Abstract
Sensor alignment plays a key role in the accurate estimation of the ballistic trajectory. A sparse regularization-based sensor alignment method coupled with the selection of a regularization parameter is proposed in this paper. The sparse regularization model is established by combining the traditional model of trajectory estimation with the sparse constraint of systematic errors. The trajectory and the systematic errors are estimated simultaneously by using the Newton algorithm. The regularization parameter in the model is crucial to the accuracy of trajectory estimation. Stein’s unbiased risk estimator (SURE) and general cross-validation (GCV) under the nonlinear measurement model are constructed for determining the suitable regularization parameter. The computation methods of SURE and GCV are also investigated. Simulation results show that both SURE and GCV can provide regularization parameter choices of high quality for minimizing the errors of trajectory estimation, and that our method can improve the accuracy of trajectory estimation over the traditional non-regularization method. The estimates of systematic errors are close to the true value.
Highlights
In the field of aerospace, an accurate trajectory is required for testing the performance of a new type of ballistic vehicle
s unbiased risk estimator (SURE) and general cross-validation (GCV) can provide regularization parameter choices of high quality for minimizing the errors of trajectory estimation, and that our method can improve the accuracy of trajectory estimation over the traditional non-regularization method
We propose a sparse regularization based sensor alignment method involving the selection of a regularization parameter for ballistic trajectory estimation
Summary
In the field of aerospace, an accurate trajectory is required for testing the performance of a new type of ballistic vehicle. According to the historic data, only a few measurements contain systematic errors Considering this property, Liu proposed a sensor alignment method based on the sparse regularization [11]. He modified the RPM method by adding a sparse constraint term on the systematic errors into the objective function. The SURE method chooses the regularization parameter by minimizing the SURE which is constructed as an unbiased estimator of predictive mean square error (MSE). We propose a sparse regularization based sensor alignment method involving the selection of a regularization parameter for ballistic trajectory estimation. SURE and GCV methods are used to choose an appropriate regularization parameter for optimizing the performance of trajectory estimation. The performance of our method is evaluated through a simulation experiment
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