characterized in that: Generating an adaptive weight allocation sequence according to the navigation mode credibility matrix includes: Analyzing the credibility distribution characteristics of each mode in the navigation mode credibility matrix; Determining a weight distribution benchmark value according to the credibility distribution characteristics; Performing adaptive weight adjustment based on the weight allocation reference value to generate an initial weight allocation sequence; Normalizing the initial weight distribution sequence to generate an adaptive weight distribution sequence.

5. The multi-mode navigation and positioning method for unmanned aerial vehicles according to claim 1, characterized in that: The acquiring of system resource overhead required for mode switching according to the weight change trend includes: Identifying a mode switching trigger point in the weight change trend; Determining a source mode and a target mode to be switched according to the mode switching trigger point; Analyzing resource requirements for switching from the source mode to the target mode, including computing resource requirements, time delay requirements, and data transmission requirements; The system resource overhead required for mode switching is calculated based on the resource requirement.

6. The multi-mode navigation and positioning method for unmanned aerial vehicles according to claim 1, characterized in that: The determining of the frequency domain fusion strategy based on the frequency coherence coefficient includes: Dividing the frequency bands according to the frequency coherence coefficients to identify high coherence frequency bands and low coherence frequency bands; Determining a fusion mode for each frequency band based on distribution characteristics of the high coherence frequency band and the low coherence frequency band; Designing frequency domain fusion filter parameters according to the fusion mode of each frequency band; A frequency domain fusion strategy is generated based on the frequency domain fusion filter parameters.

7. The multi-mode navigation and positioning method for unmanned aerial vehicles according to claim 3, characterized in that: Generating a dynamic credibility correction factor according to the real-time performance deviation includes: Analyze the numerical distribution range and change trend of the real-time performance deviation; Determining a performance deviation level classification threshold based on the numerical distribution range; Classifying the real-time performance deviation based on the performance deviation classification threshold; A dynamic credibility correction factor is generated according to the grade classification result.

8. A multi-mode navigation and positioning device for an unmanned aerial vehicle, characterized in that: include: A demand receiving module is used to receive the accuracy requirement request of the UAV navigation task and determine the target positioning accuracy index according to the accuracy requirement request; A data acquisition module is used to acquire positioning data of each navigation mode, and perform real-time validity evaluation on the positioning data of each navigation mode to generate a multi-mode navigation state set; a performance competition negotiation module, configured to perform performance competition among the navigation modes based on the multi-mode navigation state set and generate performance competition results for the navigation modes, including: analyzing the signal strength and satellite visibility parameters of the GPS navigation mode to generate a GPS mode performance evaluation index; analyzing the drift error and stability parameters of the inertial navigation mode to generate an inertial navigation mode performance evaluation index; analyzing the feature matching degree and illumination adaptability parameters of the visual navigation mode to generate a visual navigation mode performance evaluation index; generating performance competition results for the navigation modes based on the GPS mode performance evaluation index, the inertial navigation mode performance evaluation index, and the visual navigation mode performance evaluation index; establishing an inter-mode negotiation mechanism based on the performance competition results of the navigation modes, and determining a navigation mode priority sequence through the inter-mode negotiation mechanism; a weight allocation module, configured to establish a navigation mode credibility matrix based on the navigation mode priority sequence and the multi-mode navigation state set, and generate an adaptive weight allocation sequence according to the navigation mode credibility matrix; a decision screening module, configured to analyze a weight change trend of each navigation mode based on the adaptive weight allocation sequence, obtain a system resource overhead required for mode switching based on the weight change trend, quantitatively evaluate the system resource overhead to generate a switching cost index, and dynamically screen the navigation modes based on the switching cost index to determine a dominant navigation mode and an auxiliary navigation mode at the current moment; a data fusion module, configured to perform frequency characteristic analysis on the dominant navigation mode and the auxiliary navigation mode to generate a mode characteristic spectrum, determine a frequency coherence coefficient based on the mode characteristic spectrum, determine a frequency domain fusion strategy based on the frequency coherence coefficient, and generate a fused positioning trajectory based on the frequency domain fusion strategy; The optimization output module is used to obtain the accuracy prediction evaluation result of the fusion positioning trajectory, perform trajectory optimization adjustment according to the accuracy prediction evaluation result and the target positioning accuracy index, output a high-precision positioning result, and complete the multi-mode navigation positioning of the UAV.

9. A computer device, characterized in that: The method comprises a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the method according to any one of claims 1 to 7 when executing the computer program.

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