With the development of modern industry, small UAVs have been widely used in agriculture, mapping, meteorology, and other fields. There is an increasing demand for the core attitude-solving algorithm of UAV flight control. In this paper, at first, a novel attitude solving algorithm is proposed by using quaternions to represent the attitude matrix and using Allan variance to analyze the gyroscope error and to quantify the trend of the error over time, so as to improve the traditional Mahony complementary filtering. Simulation results show that the six-axis data from the initial sensors (gyroscope and accelerometer) agree well with the measured nine-axis data with an extra magnetometer, which reduces the complexity of the system hardware. Second, based on the hardware platform, the six-axis data collected from MPU6050 are sent to FPGA for floating-point operation, transcendental function operation, and attitude solution module for processing through IIC communication, which effectively validates the attitude solution by using the proposed method. Finally, the proposed algorithm is applied to a practical scenario of a quadrotor UAV, and the test results show that the RMSE does not exceed 2° compared with the extended Kalman filter method. The proposed system simplifies the hardware but keeps the accuracy and speed of the solution, which may result in application in UAV flight control.

This article designs and implements a 3D moving target positioning and tracking system by using ultrawideband (UWB) technology. The result of the two-way ranging by the time difference of arrival (TWR-TDOA) positioning algorithm is adopted to result in a new resolution that is to resolve the hyperbolic equations. The proposed algorithm is applied to outdoor and indoor scenarios. To minimize the effect of the sensor layout, this article proposes a simplified multi-population genetic algorithm (MPGA) to obtain the optimum distribution of anchors, which can rapidly reduce the number of search iterations. To resolve the low stability of the TDOA algorithm in outdoor and indoor scenarios, the Kalman filter algorithm is utilized to improve the stability and positioning accuracy of this system and a good simulation effect is achieved. The test results show that the system’s positioning error is far less than that of using other methods. The whole system has a feature of high precision, high stability, low complexity, and low cost.

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