RESUMO
The quaternion cubature Kalman filter (QCKF) algorithm has emerged as a prominent nonlinear filter algorithm and has found extensive applications in the field of GNSS/SINS integrated attitude determination and positioning system (GNSS/SINS-IADPS) data processing for unmanned aerial vehicles (UAV). However, on one hand, the QCKF algorithm is predicated on the assumption that the random model of filter algorithm, which follows a white Gaussian noise distribution. The noise in actual GNSS/SINS-IADPS is not the white Gaussian noise but rather a ubiquitous non-Gaussian noise. On the other hand, the use of quaternions as state variables is bound by normalization constraints. When applied directly in nonlinear non-Gaussian system without considering normalization constraints, the QCKF algorithm may result in a mismatch phenomenon in the filtering random model, potentially resulting in a decline in estimation accuracy. To address this issue, we propose a novel Gaussian sum quaternion constrained cubature Kalman filter (GSQCCKF) algorithm. This algorithm refines the random model of the QCKF by approximating non-Gaussian noise with a Gaussian mixture model. Meanwhile, to account for quaternion normalization in attitude determination, a two-step projection method is employed to constrain the quaternion, which consequently enhances the filtering estimation accuracy. Simulation and experimental analyses demonstrate that the proposed GSQCCKF algorithm significantly improves accuracy and adaptability in GNSS/SINS-IADPS data processing under non-Gaussian noise conditions for Unmanned Aerial Vehicles (UAVs).
RESUMO
Objectives: The willingness of family members to take care of older relatives directly affects the quality of life of disabled older adults, so it is necessary to understand the status quo of willingness to care and its influencing factors. This has been extensively studied in other countries, but, it is rarely studied in China. Based on the theory of altruism, employing a unique sample from Shanghai, China in 2017 and 2022, we attempt to reveal the influencing factors of the care willingness of family caregivers during the transition period. Methods: To measure caregiver burden and functional disability of the care recipient, we employ the Zarit Burden Interview (ZBI) and the Barthel Index, respectively. Then we utilized the ordinary least squares (OLS) methodology and estimated four regression models. Models 1, 2, and 3 examined the impact of the variables of the caregiver burden, responsibility and love, and the quality of the caregiver-caregiver recipient relationship, respectively, on family caregivers' willingness to care. Model 4 was the full model. To testify whether the caregiver burden is likely to act as a mediator, path analysis was used, and the path was adjusted and verified. Results: According to the survey, in Shanghai, only half of the caregivers had a very high care willingness to care for disabled older relatives, while nearly one-tenth of the caregivers had a low willingness. It was the caregiver burden rather than the functional disability of older adults that harms family caregivers' willingness to care. Responsibility and caring out of love were positively related to care willingness. Relationship quality was the most important influencing factor, explaining 10.2% of the variance in care willingness. Path analysis demonstrated that responsibility, caring out of love, and relationship quality directly and through the mediation of caregiver burden indirectly affected care willingness. Conclusion: Our results revealed that reciprocal altruism presented by the quality of the caregiver-care recipient relationship had a significantly positive impact on family caregivers' willingness to care. In addition, the caregiver burden was found not only directly affected care willingness, but also acted as a mediator. To promote the perfection of laws and policies, comprehensive samples of different types of cities should be included and the measurement of key variables could be further improved in future studies.