Input Estimation Algorithms for Reentry Vehicle Trajectory Estimation

Fast and accurate estimation of trajectory is important in tracking and intercepting reentry vehicles. Validating model is a real challenge associated with the qverall trajectory estimation problem. Input estimation technique provides a’solution to this challenge. Two input estimation algorithms were introduced based on different assumptions about the input applied to the model. This investigation presents approaches consisting of an extended Kahnan filter and two input estimation algorithms to identify the reentry vehicle trajectory in its terminal phase using data from a single radar source. Numerical simulations with data generated from two models demonstrate superior capabilities as measured by accuracy compared to the extended Kalman filter. Evaluation using real flight data provides the consistent results. The comparison between two input estimation algorithms is also presented. The trajectory estimation approaches based on two algorithms are effective in solving the reentry vehicle tracking problem

Dominant channels of exciton spin relaxation in photoexcited self-assembled (In,Ga)As quantum dots

We present a comprehensive theoretical investigation of spin relaxation processes of excitons in photoexcited self-assembled quantum dots. The exciton spin relaxations are considered between dark- and bright-exciton states via the channels created by various spin-admixture mechanisms, including electron Rashba and Dresselhaus spin- orbital couplings (SOCs), hole linear and hole cubic SOCs, and electron hyperfine interactions, incorporated with single- and double-phonon processes. The hole-Dresselhaus SOC is identified as the dominant spin-admixture mechanism, leading to relaxation rates as fast as ∼10−2 ns−1, consistent with recent observations. Moreover, due to significant electron-hole exchange interactions, single-phonon processes are unusually dominant over two-phonon ones in a photoexcited dot even at temperatures as high as 15 K