Abstract
In view of the practical engineering problem of dynamic positioning of rotor UAV pod and the strong nonlinear problem in dynamic operation of UAV, a real-time estimation of the dynamic position of the rotor UAV pod is proposed by using the SR-UKF algorithm. This algorithm uses the nonlinear propagation of UT transform to generate the point set to maintain the mean and covariance information and thus achieves higher precision. Moreover, it uses the square root of covariance instead of covariance to participate in the recursive operation, thus improving the numerical stability of the filter and reducing the amount of computation. In this work, a pseudosatellite positioning platform was constructed in a field site in Nanjing. Based on evaluation of the space geometry of pseudosatellite base station, the accuracy of several nonlinear filtering algorithms was analyzed and evaluated using GPS RTK positioning results. It was found that the SR-UKF algorithm was the most accurate and efficient algorithm. It can meet the requirements of dynamic positioning of the rotor-wing UAV pod. The experiment results of this algorithm provide a more efficient positioning algorithm and implementation means for the actual engineering of UAV pod positioning using pseudosatellite system, which has high application value.
Highlights
In recent years, more and more scientific research and engineering projects have focused on carriers for the multirotor UAV technology [1,2,3,4]
In order to address the dynamic location problem of the rotor UAV pod, a pseudosatellite positioning system was constructed in the field test site by acquiring the distance observation between the pseudosatellite base station and the
Dynamic positioning was studied by using a variety of nonlinear filtering algorithms, including SR-UKF
Summary
More and more scientific research and engineering projects have focused on carriers for the multirotor UAV technology [1,2,3,4]. The most representative example is PF (particle filter) with Monte Carlo sampling method instead of the UT-based sampling method, which has better resistance and adaptive ability to deal with strong nonlinear/nongaussian problems While this algorithm is still in rapid development, there are some problems such as filtering divergence from random sampling [26], large amount of particles, [27], particle degradation [28] and sample exhaustion [29], and so on. At the same time, according to the requirement of nonlinearity and higher calculation efficiency of UAV’s motion process, the traditional UKF algorithm is improved so as to obtain the positioning result with higher precision and better stability in practical engineering. This final section analyzes the shortcomings of the theory and technology and the problems that require further improvement
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