UWB Localization Based on Improved Robust Adaptive Cubature Kalman Filter.

Aiming at the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate kinematic model in ultra-wideband (UWB) systems, this paper proposed an improved robust adaptive cubature Kalman filter (IRACKF). Robust and adaptive filtering can weaken the influence of observed outliers and kinematic model errors on filtering, respectively. However, their application conditions are different, and improper use may reduce positioning accuracy. Therefore, this paper designed a sliding window recognition scheme based on polynomial fitting, which can process the observation data in real-time to identify error types. Simulation and experimental results indicate that compared to the robust CKF, adaptive CKF, and robust adaptive CKF, the IRACKF algorithm reduces the position error by 38.0%, 45.1%, and 25.3%, respectively. The proposed IRACKF algorithm significantly improves the positioning accuracy and stability of the UWB system.

Application of a long short-term memory neural network algorithm fused with Kalman filter in UWB indoor positioning.

Ultra-wideband technology has good anti-interference capabilities and development prospects in indoor positioning. Since ultra-wideband will be affected by random errors in indoor positioning, to exploit the advantages of the Kalman filter (KF) and the long short-term memory (LSTM) network, this paper proposes a long short-term memory neural network algorithm fused with the Kalman filter (KF-LSTM) to improve UWB positioning. First, the ultra-wideband data is processed through KF to weaken the noise in the data, and then the data is fed into the LSTM network for training, and the capability of the LSTM network to process time series features is employed to obtain more accurate label positions. Finally, simulation and measurement results show that the KF-LSTM algorithm achieves 71.31%, 37.28%, and 49.31% higher average positioning accuracy than the back propagation (BP) network, (back propagation network fused with the Kalman filter (KF-BP), and LSTM network algorithms, respectively, and the KF-LSTM algorithm performs more stably. Meanwhile, the more noise the data contains, the more obvious the stability contrast between the four algorithms.

A hierarchical combination algorithm for real-time cycle slip detection and repair in low satellite elevation and high ionospheric activity conditions.

To enhance the accuracy and robustness of cycle slip detection and repair for triple-frequency data while minimizing the adverse effects of low satellite elevation and high ionospheric activity, a hierarchical combination algorithm for real-time cycle slip detection and repair is proposed. This algorithm begins by prioritizing the reduction of noise and ionospheric delay coefficients. It determines the optimal coefficients for the combination of observations from the BeiDou Navigation Satellite System's (BDS) Extra-Wide Lane (EWL), Wide Lane (WL), and Narrow Lane (NL). Leveraging the longer wavelength characteristics of the EWL combination, it simultaneously conducts cycle slip detection on the EWL combination alongside the pseudorange combination. Following this, based on the detection outcomes from the EWL combination, cycle slip detection is carried out on the WL combination. Finally, using the detection findings from the WL combination, cycle slip detection is executed on the NL combination. Given the NL combination's shorter wavelength and higher susceptibility to ionospheric delay, a dynamic ionospheric prediction model is applied to the NL combination to further mitigate the impact of ionospheric disturbances. After completing the cycle slip detection process, the results from the EWL, WL, and NL combinations are integrated and solved. Experimental results clearly demonstrate that, even in scenarios characterized by low satellite elevation and active ionospheric conditions, this algorithm consistently delivers outstanding detection performance for cycle slip instances, particularly for small cycle slip (less then two cycles). Remarkably, this performance is achieved without the need for intricate searches during cycle slip repair.

Improved GNSS integer ambiguity resolution method based on the column oriented Cholesky decomposition.

Because the traditional Cholesky decomposition algorithm still has some problems such as computational complexity and scattered structure among matrices when solving the GNSS ambiguity,  it is the key problem to further improve the computational efficiency of the least squares ambiguity reduction correlation process in the carrier phase integer ambiguity solution. But the traditional matrix decomposition calculation is more complex and time-consuming, to improve the efficiency of the matrix decomposition, in this paper, the decomposition process of traditional matrix elements is divided into two steps: multiplication update and column reduction of square root calculation. The column reduction step is used to perform square root calculation and column division calculation, while the update step is used for the update task of multiplication. Based on the above ideas, the existing Cholesky decomposition algorithm is improved, and a column oriented Cholesky (C-Cholesky) algorithm is proposed to further improve the efficiency of matrix decomposition, so as to shorten the calculation time of integer ambiguity reduction correlation. The results show that this method is effective and superior, and can improve the data processing efficiency by about 12.34% on average without changing the integer ambiguity accuracy of the traditional Cholesky algorithm.

Multi-orbit lunar GNSS constellation design with distant retrograde orbit and Halo orbit combination.

The Moon is the closest natural satellite to mankind, with valuable resources on it, and is an important base station for mankind to enter deep space. How to establish a reasonable lunar Global Navigation Satellite System (GNSS) to provide real-time positioning, navigation, and timing (PNT) services for Moon exploration and development has become a hot topic for many international scholars. Based on the special spatial configuration characteristics of Libration point orbits (LPOs), the coverage capability of Halo orbits and Distant Retrograde Orbit (DRO) in LPOs is discussed and analyzed in detail. It is concluded that the Halo orbit with a period of 8 days has a better coverage effect on the lunar polar regions and the DRO has a more stable coverage effect on the lunar equatorial regions, and the multi-orbital lunar GNSS constellation with the optimized combination of DRO and Halo orbits is proposed by combining the advantages of both. This multi-orbital constellation can make up for the fact that a single type of orbit requires a larger number of satellites to fully cover the Moon, using a smaller number of satellites for the purpose of providing PNT services to the entire lunar surface. We designed simulation experiments to test whether the multi-orbital constellations meet the full lunar surface positioning requirements, and compare the coverage, positioning, and occultation effects of the four constellation designs that pass the test, and finally obtain a set of well-performing lunar GNSS constellations. The results indicate that the multi-orbital lunar GNSS constellation combining DRO and Halo orbits can cover 100% of the Moon surface, provides there are more than 4 visible satellites at any time on the Moon surface, which meets the navigation and positioning requirements, and the Position Dilution of Precision (PDOP) value is stable within 2.0, which can meet the demand for higher precision Moon surface navigation and positioning.

Leucine-rich repeat kinase 2 (LRRK2) inhibition upregulates microtubule-associated protein 1B to ameliorate lysosomal dysfunction and parkinsonism.

Mutations in LRRK2 (encoding leucine-rich repeat kinase 2 protein, LRRK2) are the most common genetic risk factors for Parkinson's disease (PD), and increased LRRK2 kinase activity was observed in sporadic PD. Therefore, inhibition of LRRK2 has been tested as a disease-modifying therapeutic strategy using the LRRK2 mutant mice and sporadic PD. Here, we report a newly designed molecule, FL090, as a LRRK2 kinase inhibitor, verified in cell culture and animal models of PD. Using the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine mice and SNCA A53T transgenic mice, FL090 ameliorated motor dysfunctions, reduced LRRK2 kinase activity, and rescued loss in the dopaminergic neurons in the substantia nigra. Notably, by RNA-Seq analysis, we identified microtubule-associated protein 1 (MAP1B) as a crucial mediator of FL090's neuroprotective effects and found that MAP1B and LRRK2 co-localize. Overexpression of MAP1B rescued 1-methyl-4-phenylpyridinium induced cytotoxicity through rescuing the lysosomal function, and the protective effect of FL090 was lost in MAP1B knockout cells. Further studies may be focused on the in vivo mechanisms of MAP1B and microtubule function in PD. Collectively, these findings highlight the potential of FL090 as a therapeutic agent for sporadic PD and familial PD without LRRK2 mutations.