Jin Woo Song

In this paper, we propose a novel method to reduce non-Gaussian errors in measurements using sampling-based distribution estimation. Although non-Gaussian errors are often treated as statistical deviations, they can frequently arise in practical unmanned aerial systems that depend on global navigation satellite systems (GNSS), where position measurements are degraded by multipath effects. However, nonlinear or robust filters have shown limited effectiveness in correcting such errors, particularly when they appear as persistent biases in measurements over time. In such cases, adaptive techniques have often demonstrated greater effectiveness. The proposed method estimates the distribution of observed measurements using a sampling-based approach and derives a reformed measurement from this distribution. By incorporating this reformed measurement into the filter update, the proposed approach achieves lower error levels than traditional adaptive filters. To validate the effectiveness of the method, Kalman filter simulations are conducted for drone GNSS/INS navigation. The results show that the proposed method outperforms conventional non-Gaussian filters in handling measurement bias caused by non-Gaussian errors. Furthermore, it achieves nearly twice the estimation accuracy compared to adaptive approaches. These findings confirm the robustness of the proposed technique in scenarios where measurement accuracy temporarily deteriorates before recovering.

This paper presents a fault tolerant navigation algorithm for vertical takeoff and landing advanced aerial mobility platforms. The proposed algorithm incorporates constraints based on the distances between sensors, using the innovation calculated from filter estimation and measurement as a weighting factor. With the proposed sensor configuration, the platform's attitude is computed and then used for measurement updates, while an adaptive rule is employed to enhance filter robustness. A comprehensive analysis was conducted using Monte Carlo simulations to confirm the algorithm's robustness, and experimental test verified its feasibility in real-world conditions. The results demonstrated that the proposed algorithm maintained robust performance even in various sensor fault scenarios and achieved significant improvements in attitude estimation accuracy compared to traditional navigation systems. Moreover, the successful experimental validation shows that the algorithm can be effectively implemented using a single inertial measurement unit together with multi-positioning sensors, such as real-time kinematics and ultra- wideband, thereby enhancing reliability and fault tolerance in aerial navigation applications.

In recent years, the error-state Kalman filter (ErKF) has been widely employed in various applications, including robotics, aerospace, and localization. However, incorporating state constraints into the ErKF framework using the estimate projection method remains ambiguous. This paper examines this issue in depth, specifically exploring whether constraints should be enforced before or after the ErKF correction step. We adopt a mathematical approach, deriving analytical solutions and analyzing their statistical properties. Our findings prove that, for a linear system with linear constraints, both methods yield statistically equivalent results. However, the filter’s behavior becomes uncertain when dealing with linearized constraints. We further identify a special case of a nonlinear constraint where the results of the linear case remain valid. To support our theoretical analysis and evaluate the filter’s performance under non-ideal conditions, we conduct two Monte Carlo simulations considering increasing initialization errors and constraint incompleteness. The simulation results validate our theoretical insights and suggest that applying constraints to the error state after the correction step may lead to superior performance compared to the alternative approach.

In this paper, we propose a multi-position cluster-based weighted position estimation method that minimizes the influence of multipath (MP)/non-line-of-sight (NLOS) signals using a global navigation satellite system (GNSS) receiver. The proposed method is suitable for positioning passenger cars, particularly in urban driving environments. Density-based spatial clustering of applications with noise (DBSCAN)-based clustering is performed by generating multi-position data through a subset of observable satellites and analyzing the density characteristics of position data generated by line-of-sight (LOS) satellite signals from the generated multi-position data. To estimate the optimal position through clustered data, we propose a method by constructing a weighted model through Doppler-based velocity measurements, which is robust to MP delay signals compared to code-based pseudorange measurements. In addition, to prevent unnecessary clustering points from having weights, the predicted range is selected based on the nonholonomic constraint of the vehicle. The proposed algorithm was quantitatively validated by selecting a region in an actual urban environment where the MP/NLOS error could occur significantly. It was confirmed that the accuracy of vehicle position was improved by approximately 24% by the proposed method compared to existing positioning solutions.

In recent years, the error-state Kalman filter (ErKF) has been extensively employed across various applications, including but not limited to robotics, aerospace, and localization. However, incorporating constraints into the ErKF framework when state constraint is necessary has remained a challenging task due to its intrinsic properties. This paper explores all possible ways to achieve this goal in the context of the estimate projection method. In particular, the constraint can be enforced before or after the ErKF's correction step. We approach the problem from a mathematical perspective by deriving analytical solutions and discussing their statistical properties. We prove that the two mentioned methods are statistically identical for a linear system with linear constraints. Conversely, the filter's behavior remains uncertain in the presence of linearized constraints. However, we provide a special case of the nonlinear constraint, wherein the results of the linear case remain valid. To support our theorem and verify the filter's performance when the assumptions are invalidated, we present two Monte Carlo simulations under the increasing initialization error and the constraint's incompleteness. The simulation results clearly confirm our insights and lead to the conclusion that constraining the error-state after the correction may offer superior outcomes compared to its competitor.

This article proposes a computationally lightweight marginalized particle filtering (MPF) algorithm with improved filter consistency. For mixed linear/nonlinear state-space models such as terrain referenced navigation (TRN) models, an MPF can improve the convergence and efficiency of the filters by separately applying a Kalman filter (KF) and a particle filter (PF) to each state space rather than using only one type of filter. However, an MPF still has a high degree of complexity because it requires the same number of KFs as the number of particles in the PF. To address this issue, our method simplifies the MPF through Gaussian approximation to use only one KF and improves the filter consistency compared to other MPF simplification methods. The proposed method further reduces the complexity by considering the dynamic model of a TRN system. This article presents a complexity analysis between the original MPF and other MPF simplification methods and numerical experiments for a range/bearing example and a TRN system. The results show that the proposed algorithm has better filter consistency than the existing simplification algorithms and can achieve performance comparable to an MPF with less computation.

The low-altitude operation of rotorcraft is inevitably affected by the ground effect that poses challenging issues in designing altitude controller. This letter is concerned to the altitude tracking problem for rotorcraft unmanned aerial vehicles (UAVs) under the presence of the ground effect. In the light of existing in-ground-effect (IGE) models, we present adaptive altitude controllers to deal with unknown parameters in the IGE models. The controllers guarantee robust stability of the rotorcraft UAV in vertical movements, and improve the altitude track performances in close proximity to ground. Compared to the existing study, the improvement of the proposed controllers are then illustrated by several flight experiments.

Redundant inertial sensor systems improve navigation performance and guarantee fault detection or fault isolation functions. However, they may cause unexpected side effects owing to their enlarged size. The misalignment between the center of gravity of the sensor system and the rotational origin of the vehicle results in the lever arm effect inducing significant acceleration errors. To minimize the lever arm effect error, the configuration of redundant sensors should be carefully considered, as their performance is highly dependent on its configuration. Thus, the figure-of-merit (FOM), which is considered a performance index or cost function, is proposed to obtain the optimal configuration for minimizing the lever arm effect error. Further, to define an FOM, the angular velocity and angular acceleration are considered as random variables and to quantify them, they were replaced as variances. The lever arm effect error was found to be completely eliminated when a redundant inertial measurement unit (RIMU) with more than eight single-axis inertial sensors was configured using the proposed FOM. Further, a case study and simulation were conducted to verify the proposed FOM and configuration, and it was found that the proposed FOM can be used to obtain the optimal configuration of the RIMU and as an indicator of the lever arm effect error.

The prior covariance estimation method based on inverse covariance intersection (ICI) is proposed to apply the particle flow filter. The proposed method has better estimate performance and guarantees consistent estimation results compared with previous works. ICI is the recently developed method of ellipsoidal intersection and is used to get the combined estimate of prior covariance. This method integrates the sample covariance estimate, which is unbiased but usually with high variance, together with a more structured but typically a biased target covariance through fusion gains. For verifying the performance of the proposed algorithm, analysis and simulations are performed. Through the simulations, the results are given to illustrate the consistency and accuracy of the proposed algorithm s estimation and target tracking performance.