Fault detection and isolation method for integrated navigation system of unmanned aerial vehicle

Abstract

The invention discloses a fault detection and isolation method for an integrated navigation system of an unmanned aerial vehicle, which comprises the following steps of performing fault detection andisolation according to original data output by an IMU (Inertial Measurement Unit), and performing fault detection and isolation on each piece of measurement information to ensure high reliability of the integrated navigation system of the unmanned aerial vehicle. According to the method, the calculated amount is small, IMU faults and measurement faults can be detected in real time, sensors with faults are isolated, and the number of IMUs installed in a non-coaxial mode is not limited. According to the method, the IMU unit data source is directly detected, IMU faults can be prevented from beinginjected into the filter, it is guaranteed that the filter is not polluted by the faults, fault detection and isolation are carried out on each measurement, and the method is suitable for fault detection and isolation of various sensors with different rates.

Description

technical field [0001] The invention belongs to the field of unmanned aerial vehicles, and in particular relates to a fault detection and isolation method for an integrated navigation system of an unmanned aerial vehicle. Background technique [0002] At present, the development of drone technology is advancing by leaps and bounds, and the application fields based on drones are becoming more and more extensive. It has been widely used in military reconnaissance, battlefield surveillance, fire detection, environment and traffic monitoring. The surrounding environment of the UAV is complex and changeable during the execution of the mission, and all kinds of sensors may have short-term failures. In order to ensure the high reliability of the UAV integrated navigation system and the smooth execution of the mission, sensor hardware redundancy can be designed. It is very important to make good use of redundant sensor information, and to detect and isolate sensor faults in real ti...

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Problems solved by technology

[0003] At present, the fault detection and isolation methods commonly used in UAV integrated navigation systems include: direct comparison method, wavelet transform method, state estimation method, neural network method and expert system method, etc., but the above methods are deficient in engineering applications. The comparison method will increase the amount of calculation and the detection effect will drop sharply with the increase of the number of devices in the redundant inertial group; the wavelet change method has a large amount of calculation and poor real-time performance, it is difficult to select the optimal wavelet base and decomposition layers, and it cannot distinguish Mutation signals caused by UAV maneuvers and sensor failures are prone to false alarms; the state estimation method needs to establish an accurate UAV dynamics model, and the statistical characteristics of noise are not practical; the neural network method is difficult to obtain sufficient Fault feature information, engineering application is difficult; expert system method is difficult to establish expert knowledge base and fault reasoning mechanism

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