A novel passing priority allocation method, based on the collision avoidance urgency, used for a formation of UAVs in a serried obstacle laden environment, is presented in this paper. The method employs the collision cone and the prediction of the collision time to evaluate the urgency of imminent collision of each UAV, so as to establish a primary criterion for the priority allocation. And, the priority update policy is also innovative designed to alleviate burdens on the instability of frequent shifty context. This paper exploits the potential field in a cooperative way to plan the path for each UAV, steering the UAVs to a given three-dimensional position, which combines the adaptive priorities to guide the UAVs passing through the barriers in turn. Numerical simulations and semi-physical experiments in designed outdoor condition are carried out to compare the performance of the proposed adaptive priorities with the regular-used default priorities. The total passing time and the energy cost of the semi-physical tests provide the promising validation in terms of the efficiency and superiority of the proposed method.

The projectile-borne data recorder is used to measure and record the position and attitude data of exterior ballistic flights, which experience high overload during the launching process. The navigation algorithm can be optimized by analyzing the data stored in the recorder. As the primary means of acquiring the navigation data, the micro inertial measurement unit (MIMU) is an inevitable part of the projectile equipment. However, its mechanical structure could hardly bear the high overload during operation. In view of the above problems, a novel projectile-borne data recorder triggered by overload is designed in this paper. The recorder could have the navigation system powered when the projectile leaves the barrel so as to activate the data recording of the high overload. Furthermore, the viability of the MIMU of the high overload is guaranteed through a specific way of system encapsulation. In the proposed design, the overload switch redundancy and the power supply redundancy were adopted to improve reliability. The proposed design is tested with practical experiments, and the results show that the proposed recorder could be effectively triggered by overloading, and the supply voltage is stable, which helps record reliable data for the projectile.

The wind as a natural phenomenon would cause the derivation of the pesticide drops during the operation of agricultural unmanned aerial vehicles (UAV). In particular, the changeable wind makes it difficult for the precision agriculture. For accurate spraying of pesticide, it is necessary to estimate the real-time wind parameters to provide the correction reference for the UAV path. Most estimation algorithms are model based, and as such, serious errors can arise when the models fail to properly fit the physical wind motions. To address this problem, a robust estimation model is proposed in this paper. Considering the diversity of the wind, three elemental time-related Markov models with carefully designed parameter α are adopted in the interacting multiple model (IMM) algorithm, to accomplish the estimation of the wind parameters. Furthermore, the estimation accuracy is dependent as well on the filtering technique. In that regard, the sparse grid quadrature Kalman filter (SGQKF) is employed to comprise the computation load and high filtering accuracy. Finally, the proposed algorithm is ran using simulation tests which results demonstrate its effectiveness and superiority in tracking the wind change.

In recent years, the AHRS has become an important research area. In order to improve the orientation measurement accuracy of AHRS, this paper first expounds the algorithm principle of the AHRS. Then, aiming at the problem of poor stability of the traditional gradient descent method under high dynamics or large magnetic field interference, a gain-scheduled anti-interference AHRS orientation algorithm is designed. By using the switching architecture designed with this algorithm for gain scheduling, it can ensure stable performance while being subjected to apparent acceleration and ferromagnetic interference. Finally, experimental verification is conducted, and the experimental results show that: in the presence of interference, the roll angle error is 0.0350°, the pitch angle error is 0.0277°, and the yaw angle error is 0.9611°, thereby verifying the strong anti-interference performance of the gain-scheduled AHRS orientation algorithm proposed in this paper.

In order to make full use of the complementarity of existing navigation systems to improve the navigation and positioning accuracy, this paper investigates the visual/INS integrated navigation system based on the factor graph optimization (FGO) algorithm. In the traditional visual/INS integrated navigation system, there is often the problem of image blurring due to the rapid change of light and shadow captured by the vision sensor and the attitude change of the carrier, which leads to the decrease of the positioning accuracy of the combined navigation system. Therefore, this paper firstly introduces Retinex algorithm and BID algorithm to carry out the design of image de-blurring method; then implements the visual/INS integrated navigation algorithm based on the factor graph optimization method; finally, the experimental scheme is designed, comparative experiments are carried out, and the researched method is verified by using the public dataset. The experimental results show that the combined navigation algorithm realized in this paper has high accuracy, and its positioning accuracy is improved by 40% compared with visual positioning.

As global navigation satellite systems (GNSS) become ubiquitous across diverse applications, narrowband suppression interference in complex RF environments poses a serious threat to receiver signal acquisition and tracking. To address the poor adaptive mitigation capabilities of conventional GNSS receivers under interference— often leading to positioning outages—this paper proposes a front-end, frequency-domain interference-detection-based adaptive notch-filter anti-jamming system. By combining a simplified Welch power-spectral-density estimator with a Top-K thresholding strategy, the system rapidly extracts interference center frequencies and bandwidths, then dynamically constructs cascaded second-order notch filters to suppress multiple interference tones simultaneously. Implemented on a Zynq SoC, the sampling-processing-output interference-suppression chain integrates seamlessly with existing GNSS front-end architectures. Experimental results demonstrate high-accuracy parameter extraction and real-time suppression: under both continuous-wave and swept-frequency interference, the system markedly improves navigation signal quality and overall anti-jamming performance.