Autonomous flight reliable positioning system and method based on multi-source data

Abstract

The invention provides an autonomous flight reliable positioning system and method based on multi-source data. The autonomous flight reliable positioning system comprises five main functional layers: a multi-source sensing data acquisition layer, a sensor end credible layer, a credible center verification processing layer, a trust sensing weight fusion layer and a safety output and auditing layer. According to the method, the complex environment adaptability is improved through cooperative collection of the multi-source sensors, a cryptographic mechanism ensures that data are real and complete, and cheating and tampering attacks are resisted; the positioning precision is optimized through dynamic trust weighted fusion, and the weight of the sensor can be adjusted in a self-adaptive mode; traceable auditing of the positioning process is achieved through full-link logs and encrypted evidence storage; the modular design supports distributed deployment and scene expansion, and is suitable for various types of autonomous platforms; the abnormal response mechanism guarantees the safe fault tolerance of the system, the system can still operate stably under attack or interference, and the reliability and safety of autonomous flight positioning are comprehensively improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent navigation and positioning technology, and in particular to a reliable positioning system and method for autonomous flight based on multi-source data. Background Technology

[0002] In traditional multi-source fusion positioning systems, positioning accuracy and robustness are typically improved by fusing data from sensors such as GNSS, IMU, and SLAM. However, existing methods mainly focus on physical redundancy and algorithm optimization, lacking verification mechanisms for the authenticity, integrity, and timeliness of data sources. In complex or adversarial environments, security threats such as GPS spoofing, visual data tampering, and communication link hijacking will severely impact the reliability and security of the positioning system.

[0003] While existing technologies offer autonomous positioning methods, they still fail to address the core issue of data source verification, resulting in insufficient positioning reliability in the face of malicious attacks. Therefore, a solution is urgently needed that enhances the anti-attack capabilities of positioning systems while maintaining positioning accuracy. Summary of the Invention

[0004] The purpose of this invention is to provide a reliable autonomous flight positioning system and method based on multi-source data. It uses cryptographic tools to design a verification mechanism for the authenticity, integrity, and timeliness of the data source, and provides reliable fusion autonomous positioning.

[0005] According to one objective of the present invention, the present invention provides an autonomous flight reliable positioning system based on multi-source data, comprising: A multi-source sensing data acquisition layer is used to deploy heterogeneous sensors to collect environmental information and motion status; The sensor-side trusted layer includes a trusted signature module and a trusted encryption module, which are used to perform trusted verification and encapsulation on the data output by the heterogeneous sensor. The Trusted Center Verification Processing Layer is used to uniformly receive, verify, and schedule the data output from the Trusted Layer at the sensor end. The trust-aware weighted fusion layer is used to weight and fuse verified data based on sensor trust levels, and output the positioning result and error covariance. The security output and audit layer is used to encrypt and store the positioning results, interface with the navigation control module, and trigger security response strategies when an anomaly is detected.

[0006] Furthermore, the heterogeneous sensor includes a GPS / GNSS positioning and navigation module, an IMU inertial module, a barometer module, and a visual / laser SLAM module.

[0007] Furthermore, the GPS / GNSS positioning and navigation module is used to receive satellite navigation signals to calculate the device's position and velocity information; the IMU inertial module measures angular velocity and linear acceleration through gyroscopes and accelerometers to obtain attitude changes, velocity, and relative position estimates; the barometer module measures atmospheric pressure and converts it into relative altitude information; and the visual / laser SLAM module is used to achieve positioning and map building in unknown environments.

[0008] Furthermore, the trusted signature module interacts with the GPS / GNSS positioning and navigation module to digitally sign the positioning coordinates and add a timestamp and randomization factor; the trusted signature module interacts with the visual / laser SLAM module to sign the SLAM pose of each frame.

[0009] Furthermore, the trusted encryption module interacts with the IMU inertial module, using symmetric encryption to encrypt the acceleration and angular velocity information and then concatenating them for HMAC integrity verification; the trusted encryption module also interacts with the barometer module, performing symmetric encryption on the altitude measurement information and then concatenating it for HMAC integrity verification.

[0010] Furthermore, the trusted center verification processing layer includes: The data receiving and scheduling module is used to uniformly receive, synchronize, and schedule data from the trusted layer at the sensor end. The signature / HMAC verification module is used to perform public key signature verification or HMAC verification on incoming data packets to confirm the trustworthiness of the source. The timestamp / randomization factor checking module is used to compare the timestamps and randomization factors of the data to prevent replay attacks. The structural integrity verification module is used to determine whether the data packet structure conforms to the trusted protocol specification in order to filter out abnormal packets; A trusted data caching module is used to store data that has passed signature / HMAC verification, timestamp / randomization factor check and structural integrity verification, for use by the trust-aware weighted fusion layer; Exception logging module: Records verification failure data for security auditing.

[0011] Furthermore, the trust-aware weighted fusion layer includes: The trust weight calculation module is used to dynamically evaluate the trust level of sensors and generate weight coefficients based on the historical reliability, error distribution and attack detection of sensor data. The trust-weighted fusion module is used to input the source data into an extended Kalman filter or an unscented Kalman filter, and fuse the trust weights to achieve the optimal estimation of position and attitude. The integrated positioning result output module outputs spatial position coordinates and heading angle, which are then connected to the downstream control unit. The covariance output module is used to output the error covariance matrix and update the trust weights in the trust weight calculation module through a dynamic feedback mechanism.

[0012] Furthermore, the security output and audit layer includes: The location and trajectory encryption and evidence storage module is used to encrypt and store key location results for later retrospective auditing. The navigation / control module directly interfaces with downstream control units such as the flight control system; The security response strategy module is used to trigger source degradation, backup sensor activation, mission abort, or return-to-home strategies when persistent anomalies or signs of attack are detected.

[0013] Furthermore, the sensor modules of the multi-source sensing data acquisition layer are deployed on the same device or distributed platform to achieve collaborative acquisition of heterogeneous data.

[0014] According to another objective of the present invention, the present invention provides a positioning method for an autonomous flight reliable positioning system based on multi-source data, characterized by comprising the following steps: S1. Data Acquisition Steps: Through the GPS / GNSS positioning and navigation module, IMU inertial module, barometer module, and visual / laser SLAM module of the multi-source sensing data acquisition layer, position velocity, angular velocity / acceleration, relative altitude, environmental features, and pose data are acquired respectively. S2, Data Trusted Encapsulation Steps: Use the trusted signature module of the trusted layer at the sensor end to digitally sign the GPS / GNSS positioning coordinates and SLAM pose, and add a timestamp and randomization factor. Use the trusted encryption module to symmetrically encrypt the IMU data and barometer altitude information and then perform HMAC integrity verification. S3. Data verification and processing steps: The receiving and scheduling module of the trusted center verification and processing layer receives and synchronizes the encapsulated data in a unified manner. It then sequentially performs signature / HMAC verification, timestamp / randomization factor check, and structural integrity verification according to the data scheduling rules. Data that passes verification is stored in the trusted data cache module, and data that fails verification is logged as an anomaly. S4. Trust-weighted fusion step: The trust weight calculation module of the trust perception weighted fusion layer generates trust weights based on the historical reliability of the sensor, error distribution and attack detection. The trust weighted fusion module inputs the trusted data into the Kalman filter and fuses the trust weights, outputting the positioning result and error covariance. The covariance output module feeds the error covariance back to the trust weight calculation module to update the weights. S5. Security Output and Response Steps: The security output and audit layer transmits the positioning results to the navigation / control module. Optionally, the positioning and trajectory encryption and evidence storage module can be used for encrypted storage. If an anomaly or attack is detected, the security response strategy module triggers a preset fault tolerance mechanism.

[0015] This invention's technical solution utilizes multi-source sensor collaborative acquisition to enhance adaptability to complex environments; cryptographic mechanisms ensure data authenticity and integrity, resisting deception and tampering attacks; dynamic trust-weighted fusion optimizes positioning accuracy, adaptively adjusting sensor weights; end-to-end logging and encrypted evidence storage enable traceable auditing of the positioning process; modular design supports distributed deployment and scenario expansion, suitable for various autonomous platforms; and an anomaly response mechanism ensures system security and fault tolerance, maintaining stable operation even under attacks or interference, comprehensively improving the reliability and security of autonomous flight positioning. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the structure of an embodiment of the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0020] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0021] Example 1 like Figure 1 As shown, this invention discloses an autonomous flight reliable positioning system based on multi-source data. It employs a modular structure design and introduces cryptographic mechanisms for encrypting, authenticating, verifying, trust-assessing, and fusing sensor data. The overall structure is divided into five main functional layers: 1. Multi-source sensing data acquisition layer This layer includes a variety of heterogeneous sensor devices: GPS / GNSS positioning and navigation module: Receives satellite signals to calculate position and speed, which is the foundation of outdoor navigation, but is easily interfered with in weak signal environments; IMU (Inertial Measurement Unit): Composed of a gyroscope and an accelerometer, it obtains attitude, velocity and relative position estimates through integration. It has a fast response but limited long-term accuracy. Barometer module: measures atmospheric pressure and converts it into relative altitude to improve vertical positioning accuracy; Visual / LiDAR SLAM module: Enables localization and map building in unknown environments, with strong autonomy but sensitive to computing resources and perception conditions.

[0022] 2. Trusted Layer at the Sensor End Ensure that data has anti-tampering, anti-replay, and traceability capabilities before entering the system: Trusted signature module: Interacts with GPS / GNSS module to perform coordinate digital signature, adding timestamp and randomization factor; interacts with visual / laser SLAM module to perform pose signature, preventing forgery and tampering; Trusted encryption module: It interacts with the IMU module to symmetrically encrypt acceleration and angular velocity information and performs HMAC verification; it interacts with the barometer module to encrypt and protect altitude information.

[0023] 3. Trusted Center Verification Processing Layer Unified reception and scheduling of sensor data processing, including: Data receiving and scheduling module: Unifies data receiving, time synchronization and scheduling management to avoid data loss or timing errors; The signature / HMAC verification module verifies the trustworthiness of the data packet source; if successful, it is cached; otherwise, a security response is triggered. Timestamp / randomization factor checking module: Prevents replay attacks and verifies data timeliness and uniqueness; Structural integrity verification module: Filters abnormal packets to ensure that the data structure conforms to the protocol specifications; Trusted data caching module: Stores all verified data for subsequent fusion and retrieval; Exception logging module: Records verification failure data for security auditing.

[0024] 4. Trust-Perceived Weighted Fusion Layer Integrating multi-source information by introducing a trust management mechanism and a weighted fusion method: Trust weight calculation module: dynamically evaluates sensor trust based on historical reliability, error distribution, attack detection, etc., and generates weight coefficients; Trust-weighted fusion module: Inputs data into a Kalman filter and fuses trust weights to achieve optimal estimation; Fusion positioning result output module: outputs spatial position coordinates and heading angle, and interfaces with downstream control unit; Covariance output module: Outputs the error covariance matrix, which is used as feedback to update the trust weights.

[0025] 5. Security Output and Audit Layer It features log auditing and real-time response capabilities: Location and trajectory encryption and evidence storage module (optional): Encrypts and stores key location results for later retrospective auditing; Navigation / Control Module: Directly interfaces with downstream control units such as the flight control system; Security Response Strategy Module: When an anomaly is detected, fault tolerance mechanisms such as source weight reduction, activation of backup sensors, or task abort are triggered.

[0026] This invention introduces cryptographic signatures and randomization factor mechanisms into multi-source sensor data streams; the data receiver embeds a trusted verification chain to examine each data packet layer by layer; the fusion algorithm dynamically adjusts the participation degree based on trust weights to resist malicious sensors or data pollution; the system provides a closed-loop output and auditing mechanism, ensuring that the positioning results are verifiable and traceable; it can be extended to edge computing or distributed collaborative positioning scenarios.

[0027] Example 2 This embodiment is basically the same in structure as Embodiment 1. The difference is that this embodiment is an autonomous flight reliable positioning system based on multi-source data. It aims to improve the security and robustness of multi-source fusion positioning technology in complex environments. It adopts a modular structure design and introduces cryptographic mechanisms to encrypt and protect sensor data, authenticate and verify it, assess trust, and perform fusion calculations. This ensures that the output positioning results still have high reliability and auditability even when encountering attacks such as forgery, replay, and interference.

[0028] The overall system structure of this embodiment includes five main functional layers: a multi-source sensing data acquisition layer, a sensor-end trust layer, a trust center verification and processing layer, a trust-aware weighted fusion layer, and a security output and audit layer, wherein: 1. Multi-source sensing data acquisition layer This layer comprises various heterogeneous sensor devices, including a GPS / GNSS positioning and navigation module, an IMU inertial module, a barometer module, and a visual / laser SLAM module. These modules independently acquire environmental information and motion status, forming multiple data source inputs. Each module can be deployed on the same device or a distributed platform. They each possess unique measurement mechanisms and technological advantages, and their synergistic effect can improve the system's robustness and accuracy in complex environments.

[0029] 1) GPS / GNSS positioning and navigation module: This module receives signals from satellite navigation systems such as the Global Positioning System and BeiDou to calculate the device's position and velocity on the Earth's surface. This module provides relatively stable global positioning capabilities and is fundamental for outdoor navigation. However, it is susceptible to signal blockage and multipath interference in weak signal environments such as tunnels, urban areas with tall buildings, or indoor spaces. 2) An IMU (Inertial Measurement Unit) consists of a gyroscope and an accelerometer, used to measure angular velocity and linear acceleration, respectively. By integrating these data, estimates of attitude change, velocity, and relative position can be obtained. IMUs are characterized by fast response and independence from external signal sources. They can still provide short-term, high-frequency position estimates even when GPS / GNSS positioning and navigation module signals are interrupted. However, due to accumulated errors, their long-term accuracy is limited. 3) Barometer module: This module measures atmospheric pressure and converts it into relative altitude information using an international standard atmospheric model. It is small in size and low in power consumption, and is often used to assist in altitude estimation. In multi-source sensor fusion systems, the barometer can serve as an altitude reference, enhancing the vertical positioning accuracy of the IMU inertial module and the GPS / GNSS positioning and navigation module, which is particularly valuable in situations where the GPS / GNSS positioning and navigation module has low accuracy or no altitude reference. 4) Visual / LiDAR SLAM module: A technology that uses cameras or LiDAR to achieve real-time localization and map building in unknown environments. Visual SLAM extracts environmental feature points from images, while LiDAR scans the environment's geometry using lasers. Both can provide high-precision localization and mapping capabilities in areas without GPS / GNSS positioning and navigation signals. Visual / LiDAR SLAM modules have advantages such as strong autonomy and adaptability to complex environments, but they are also relatively sensitive to perception conditions such as computing resources, lighting, and reflectivity.

[0030] 2. Trusted Layer at the Sensor End The purpose of this layer is to ensure that each type of data possesses tamper-proof, replay-proof, and traceable identity capabilities before entering the trusted central verification and processing layer. After data acquisition, each sensor module performs trusted verification and encapsulation on its output. The encapsulation process is carried out through a trusted signature module and a trusted encryption module.

[0031] 1) The trusted signature module mainly includes two functions: first, it interacts with the GPS / GNSS positioning and navigation module to digitally sign the positioning coordinates and add a timestamp and randomization factor to ensure uniqueness and anti-replayability; second, it interacts with the visual / laser SLAM module to sign the SLAM pose of each frame to prevent forgery and frame tampering. 2) The trusted encryption module mainly includes two functions: First, it interacts with the IMU inertial module to encrypt information such as acceleration and angular velocity using symmetric encryption, and then concatenates it with HMAC integrity verification to reliably package the transmitted information of the IMU inertial module; Second, it interacts with the barometer module to symmetrically encrypt the altitude measurement information, and then concatenates it with HMAC integrity verification to reliably transmit and protect the communication content.

[0032] 3. Trusted Center Verification Processing Layer All data transmitted by the sensors is uniformly received and processed by the Trusted Center Verification Processing Layer. This layer includes a data reception and scheduling module, a signature / HMAC verification module, a timestamp / randomization factor check module, a structural integrity verification module, a trusted data caching module, and an anomaly log recording module.

[0033] 1) Data Reception and Scheduling Module: This module is the core coordination unit in the multi-sensor system, primarily responsible for the unified reception, time synchronization, and scheduling management of reliable data from the aforementioned sensors. Its basic task is to ensure that sensor data enters the system's data processing flow in an orderly and efficient manner, avoiding data loss, delays, or timing discrepancies, thereby providing accurate and timely information support for subsequent positioning, navigation, and control. 2) The signature / HMAC verification module performs public key signature verification or HMAC verification on the incoming data packet to ensure that the source is trustworthy. If the verification is successful, the data is transmitted to the trusted data cache module; if the verification fails, the data is transmitted to the security response strategy module. 3) Timestamp / randomization factor checking module: compares the timestamp and randomization factor of the data to see if they are reasonable and if they have been used before, thereby preventing replay attacks. Verification is passed by transmitting the data to the trusted data cache module. If the verification fails, the data is transmitted to the security response strategy module. 4) The structural integrity verification module determines whether the data packet structure conforms to the trusted protocol specification, filters abnormal packets, and transmits the data that passes the verification to the trusted data cache module. If the verification fails, the data is transmitted to the security response strategy module. 5) Trusted data cache module: Only data that has passed all verifications will enter the trusted data cache module for subsequent use by the trust-aware weighted fusion layer; 6) Anomaly logging module: Records all verification failure data for security auditing and subsequent risk assessment.

[0034] This layer is the security core of the entire system, ensuring that data has complete identity authentication, timeliness verification and structural constraint capabilities before entering the trust-aware weighted fusion layer.

[0035] 4. Trust Perception Weighted Fusion Layer To ensure data reliability, the system introduces a trust management mechanism and a weighted fusion method to integrate multiple information sources. This layer improves positioning accuracy and fault tolerance in complex and dynamic environments by fusing redundant and reliable multi-source information while considering sensor trust levels. This layer includes a trust weight calculation module, a trust weighted fusion module, a fusion positioning result output module, and a covariance output module.

[0036] 1) Trust weight calculation module: Based on factors such as the historical reliability, error distribution, and attack detection of sensor data, dynamically evaluate the current trust level of each sensor, generate weight coefficients, and transmit them to the trust weighted fusion module. 2) Trust-weighted fusion module: Input the source data into the extended Kalman filter or the unscented Kalman filter, fuse the trust weights to achieve the optimal estimation of position and attitude, and finally output the data to the fusion positioning result output module and the covariance output module. 3) The fusion positioning result output module outputs the final estimated x, y, z coordinates and heading angle θ to represent the spatial position. The data is transmitted to the positioning and trajectory encryption and storage module (optional) and the navigation / control module. 4) Covariance output module: Outputs the error covariance matrix. Through a dynamic feedback mechanism, it ensures the stability and adaptability of the positioning system. Finally, it outputs the data to the trust weight calculation module to facilitate the updating of trust weights and assist in navigation control and behavior decision-making.

[0037] 5. Security Output and Audit Layer The security output and audit layer serves as the final output interface of the positioning system, providing both log auditing and real-time response capabilities to enhance the system's resilience and accountability in the face of attacks or failures. This layer includes an optional positioning and trajectory encryption and evidence storage module, a navigation / control module, and a security response strategy module.

[0038] 1) Location and trajectory encryption and evidence storage module (optional): The data comes from the fusion location result output module. Key location results are encrypted and stored for later retrospective auditing. 2) Navigation / Control Module: Data is sourced from the fusion positioning result output module and directly connects to downstream control units such as the flight control system and autopilot module; 3) Security response strategy module: The data comes from the abnormal log recording module. If persistent abnormalities or attack signs are detected during the data verification or fusion phase, fault tolerance mechanisms such as source demotion, backup sensor activation, task abort or return-to-home strategy can be triggered to ensure the safe operation of the system.

[0039] This invention introduces cryptographic signatures and randomization factor mechanisms into multi-source sensor data streams; the data receiver embeds a trusted verification chain to examine each data packet layer by layer; the fusion algorithm dynamically adjusts the participation degree based on trust weights to effectively resist malicious sensors or data pollution; the system provides a closed-loop output and auditing mechanism, ensuring that the positioning results are verifiable and traceable; it can be extended to edge computing or distributed collaborative positioning scenarios.

[0040] Example 3 This embodiment is basically the same in structure as Embodiments 1 and 2, except that, in practical applications, this embodiment takes the autonomous flight of a drone as an example: The multi-source perception data acquisition layer is deployed on the UAV platform. The GPS / GNSS module receives satellite signals in real time, the IMU module collects attitude data at high frequency, the barometer obtains altitude information, and the visual SLAM module extracts environmental features through the camera.

[0041] In the trusted layer of the sensor, GPS coordinates are digitally signed by the trusted signature module after being appended with a timestamp and a randomization factor. IMU data is symmetrically encrypted by the trusted encryption module and then concatenated for HMAC verification.

[0042] Data is transmitted to the trusted center verification and processing layer of the ground station, where it is sequentially scheduled, verified for signature, checked for timestamps, and verified for structural integrity according to the data scheduling rules. Data that passes the verification is stored in the cache.

[0043] The trust-aware weighted fusion layer evaluates trust weights based on historical data from each sensor. For example, it assigns higher weights when the GPS signal is good and automatically reduces the weights and increases the weights of SLAM data when the signal is interfered with. It then fuses the data by extending the Kalman filter and outputs the real-time position and error covariance of the UAV.

[0044] The security output and audit layer encrypts and stores the positioning results, while connecting to the flight control system to achieve navigation control. If continuous abnormal data is detected, the security response strategy module triggers the UAV to return to home or the mission to be aborted.

[0045] This embodiment achieves reliable positioning and safe operation of UAVs in complex environments through the collaborative work of various functional layers.

[0046] Compared with existing technologies, the technical solution of this invention uses cryptographic mechanisms to verify the authenticity, integrity, and timeliness of data sources, thereby improving the system's resistance to attacks; a trusted verification chain ensures the security of data during transmission and processing, preventing forgery and replay attacks; a trust-weighted fusion mechanism dynamically adapts to changes in sensor reliability, ensuring positioning accuracy in complex environments; a closed-loop audit mechanism makes positioning trajectories traceable, meeting security audit requirements; and a modular design supports the system's expanded applications in edge computing and distributed scenarios.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A reliable positioning system for autonomous flight based on multi-source data, characterized in that, include: A multi-source sensing data acquisition layer is used to deploy heterogeneous sensors to collect environmental information and motion status; The sensor-side trusted layer includes a trusted signature module and a trusted encryption module, which are used to perform trusted verification and encapsulation on the data output by the heterogeneous sensor. The Trusted Center Verification Processing Layer is used to uniformly receive, verify, and schedule the data output from the Trusted Layer at the sensor end. The trust-aware weighted fusion layer is used to weight and fuse verified data based on sensor trust levels, and output the positioning result and error covariance. The security output and audit layer is used to encrypt and store the positioning results, interface with the navigation control module, and trigger security response strategies when an anomaly is detected.

2. The autonomous flight reliable positioning system based on multi-source data according to claim 1, characterized in that, The heterogeneous sensors include a GPS / GNSS positioning and navigation module, an IMU inertial module, a barometer module, and a visual / laser SLAM module.

3. The autonomous flight reliable positioning system based on multi-source data according to claim 2, characterized in that, The GPS / GNSS positioning and navigation module is used to receive satellite navigation signals to calculate the device's position and velocity information. The IMU inertial module measures angular velocity and linear acceleration through gyroscopes and accelerometers to obtain attitude changes, velocity, and relative position estimates. The barometer module measures atmospheric pressure and converts it into relative altitude information. The visual / laser SLAM module is used to achieve positioning and map building in unknown environments.

4. The autonomous flight reliable positioning system based on multi-source data according to claim 2, characterized in that, The trusted signature module interacts with the GPS / GNSS positioning and navigation module to digitally sign the positioning coordinates and add a timestamp and randomization factor; the trusted signature module interacts with the visual / laser SLAM module to sign the SLAM pose of each frame.

5. The autonomous flight reliable positioning system based on multi-source data according to claim 2, characterized in that, The trusted encryption module interacts with the IMU inertial module, using symmetric encryption to encrypt acceleration and angular velocity information and then concatenating them for HMAC integrity verification; the trusted encryption module also interacts with the barometer module, using symmetric encryption to encrypt altitude measurement information and then concatenating it for HMAC integrity verification.

6. The autonomous flight reliable positioning system based on multi-source data according to claim 1, characterized in that, The trusted center verification processing layer includes: The data receiving and scheduling module is used to uniformly receive, synchronize, and schedule data from the trusted layer at the sensor end. The signature / HMAC verification module is used to perform public key signature verification or HMAC verification on incoming data packets to confirm the trustworthiness of the source. The timestamp / randomization factor checking module is used to compare the timestamps and randomization factors of the data to prevent replay attacks. The structural integrity verification module is used to determine whether the data packet structure conforms to the trusted protocol specification in order to filter out abnormal packets; A trusted data caching module is used to store data that has passed signature / HMAC verification, timestamp / randomization factor check and structural integrity verification, for use by the trust-aware weighted fusion layer; Exception logging module: Records verification failure data for security auditing.

7. The autonomous flight reliable positioning system based on multi-source data according to claim 1, characterized in that, The trust-aware weighted fusion layer includes: The trust weight calculation module is used to dynamically evaluate the trust level of sensors and generate weight coefficients based on the historical reliability, error distribution and attack detection of sensor data. The trust-weighted fusion module is used to input the data from each source into an extended Kalman filter or an unscented Kalman filter, and fuse the trust weights to achieve the optimal estimation of position and attitude. The integrated positioning result output module outputs spatial position coordinates and heading angle, which are then connected to the downstream control unit. The covariance output module is used to output the error covariance matrix and update the trust weights in the trust weight calculation module through a dynamic feedback mechanism.

8. The autonomous flight reliable positioning system based on multi-source data according to claim 1, characterized in that, The security output and audit layer includes: The location and trajectory encryption and evidence storage module is used to encrypt and store key location results for later retrospective auditing. The navigation / control module directly interfaces with the downstream control unit of the flight control system; The security response strategy module is used to trigger source degradation, backup sensor activation, mission abort, or return-to-home strategies when persistent anomalies or signs of attack are detected.

9. The autonomous flight reliable positioning system based on multi-source data according to claim 1, characterized in that, The sensor modules of the multi-source sensing data acquisition layer are deployed on the same device or distributed platform to achieve collaborative acquisition of heterogeneous data.

10. The positioning method of the autonomous flight reliable positioning system based on multi-source data according to claim 1, characterized in that, Includes the following steps: S1. Data Acquisition Steps: Through the GPS / GNSS positioning and navigation module, IMU inertial module, barometer module, and visual / laser SLAM module of the multi-source sensing data acquisition layer, position velocity, angular velocity / acceleration, relative altitude, environmental features, and pose data are acquired respectively. S2, Data Trusted Encapsulation Steps: Use the trusted signature module of the trusted layer at the sensor end to digitally sign the GPS / GNSS positioning coordinates and SLAM pose, and add a timestamp and randomization factor. Use the trusted encryption module to symmetrically encrypt the IMU data and barometer altitude information and then perform HMAC integrity verification. S3. Data verification and processing steps: The receiving and scheduling module of the trusted center verification and processing layer receives and synchronizes the encapsulated data in a unified manner. It then sequentially performs signature / HMAC verification, timestamp / randomization factor check, and structural integrity verification according to the data scheduling rules. Data that passes verification is stored in the trusted data cache module, and data that fails verification is logged as an anomaly. S4. Trust-weighted fusion step: The trust weight calculation module of the trust perception weighted fusion layer generates trust weights based on the historical reliability of the sensor, error distribution and attack detection. The trust weighted fusion module inputs the trusted data into the Kalman filter and fuses the trust weights, outputting the positioning result and error covariance. The covariance output module feeds the error covariance back to the trust weight calculation module to update the weights. S5. Security Output and Response Steps: The security output and audit layer transmits the positioning results to the navigation / control module. Optionally, the positioning and trajectory encryption and evidence storage module can be used for encrypted storage. If an anomaly or attack is detected, the security response strategy module triggers a preset fault tolerance mechanism.

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