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In this paper, a new approach to gain-scheduling of robust autopilot was proposed for morphing guided aerial bombs in a bank-to-turn (BTT) mode. The nonlinear dynamic model was established and anticipant performance criterions were given firstly. Feedback robust BTT autopilot of 3-channel was designed independently for selected operating points in order to restrain cross-channel coupling disturbances and aerodynamic perturbations. Then, the sweepback and mach number were selected to schedule the control gains iteratively based on guardian maps for ensuring all closed-loop poles to locate inside the desired region. The method proposed here attempted to extend the performance of an initial design obtained for a single arbitrary point to the whole linearized domain while maintaining expected stability over the entire range of sweepback. We carried out a number of time- and frequency-domain analysis procedures on the resulting designs and assessed the performance on a nonlinear simulation. The numerical simulations showed that the method proposed applied on the autopilot design and had better tracking performance.

The aim of this paper is to explore the possibility of using geo-referenced aerial images to augment an Unmanned Aerial Vehicle (UAV) navigation system in case of GPS failure. The proposed method is based on image matching between the current view from a monocular video camera and a previously known database of geo-referenced images. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or artificial landmarks. The novel idea is to perform global localization, position tracking, localization failure recovery and safe landing. Key points of current video frame and reference database are extracted by implementing Speeded-Up Robust Features (SURF) algorithm on a NVIDIA Geforce 240M graphics processing unit (GPU). The proposed system has been tested on real flight data and testing results confirmed that the proposed system is real-time and sufficiently robust to scale, rotation, illumination, noise and affine projection, thus it can be an ideal scheme for autonomous navigation system for UAVs under GPS blockage conditions.

In order to solve the problem that the precision and stability of Doppler radar/Fiber Optical Gyroscope Strapdown Inertial Naivgation System (FOG-SINS)/Barometer Integrated Navigation System (DFBINS) for helicopters will be highly affected if there are outliers in doppler radar, especially consecutive outliers appear, a useful method to eliminate these outliers based on orthogonality of innovation is proposed in this paper. Outliers in Doppler radar can be detected by judging whether the orthogonality of innovation in Kalman filter is lost or not, and an activation function as the weight factor to each element of observation is assigned, so it can keep innovation sequence of kalman filter orthogonal and outliers can be detected and corrected. The excellent results of digital simulation for a long distance straight line flying test show that the modified method is effectively resistant to the adverse effects on accuracy and stability of DFBINS caused by outliers in Doppler radar, the proposed algorithm is of high value in practice.

In order to meet the requirements of high precision orbit determination and prediction for the safe and pinpoint lunar soft landing, the paper presents an autonomous navigation based on SINS (strapdown inertial navigation system) /DOPPLER Radar/Radar Altimeter integrated navigation system. According to the error characteristics of each sensor, an effective information fusion algorithm is designed. The excellent result of vehicle experiment indicates that the integrated navigation has high precision, the system design method is successful, and it has better value of engineering application.

Though fiber optic gyroscope strapdown inertial navigation system (FOG-SINS)/Doppler radar/ Baro-altimeter integrated navigation system has been done some research in theory, the appropriate study of practice has not been done yet in China and there are still many problems need to be solved. This paper gives a new design of a low cost FOG-SINS/Doppler radar /Baro-altimeter integrated navigation system according to the disadvantages of precision and independence of navigation for helicopters. The mathematical model and digital simulation were presented and a series of long endurance hardware-in-the-loop simulation tests were conducted in a land vehicle. The excellent result of the simulation tests indicates that the designed system is effective, it can satisfy the requirement of navigation for helicopters when the GPS is disabled.

Recent years have seen a booming of safety-related Intelligent Transportation System (ITS) applications, which have placed increasingly stringent requirements on the performance of Global Navigation Satellite Systems (GNSS). Examples include lane control, collision avoidance, and intelligent speed assistance. Detecting the lane level anomalous driving behavior is crucial for these safety critical ITS applications. The two major issues associated with the lane-level irregular driving identification are (1) accessibility to high accuracy positioning and vehicle dynamic parameters, and (2) extraction of anomalous driving behavior from these parameters. This paper introduces an integrated algorithm for detecting lane-level anomalous driving. Lane-level high accuracy vehicle positioning is achieved by fusing GPS and Beidou feeds with Inertial Measurement Unit (IMU) using Unscented Particle Filter (UPF). Anomalous driving detection is achieved based on the application of a newly designed Fuzzy Inference System. Computer simulation and real-world field test demonstrate the advantage of the proposed approach over existing ones from previous studies.