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Keck Voon Ling

Publications and source records attributed to Keck Voon Ling.

5 recordsLinked to original sources

Reachability-Based Safe-Start Regions for Approach to a Tumbling Target with Rotating LOS Constraints

This paper presents a closed-form test that decides, before the maneuver begins, whether a chaser can reach and hold station at the hold point of a tumbling, uncooperative target inside a line-of-sight (LOS) corridor that turns with the target. A closed-loop controller cannot answer this question: a receding-horizon controller checks the corridor only over its prediction horizon, and near a tumbling target the corridor sweeps past faster than bounded thrust can follow, so a start that is feasible at the first step can become unrecoverable later. Today the only ways to know are to fly the controller in simulation or to compute a Hamilton--Jacobi reachable set, both too slow onboard. The test combines two criteria derived from bounded-thrust relative orbital dynamics: a directional erosion margin, the corridor margin that rotation-induced drift consumes before the thruster arrests it, and a synchronization radius, beyond which the apparent rotational velocity cannot be cancelled. Guidance pairs a three-regime tracking law with a receding-horizon quadratic program. Benchmarked against polytopic backward and forward reachable sets, Hamilton--Jacobi level sets and closed-loop Monte Carlo simulation, the test runs over two orders of magnitude faster than Hamilton--Jacobi and, over 500 closed-loop cases, predicts feasibility with 91% recall and 80% precision. The gap to Hamilton--Jacobi is structural, not a method error: reaching the hold point and co-rotating with it is a stronger requirement than arriving with arbitrary velocity, and the gap widens with tumble rate. The test therefore gives an onboard go/no-go answer where Hamilton--Jacobi reachability is too expensive.

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Low-Thrust Trajectory Optimization for Cubesat Lunar Mission: HORYU-VI

This paper presents a low-thrust trajectory optimization strategy to achieve a near-circular lunar orbit for a CubeSat injected into a lunar flyby trajectory. The 12U CubeSat HORYU-VI is equipped with four Hall-effect thrusters and designed as a secondary payload on NASA's Space Launch System under the Artemis program. Upon release, the spacecraft gains sufficient energy to escape the Earth-Moon system after a lunar flyby. The proposed trajectory is decomposed into three phases: (1) pre-flyby deceleration to avoid heliocentric escape, (2) lunar gravitational capture, and (3) orbit circularization to the science orbit. For each phase, an impulsive-burn solution is first computed as an initial guess, which is then refined through finite-burn optimization using Sequential Quadratic Programming (SQP). The dynamical model incorporates Earth-Moon-Sun-Jupiter gravitational interactions and a high-fidelity lunar gravity field. All trajectories are independently verified with NASA's General Mission Analysis Tool (GMAT). Results demonstrate that HORYU-VI achieves lunar capture within 200 days, establishes a stable science orbit at 280 days, and can spiral down to a near-circular 100 km orbit by 450 days, using a total Delta-V of 710 m/s, well within the capability of the electric propulsion system.

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PrNet: A Neural Network for Correcting Pseudoranges to Improve Positioning with Android Raw GNSS Measurements

We present a neural network for mitigating biased errors in pseudoranges to improve localization performance with data collected from mobile phones. A satellite-wise Multilayer Perceptron (MLP) is designed to regress the pseudorange bias correction from six satellite, receiver, context-related features derived from Android raw Global Navigation Satellite System (GNSS) measurements. To train the MLP, we carefully calculate the target values of pseudorange bias using location ground truth and smoothing techniques and optimize a loss function involving the estimation residuals of smartphone clock bias. The corrected pseudoranges are then used by a model-based localization engine to compute locations. The Google Smartphone Decimeter Challenge (GSDC) dataset, which contains Android smartphone data collected from both rural and urban areas, is utilized for evaluation. Both fingerprinting and cross-trace localization results demonstrate that our proposed method outperforms model-based and state-of-the-art data-driven approaches.

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Localization with Noisy Android Raw GNSS Measurements

Android raw Global Navigation Satellite System (GNSS) measurements are expected to bring smartphones power to take on demanding localization tasks that are traditionally performed by specialized GNSS receivers. The hardware constraints, however, make Android raw GNSS measurements much noisier than geodetic-quality ones. This study elucidates the principles of localization using Android raw GNSS measurements and leverages Moving Horizon Estimation (MHE), Extended Kalman Filter (EKF), and Rauch-Tung-Striebel (RTS) smoother for noise suppression. Experimental results show that the RTS smoother achieves the best positioning performance, with horizontal positioning errors significantly reduced by 76.4% and 46.5% in static and dynamic scenarios compared with the baseline weighted least squares (WLS) method. Our codes are available at https://github.com/ailocar/androidGnss.

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Moving Horizon Estimation for ARMAX process with t-Distribution Noise

In this paper, instead of the usual Gaussian noise assumption, $t$-distribution noise is assumed. A Maximum Likelihood Estimator using the most recent N measurements is proposed for the Auto-Regressive-Moving-Average with eXogenous input (ARMAX) process with this assumption. The proposed estimator is robust to outliers because the `thick tail' of the t-distribution reduces the effect of large errors in the likelihood function. Instead of solving the resulting nonlinear estimator numerically, the Influence Function is used to formulate a computationally efficient recursive solution, which reduces to the traditional Moving Horizon Estimator when the noise is Gaussian. The formula for the variance of the estimate is derived. This formula shows explicitly how the variance of the estimate is affected by the number of measurements and noise variance. The simulation results show that the proposed estimator has smaller variance and is more robust to outliers than the Moving Window Least-Squares Estimator. For the same accuracy, the proposed estimator is an order of magnitude faster than the particle filter.

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