Redundant integrated navigation method and system based on unmanned aerial vehicle

Abstract

The invention provides a redundancy integrated navigation method and system based on an unmanned aerial vehicle, the core of the redundancy integrated navigation method lies in a double-layer redundancy mechanism of multi-sensor hardware and multi-fusion-device algorithm software and a multi-fusion-device smooth switching mechanism, and the method comprises the following steps: acquiring sensor data and preprocessing the sensor data; fault diagnosis and performance evaluation are carried out on the preprocessed sensor data, and sensor redundancy switching is comprehensively judged and executed; performing multi-fuser algorithm software resolving and performance evaluation on the sensor data after redundancy processing, and comprehensively judging and executing fuser redundancy switching; and carrying out dynamic delay compensation and structured packaging release on the fusion device navigation state data after redundancy processing. The system is used for implementing the method. The problems that a traditional redundant integrated navigation system is large in hardware resource consumption, insufficient in reliability and the like are solved, and the robustness and reliability of the unmanned aerial vehicle integrated navigation system are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle navigation, in particular to a redundancy combined navigation method and system based on unmanned aerial vehicles. BACKGROUND

[0002] The navigation system of an unmanned aerial vehicle provides necessary state data for the flight of the unmanned aerial vehicle. The sensors used by the navigation system mainly include an inertial measurement unit (IMU), a global satellite positioning navigation system (GNSS), a barometer, a magnetic compass, etc. The inertial navigation system directly measures the acceleration and angular velocity information of the carrier through the IMU and is not affected by external environment interference. However, the integral characteristic of the inertial navigation system causes the navigation error to continuously accumulate over time. For a low-precision IMU used by a small micro unmanned aerial vehicle, the error will accumulate more quickly. The satellite navigation system can provide three-dimensional position and velocity information and can be directly used for navigation positioning. However, it is greatly affected by the external environment. Once the satellite signal is disturbed, the measurement accuracy of the GNSS sensor will rapidly decrease. The barometer measures the atmospheric pressure and calculates the flight height of the unmanned aerial vehicle through the conversion relationship with the standard atmospheric pressure at sea level. The magnetic compass measures the three-axis geomagnetic field strength of the environment where the unmanned aerial vehicle is located and calculates the heading angle of the unmanned aerial vehicle. However, the barometer and the magnetic compass can only provide single state observation information. Based on the characteristics of the above various sensors, the navigation system of the unmanned aerial vehicle usually adopts a redundancy combined navigation method based on multi-sensor information fusion to obtain navigation state estimation information with more dimensions, higher accuracy, and greater stability than single sensors.

[0003] The performance of a sensor usually directly affects the state estimation performance of the navigation system. When a sensor fails, it may affect the navigation accuracy, and in severe cases, it may even cause a flight safety accident. In engineering applications, redundancy combined navigation design is usually adopted to avoid the influence caused by the failure of a single sensor.

[0004] Traditional redundancy combined navigation systems include sensor and processor redundancy design and sensor redundancy design.

[0005] The sensor and processor redundancy design scheme adopts multiple independent hardware subsystems. Each subsystem includes independent and complete sensors, processors, and algorithm software and can independently output navigation state information such as attitude, velocity, and position. The subsystems are hard backups for each other, ensuring that the combined navigation system can continue to operate normally under the condition that part of the subsystems fail, thereby enhancing the reliability of the system. Although this design scheme can achieve good redundancy combined navigation system design, it has the disadvantages of large hardware resource consumption, large size, and high cost.

[0006] The sensor redundancy design scheme only makes redundancy at the sensor hardware level, adopts a single processor and algorithm software, and although this processing manner can solve the hardware failure of the navigation system caused by sensor failure at a lower cost, it cannot solve the software problem of the navigation system caused by abnormal algorithm running, and the system robustness and safety are insufficient. SUMMARY

[0007] The purpose of the present application is to provide a redundancy combined navigation method and system based on a UAV, which significantly improves the robustness and reliability of the combined navigation of the UAV through a double-layer redundancy mechanism of multi-sensor hardware and multi-fusion algorithm software and a multi-fusion smooth switching mechanism.

[0008] The present application also provides a redundancy combined navigation method based on a UAV, comprising: Based on the sensor raw measurement data, the raw measurement data is preprocessed, and numerical optimization and down-sampling are performed to obtain processed sensor data; Based on the preprocessed sensor data, multi-sensor fault diagnosis and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; Based on the sensor state observation data after switching, multi-fusion independent navigation solution and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; Based on the fusion delay time domain navigation state data after switching, time domain delay compensation is performed to obtain current time domain navigation state data, which is encapsulated and processed to publish navigation data.

[0009] Optionally, in the redundancy combined navigation method based on a UAV described in the present application, based on the sensor raw measurement data, the raw measurement data is preprocessed, and numerical optimization and down-sampling are performed to obtain processed sensor data, which specifically comprises: Compensate for zero bias error and scale factor error of sensor data based on preset calibration parameters; Establish a sensor temperature characteristic curve through high and low temperature environment test, and perform real-time online temperature compensation based on the temperature characteristic curve; Synchronize the sensor data in time through timestamp alignment, and identify and eliminate abnormal data based on statistical methods; Perform online adaptive filtering and smoothing processing of the sensor data based on fast Fourier transform (FFT) to obtain optimized sensor data; Down-sample the optimized sensor data based on the equal-interval average method to obtain the processing result.

[0010] Optionally, in the redundancy combined navigation method based on a UAV described in the present application, based on the preprocessed sensor data, multi-sensor fault diagnosis is performed to determine whether redundancy switching is needed, which specifically comprises: measuring value over-limit determination on the sensor raw data based on a preset threshold value; data loss state is identified through data continuity analysis; sensor stuck detection is realized by combining time window value comparison; comprehensive over-limit detection, data loss detection and stuck detection, output fault diagnosis result; If the diagnosis result is normal, sensor performance evaluation is performed, if the diagnosis result diagnoses a fault response, sensor switching is performed, sensor switching execution accesses the backup sensor data to the fusion data interface, and the faulty sensor is cut out of the fusion data interface.

[0011] Optionally, in the redundancy combined navigation method based on the unmanned aerial vehicle described in the present application, based on the preprocessed sensor data, the comprehensive performance of the multiple sensors is evaluated to determine whether redundancy switching is needed, specifically including: FFT is used to perform frequency spectrum analysis on the sensor data to identify abnormal vibration signals; The output difference of the redundant sensors is compared through multi-sensor cross-consistency verification, and the redundant sensors refer to a plurality of sensor sets of the same type; The residual error is calculated through the aircraft physical model, geographic information and kinematic constraints to determine the degree of deviation of the data from the theoretical value; According to the performance analysis result, the confidence weight of each sensor is dynamically updated, and it is judged whether to switch the sensor.

[0012] Optionally, in the redundancy combined navigation method based on the unmanned aerial vehicle described in the present application, based on the sensor state observation data after switching, multi-fusion independent navigation calculation and comprehensive performance evaluation are performed to determine whether redundancy switching is needed, specifically including: Based on the data after sensor switching, the multi-fusion uses error state Kalman filter (ESKF) algorithm to generate multiple sets of navigation calculation results; Based on the navigation calculation result, chi-square test and residual error analysis method are used to calculate the comprehensive confidence score combined with the sensor noise level and historical reliability, and the score is taken as the fusion evaluation result; Based on the fusion evaluation result, it is judged whether to switch the fusion, and when the fusion is switched, low-pass filter algorithm is used to suppress the jump in the switching process, and the fusion switching execution cuts out the main fusion navigation state data and accesses the backup fusion navigation state data.

[0013] Optionally, in the redundancy combined navigation method based on the unmanned aerial vehicle described in the present application, based on the delayed time domain navigation state data of the fusion after switching, time domain delay compensation is performed to obtain the current time domain navigation state data, and the current time domain navigation state data is packaged and processed to publish navigation data, specifically including: Estimating the delay error between the observation data and the predicted state based on the least square method online; Based on the delay error, the navigation state data of the current time domain is generated by extrapolation of the real-time measurement data of the IMU; The navigation state data after time domain synchronization is structured and packaged with the fusion state data and published.

[0014] In a second aspect, the embodiments of the present application provide a redundancy combined navigation system based on a UAV, which comprises a memory and a processor, wherein the memory comprises a program of a redundancy combined navigation method based on a UAV, and the program of the redundancy combined navigation method based on a UAV is implemented when the processor is executed to realize the steps of the redundancy combined navigation method based on a UAV described above.

[0015] As can be seen from the above, the redundancy combined navigation method and system based on a UAV provided by the present application, by preprocessing the original measurement data based on the sensor, and performing numerical optimization and downsampling, the processed sensor data is obtained; based on the preprocessed sensor data, multi-sensor fault diagnosis and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; based on the sensor state observation data after switching, multi-fusion independent navigation solving and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; based on the navigation state data of the delay time domain of the fusion after switching, time domain delay compensation is performed to obtain the current time domain navigation state data, and the current time domain navigation state data is packaged and processed to publish the navigation data; through the dual redundancy mechanism of multi-sensor hardware and multi-fusion algorithm software and the multi-fusion smooth switching mechanism, the robustness and reliability of the combined navigation of the UAV are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the present application. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0017] Figure 1 The flowchart of the redundancy combined navigation method based on a UAV provided by the present application; Figure 2 The detailed flowchart of the redundancy combined navigation method based on a UAV provided by the present application; Figure 3 The overall block diagram of the redundancy combined navigation system based on a UAV provided by the present application; Figure 4 The composition and connection diagram of each subsystem of the redundancy combined navigation system based on a UAV provided by the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, only some embodiments of the present application are described, rather than all the embodiments. The components of the present application described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0019] It should be noted that similar reference numbers and letters represent similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0020] Please refer to Figures 1-2 . Figure 1 is a flow chart of a redundancy combined navigation method based on a UAV of the present application. Figure 2 is a detailed flow chart of a redundancy combined navigation method based on a UAV of the present application. The redundancy combined navigation method based on a UAV is used in a terminal device. The redundancy combined navigation method based on a UAV includes the following steps: Based on the original sensor measurement data, the original measurement data is preprocessed, and numerical optimization and down-sampling are performed to obtain processed sensor data; Based on the preprocessed sensor data, multi-sensor fault diagnosis and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; Based on the switched sensor state observation data, multi-fusion independent navigation solution and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; Based on the switched fusion delayed time domain navigation state data, time domain delay compensation is performed to obtain current time domain navigation state data, and encapsulation processing is performed on the current time domain navigation state data to publish navigation data.

[0021] It should be noted that based on the sensor hardware outside the system, the data processing subsystem collects the raw measurement data of the redundant IMU, GNSS, magnetic compass and barometer and other sensors, and performs preliminary data processing (down-sampling, filtering, etc.) on these data, and transmits the processed data to the sensor redundancy subsystem as sensor data; the sensor redundancy subsystem performs operation analysis and judgment processing on the sensor data, converts it into state observation data and transmits it to the fusion redundancy subsystem; the fusion redundancy subsystem receives the state observation data, performs data fusion algorithm operation processing, and generates navigation state data and transmits it to the data output subsystem; the data output subsystem performs time domain delay compensation processing on the received navigation state data and outputs it.

[0022] According to the embodiment of the application, based on the sensor raw measurement data, the raw measurement data is preprocessed, and numerical optimization and down-sampling are performed to obtain processed sensor data, specifically including: Compensate for zero bias error and scale factor error of sensor data based on preset calibration parameters; Establish a sensor temperature characteristic curve through high and low temperature environment test, and perform real-time online temperature compensation based on the temperature characteristic curve; Synchronize the sensor data in time through timestamp alignment, and identify and eliminate abnormal data based on statistical method; Perform online adaptive filtering and smoothing processing of sensor data based on fast Fourier transform (FFT) to obtain optimized sensor data; Down-sample the optimized sensor data based on equal-interval averaging method to obtain the processing result.

[0023] According to the embodiment of the application, based on the preprocessed sensor data, multi-sensor fault diagnosis is performed to determine whether redundancy switching is needed, specifically including: Determine the measurement value of the sensor raw data based on the preset threshold; Identify the data loss state through data continuity analysis; Realize sensor stuck detection by combining time window value comparison; Integrate the above-mentioned detection results of over-limit detection, data loss detection and stuck detection to output the fault diagnosis result; If the diagnosis result is normal, perform sensor performance evaluation, and if the diagnosis result diagnoses a fault response, perform sensor switching.

[0024] It should be noted that the measurement value of the sensor raw data is determined based on the preset threshold, the data loss state is identified through data continuity analysis, the sensor stuck detection is realized by combining time window value comparison, and the above-mentioned detection results are integrated, if it is normal data, it is output to the sensor evaluation module, if a fault response is detected, the sensor switching instruction is triggered.

[0025] According to the embodiment of the present application, based on the pre-processed sensor data, the multi-sensor comprehensive performance evaluation is carried out to determine whether the redundancy switching is needed, specifically including: The FFT is used to perform the spectrum analysis on the sensor data to identify the abnormal vibration signal; The output difference of the redundant sensors is compared through the multi-sensor cross-consistency verification, and the redundant sensors refer to a plurality of sensor sets of the same type; The residual error is calculated through the aircraft physical model, geographic information and kinematic constraints to determine the deviation degree of the data from the theoretical value; According to the performance analysis result, the confidence weight of each sensor is dynamically updated, and it is determined whether the sensor switching is needed.

[0026] It should be noted that the output difference of the redundant sensors is compared through the multi-sensor cross-consistency verification, the residual error is calculated through the aircraft physical model, geographic information and kinematic constraints to determine the deviation degree of the data from the theoretical value, and according to the analysis result, the confidence weight of each sensor is dynamically updated and output to the sensor switching module. The sensor switching module performs the optimal sensor combination decision: based on the real-time confidence weight, the main sensor data group and the auxiliary sensor data group composed of two groups of IMU data, one group of magnetic compass data, one group of barometer data and one group of GNSS data are selected from the redundant sensor pool, and the selected two groups of sensor outputs are output to the fusioner redundancy subsystem. Through the above steps, the sensor redundancy subsystem ensures that the two groups of sensor data input into the fusioner redundancy subsystem are normal through the fault diagnosis-evaluation-switching three-level control mechanism.

[0027] According to the embodiment of the present application, based on the sensor state observation data after the switching, the multi-fusioner independent navigation solution and comprehensive performance evaluation are carried out to determine whether the redundancy switching is needed, specifically including: Based on the data after the sensor switching, the multi-fusioner uses the error state Kalman filter (ESKF) algorithm to generate a plurality of navigation solution results; Based on the navigation solution result, the chi-square test and residual error analysis method are used to calculate the comprehensive confidence score in combination with the sensor noise level and historical reliability, and the comprehensive confidence score is used as the fusioner evaluation result; Based on the fusioner evaluation result, it is determined whether the fusioner switching is needed, and when the fusioner switching is needed, the low-pass filter algorithm is used to suppress the jump in the switching process.

[0028] It should be noted that the fusion redundancy subsystem is composed of a fusion module, a fusion evaluation module and a fusion switching module, and the reliability of state estimation is realized through a double-channel fault-tolerant fusion and dynamic switching mechanism. The fusion module adopts a double-independent fusion algorithm parallel operation software architecture: the main fusion and the backup fusion respectively receive the main sensor group data and the auxiliary sensor group data output by the sensor redundancy subsystem, based on the error state Kalman filter (ESKF) algorithm, a 16-dimensional error state vector containing position error, velocity error, attitude error, angular velocity error and acceleration error is defined, the nominal state prediction, error covariance matrix update and observation residual calculation are performed by recursion, and the error state injection and covariance reset are completed based on the observation data of the accelerometer, GNSS and magnetic compass, the double-channel navigation solution result is generated and output to the fusion evaluation module. The fusion evaluation module performs multi-dimensional verification of navigation performance: the statistical significance of the solution residual is determined by chi-square test, the filter convergence is evaluated by residual covariance matrix, the comprehensive credibility score of the fusion result is calculated by combining factors such as sensor noise level and historical reliability, and the evaluation result is transmitted to the fusion switching module. The fusion switching module performs seamless switching of navigation output: based on the credibility score, the double-channel solution results are compared in real time, when the performance of the main fusion deteriorates, a low-pass filter algorithm is used to weight the transition of the main and backup fusion outputs, and the state jump in the switching process is suppressed, and finally the stable navigation state data is output to the data output subsystem. Through the above steps, the fusion redundancy subsystem realizes the accurate output of navigation state estimation in the fusion algorithm abnormal scene through the three-level cooperative mechanism of double-redundancy solution-dynamic evaluation-smooth switching, and significantly improves the robustness and reliability of the integrated navigation system.

[0029] According to the embodiment of the present application, based on the delayed time domain navigation state data of the fusion after switching, time domain delay compensation is performed to obtain current time domain navigation state data, and encapsulation processing is performed thereon to publish navigation data, specifically including: Based on the least square method, the delay error between the observation data and the predicted state is estimated online; Based on the delay error, the current time domain navigation state data is generated by extrapolation of the real-time measurement data of the IMU; The time domain synchronized navigation state data and the fusion state data are structured and encapsulated and published.

[0030] It should be noted that the data output subsystem is composed of a time domain delay compensation module and a data packaging module, and high timeliness and integrity of navigation data are realized through dynamic compensation and structured packaging. The time domain delay compensation module performs adaptive time domain synchronization: according to the current motion intensity of the aircraft, the storage length of the navigation state history buffer is dynamically adjusted, the delay error between the observation data and the predicted state is estimated online based on the least square method, the navigation state information of the current time domain is generated through extrapolation of the real-time measurement data of the IMU, and the time lag caused by the data transmission and processing link is eliminated. The data packaging module performs multi-dimensional information integration: the navigation state information after time domain synchronization and the system running state information are structured and packaged, wherein the navigation state information includes attitude, speed, position, etc., the system running state information covers sensor health flag, fusion device running state information, switching flag and other information, and the packaged data packet is output through a standard communication protocol. Through the above steps, the data output subsystem ensures the real-time and analyzability of the navigation data through dynamic delay compensation and structured data packaging technology, and provides a complete state feedback data chain.

[0031] As shown in Figures 3-4 The second aspect, the embodiments of the present application provide a redundancy combined navigation system based on unmanned aerial vehicle, which comprises a memory and a processor, the memory comprises a redundancy combined navigation method based on unmanned aerial vehicle, and the redundancy combined navigation method based on unmanned aerial vehicle is implemented when the processor executes the following steps: Based on the original sensor measurement data, the original measurement data is preprocessed, and numerical optimization and down-sampling are performed to obtain the processed sensor data; Based on the preprocessed sensor data, multi-sensor fault diagnosis and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; Based on the switching sensor state observation data, multi-fusion device independent navigation solution and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; Based on the switching fusion device delay time domain navigation state data, time domain delay compensation is performed to obtain the current time domain navigation state data, and the current time domain navigation state data is packaged and processed to publish the navigation data.

[0032] It should be noted that based on the sensor hardware outside the system, the data processing subsystem collects the original measurement data of the redundant IMU, GNSS, magnetic compass and barometer and the like, and performs preliminary data processing (down-sampling, filtering and the like) on these data, and transmits the processed data to the sensor redundancy subsystem as sensor data; the sensor redundancy subsystem performs operation analysis and judgment processing on the sensor data, converts the state observation data into the fusion redundancy subsystem; the fusion redundancy subsystem receives the state observation data, performs data fusion algorithm operation processing, and generates navigation state data to the data output subsystem; the data output subsystem performs time domain delay compensation processing on the received navigation state data and outputs.

[0033] According to the embodiment of the application, based on the sensor raw measurement data, the raw measurement data is preprocessed, and numerical optimization and down-sampling are performed to obtain processed sensor data, specifically including: Compensate the zero bias error and scale factor error of the sensor data based on the preset calibration parameters; Establish a sensor temperature characteristic curve through high and low temperature environment test, and perform real-time online temperature compensation based on the temperature characteristic curve; Synchronize the sensor data in time through timestamp alignment, and identify and eliminate abnormal data based on statistical method; Perform online adaptive filtering and smoothing processing of the sensor data based on fast Fourier transform (FFT) to obtain optimized sensor data; Down-sample the optimized sensor data based on the equal interval average method to obtain the processing result.

[0034] It should be noted that the data processing subsystem is composed of a preprocessing module, a numerical optimization module and a down-sampling module, and the accurate optimization of the sensor data is realized through a multi-stage data processing process. The preprocessing module performs three-stage error compensation, and the processing result is output to the sensor redundancy subsystem. Through the above steps, the data processing subsystem realizes the step-by-step correction of sensor errors and the accurate processing of data density through a layered data processing mechanism.

[0035] According to the embodiment of the application, based on the processing result, sensor fault diagnosis is performed to obtain a diagnosis result, specifically including: Measure the sensor data based on the processing result to obtain a measurement value; Compare the measurement value with the set threshold to obtain a measurement deviation rate; Determine whether the measurement deviation rate is greater than or equal to the set measurement deviation rate threshold; If greater than or equal to the set measurement deviation rate threshold, perform frequency spectrum analysis on the sensor data using FFT to identify abnormal vibration signals, and obtain a diagnosis result; If less than the set measurement deviation rate threshold, it is determined that the sensor is not faulty.

[0036] It should be noted that the sensor raw data is measured based on the preset threshold, the data loss state is identified through data continuity analysis, the sensor stuck detection is realized by combining the time window value comparison, if the detection result is normal data, it is output to the sensor evaluation module, if the fault response is detected, the sensor switching instruction is triggered, the sensor fault diagnosis module belongs to the sensor redundancy subsystem, and is used for realizing sensor hardware fault identification.

[0037] According to the embodiment of the application, based on the preprocessed sensor data, the comprehensive performance of the multi-sensor is evaluated, and it is judged whether the redundancy switching is needed, specifically comprising: The FFT is used for performing spectrum analysis on the sensor data to identify abnormal vibration signals; The output difference of the redundant sensor is compared through the multi-sensor cross consistency verification; The residual error is calculated through the aircraft physical model, geographic information and kinematic constraint, and the deviation degree of the data from the theoretical value is determined; According to the performance analysis result, the confidence weight of each sensor is dynamically updated, and it is judged whether the sensor switching is needed.

[0038] It should be noted that the sensor performance evaluation module belongs to the sensor redundancy subsystem, the output difference of the redundant sensor is compared through the multi-sensor cross consistency verification, the residual error is calculated through the aircraft physical model, geographic information and kinematic constraint, the deviation degree of the data from the theoretical value is determined, and according to the analysis result, the confidence weight of each sensor is dynamically updated and output to the sensor switching module. The sensor switching module belongs to the sensor redundancy subsystem, and executes the optimal sensor combination decision: based on the real-time confidence weight, the main sensor data group and the auxiliary sensor data group composed of two groups of IMU data, one group of magnetic compass data, one group of barometer data and one group of GNSS data are selected from the redundant sensor pool, and the selected two groups of sensor data are output to the fusioner redundancy subsystem. Through the above steps, the sensor redundancy subsystem realizes the three-level control mechanism of fault diagnosis-evaluation-switching, and ensures that the two groups of sensor data input into the fusioner redundancy subsystem are normal.

[0039] According to the embodiment of the application, based on the sensor state observation data after switching, the multi-fusioner independent navigation solution and comprehensive performance evaluation are performed, and it is judged whether the redundancy switching is needed, specifically comprising: Based on the data after the sensor switching, the multi-fusioner uses the error state Kalman filter (ESKF) algorithm to generate a plurality of groups of navigation solution results; Based on the navigation solution result, the chi-square test and residual analysis method are used, combined with the sensor noise level and historical reliability to calculate the comprehensive confidence score, and the score is taken as the evaluation result of the fusion device; Based on the evaluation result of the fusion device, it is judged whether to perform fusion device switching, and when the fusion device switching is performed, a low-pass filtering algorithm is used to suppress the jump in the switching process.

[0040] It should be noted that the fusion device redundancy subsystem is composed of a fusion device module, a fusion device evaluation module and a fusion device switching module, and the reliability of state estimation is realized through a double-channel fault-tolerant fusion and dynamic switching mechanism. The fusion device module adopts a double-independent fusion device algorithm parallel operation software architecture: the main fusion device and the backup fusion device respectively receive the main sensor group data and the auxiliary sensor group data output by the sensor redundancy subsystem, based on the error state Kalman filter (ESKF) algorithm, defines a 16-dimensional error state vector containing position error, velocity error, attitude error, angular velocity error and acceleration error, performs nominal state prediction, error covariance matrix update and observation residual calculation through recursion, and completes error state injection and covariance reset based on the observation data of the accelerometer, GNSS and magnetic compass, generates double-channel navigation solution results and outputs them to the fusion device evaluation module. The fusion device evaluation module performs multi-dimensional verification of navigation performance: the statistical significance of the solution residual is determined by chi-square test, the filter convergence is evaluated by residual covariance matrix, the comprehensive confidence score of the fusion result is calculated by combining the sensor noise level and historical reliability, and the evaluation result is transmitted to the fusion device switching module. The fusion device switching module performs seamless switching of navigation output: based on the confidence score, the double-channel solution results are compared in real time, when the performance of the main fusion device deteriorates, a low-pass filtering algorithm is used to weight the transition of the main and backup fusion device outputs, to suppress the state jump in the switching process, and finally the stable navigation state data is output to the data output subsystem. Through the above steps, the fusion device redundancy subsystem realizes accurate output of the navigation state estimation under the abnormal scene of the fusion device algorithm through a three-level cooperative mechanism of double-redundancy solution-dynamic evaluation-smooth switching, significantly improving the robustness and reliability of the integrated navigation system.

[0041] According to the embodiment of the application, based on the delayed time domain navigation state data of the fusion device after switching, time domain delay compensation is performed to obtain current time domain navigation state data, and the current time domain navigation state data is encapsulated and processed to publish navigation data, specifically including: Based on the least square method, the delay error between the observation data and the predicted state is estimated online; Based on the delay error, the navigation state data in the current time domain is generated by extrapolation through the real-time measurement data of the IMU; The time domain synchronized navigation state data and the fusion device state data are structured and encapsulated and published.

[0042] It should be noted that the fusion state data is state flag information and navigation solution information in the running process of the fusion device. The data output subsystem is composed of a time domain delay compensation module and a data packaging module, and high timeliness and integrity of the navigation data are realized through dynamic compensation and structured packaging. The time domain delay compensation module performs adaptive time domain synchronization: according to the current motion intensity of the aircraft, the storage length of the navigation state history buffer is dynamically adjusted, the delay error between the observed data and the predicted state is estimated online based on the least square method, the navigation state information of the current time domain is generated through extrapolation of the real-time measurement data of the IMU, and the time lag caused by the data transmission and processing link is eliminated. The data packaging module performs multi-dimensional information integration: the navigation state information after time domain synchronization and the system running state information are structured and packaged, wherein the navigation state information includes attitude, speed, position, etc., and the system running state information covers sensor health flag, fusion device running state information, switching flag and other information, and the packaged data packet is output through a standard communication protocol. Through the above steps, the data output subsystem ensures the real-time and analyzability of the navigation data through dynamic delay compensation and structured data packaging technology, and provides a complete state feedback data chain.

[0043] The third aspect of the present application provides a computer readable storage medium, the readable storage medium comprising a program of a redundancy combined navigation method based on a UAV, the program of the redundancy combined navigation method based on the UAV being executed by a processor to implement the steps of any of the above redundancy combined navigation methods based on the UAV.

[0044] The redundancy combined navigation method and system based on a UAV disclosed in the present application, by based on the sensor raw measurement data, the raw measurement data is preprocessed, and numerical optimization and downsampling are performed to obtain processed sensor data; based on the preprocessed sensor data, multi-sensor fault diagnosis and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; based on the observed data of the sensor state after switching, multi-fusion device independent navigation solution and comprehensive performance evaluation are performed to determine whether redundancy switching is needed; based on the delay time domain navigation state data of the fusion device after switching, time domain delay compensation is performed to obtain current time domain navigation state data, and the current time domain navigation state data is packaged and processed to publish navigation data; through the dual redundancy mechanism of multi-sensor hardware and multi-fusion device algorithm software and the multi-fusion device smooth switching mechanism, the robustness and reliability of the combined navigation of the UAV are significantly improved.

[0045] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other manners. The above described device embodiments are merely exemplary. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0046] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units; they can be located in one place, or distributed on multiple network units; and part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0047] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be realized in the form of hardware or in the form of hardware plus software functional unit.

[0048] Those of ordinary skill in the art can understand that all or part of the steps of the above method embodiments can be completed by a program instructing related hardware, and the foregoing program can be stored in a readable storage medium, and when the program is executed, the steps of the method embodiments are executed; and the foregoing storage medium includes mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic discs or optical discs, and various media that can store program codes.

[0049] Alternatively, the integrated units of the present application, if implemented in the form of software functional modules and sold or used as independent products, can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. The foregoing storage medium includes mobile storage devices, ROMs, RAMs, magnetic discs or optical discs, and various media that can store program codes.

Claims

1. A redundant combined navigation method based on drones, characterized in that: include: Based on the original sensor measurement data, the original measurement data is preprocessed, and numerical optimization and downsampling are performed to obtain processed sensor data; Based on the pre-processed sensor data, multi-sensor fault diagnosis and comprehensive performance evaluation are performed to determine whether redundant switching is required; Based on the sensor status observation data after switching, perform multi-fusion independent navigation solution and comprehensive performance evaluation to determine whether redundant switching is needed; Based on the delayed time domain navigation state data of the fusion device after switching, time domain delay compensation is performed to obtain the current time domain navigation state data, which is then packaged and processed to publish the navigation data.

2. The redundant combined navigation method based on drone according to claim 1, characterized in that: Based on the original sensor measurement data, the original measurement data is preprocessed, and numerical optimization and downsampling are performed to obtain the processed sensor data, which specifically includes: Perform zero bias error compensation and scale factor error compensation on sensor data based on preset calibration parameters; Establish the sensor temperature characteristic curve through high and low temperature environment tests, and perform real-time online temperature compensation based on the temperature characteristic curve; Synchronize sensor data through timestamp alignment and identify and eliminate abnormal data based on statistical methods; Perform online adaptive filtering and smoothing of sensor data based on Fast Fourier Transform (FFT) to obtain optimized sensor data; The optimized sensor data is downsampled based on the equal interval averaging method to obtain the processing results.

3. The redundant combined navigation method based on unmanned aerial vehicle according to claim 1, characterized in that: Based on the pre-processed sensor data, multi-sensor fault diagnosis is performed to determine whether redundant switching is required, including: Based on the preset threshold, the sensor raw data is judged as exceeding the measurement value limit; Identify data loss status through data continuity analysis; Combined with time window value comparison to achieve sensor stuck detection; Comprehensive over-limit detection, data loss detection, and jam detection, outputting fault diagnosis results; If the diagnosis result is normal, the sensor performance evaluation is performed. If the diagnosis result diagnoses a fault response, the sensor is switched.

4. The redundant combined navigation method based on unmanned aerial vehicle according to claim 1, characterized in that: Based on the pre-processed sensor data, a comprehensive multi-sensor performance evaluation is performed to determine whether redundant switching is required. Specifically, the evaluation includes: Use FFT to perform spectrum analysis on sensor data to identify abnormal vibration signals; Comparing the output differences of redundant sensors through multi-sensor cross-consistency verification, wherein the redundant sensors refer to a set of multiple sensors of the same type; Calculate the residuals by using the aircraft physical model, geographic information and kinematic constraints to determine the degree of deviation between the data and the theoretical value; According to the above performance analysis results, the confidence weight of each sensor is dynamically updated and it is determined whether to switch the sensor.

5. The redundant combined navigation method based on unmanned aerial vehicle according to claim 1, characterized in that: Based on the sensor status observation data after switching, perform multi-fusion independent navigation solution and comprehensive performance evaluation to determine whether redundant switching is necessary, including: Based on the data after sensor switching, the multi-fusion device uses the error state Kalman filter algorithm to generate multiple sets of navigation solution results; Based on the navigation solution results, the chi-square test and residual analysis methods are used to calculate the comprehensive credibility score combined with the sensor noise level and historical reliability, and this score is used as the fusion device evaluation result; Based on the fusion device evaluation results, it is determined whether to switch the fusion device. When switching the fusion device, a low-pass filtering algorithm is used to suppress the jump during the switching process.

6. The redundant combined navigation method based on unmanned aerial vehicle according to claim 1, characterized in that: Based on the delayed time-domain navigation state data of the fusion device after switching, time-domain delay compensation is performed to obtain the current time-domain navigation state data, which is then packaged and processed to publish the navigation data, specifically including: Online estimation of the delay error between the observed data and the predicted state based on the least squares method; Based on the delay error, the navigation status data of the current time domain is generated by extrapolating the real-time measurement data of the IMU; The navigation status data and fusion device status data after time domain synchronization are structured and packaged and published.

7. A redundant integrated navigation system based on drones, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of a redundant combined navigation method based on an unmanned aerial vehicle, and the processor executes the method according to any one of claims 1 to 6.

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