Bat Planner: Aggressive Flying Ball Player

In this study, an extended Kalman filter (EKF) for estimation of spacecraft's position and velocity by single station antenna tracking data in the presence of singularities is proposed. The designed EKF estimates the position and velocity of the spacecraft on direct nonlinear range-azimuth-elevation measurements. The proposed EKF is examined through the geostationary satellite orbital motion simulations. The corruption in the results by EKF emerges from singularities caused by the highly complex elements of the Jacobian measurement matrix. The recommendations to prevent the mentioned problem are given.

Kalman filtering represents formidable computational linear algebra requirements for each new input measurement vector. The air-to-air missile guidance problem is addressed for which an extended Kalman filter (EKF) is required because the measurements are nonlinear in Cartesian coordinates. An explicit formulation is used. At the outset, we discretize the system dynamics and measurement model and incorporate a discrete-time EKF. A factorized L D LT algorithm is used to propagate the covariance matrices between sample times. A simulation analysis of the number of data bits required in the computations is provided. Comparison with other EKF algorithms shows that this method requires only 18 bit accuracy (compared to 32-40 bits for other methods). Quantitative position, velocity and acceleration estimates obtained for a highly maneuverable target are presented. A high-accuracy floating point optical processor is presented that is capable of computing the full EKF to allow a new measurement update each msec.

A Hybrid Projectile (HP) is a tube launched munition that transforms into a gliding UAV, and is currently being researched at West Virginia University. A simple launch timer was first envisioned to control the transformation point in order to achieve maximum distance. However, this timer would need to be reprogrammed for any distance less than maximum range due to the nominal time to deployment varying with launch angle. A method was sought for automatic wing deployment that would not require reprogramming the round. A body angle estimation system was used to estimate the pitch of the HP relative to the Earth to determine when the HP is properly oriented for the designed glide slope angle. It was also necessary to filter out noise from a simulated inertial measurement unit (IMU), GPS receiver, and magnetometer. An Extended Kalman Filter (EKF) was chosen to estimate the Euler angles, position and velocity of the HP while an algorithm determined when to deploy the wings. A parametric study was done to verify the optimum deployment condition using a Simulink aerodynamic model. Because range is directly related to launch angle, various launch angles were simulated in the model. By fixing the glide slope angle to -10° as a deployment condition for all launch angles, the range differed only by a maximum of 6.1% from the maximum possible range. Based on these findings, the body angle deployment condition provides the most flexible option to maintain maximum distance without the need of reprogramming. Position and velocity estimates were also determined from the EKF using the GPS measurements. Simulations showed that the EKF estimates exhibited low root mean squared error values, corresponding to less than 3% of the total position values. Because the HP was in flight for less than a minute in this experiment, the drift encountered was acceptable.

Vision-based ego-velocity estimation is a fundamental problem in robot state estimation. However, the constraints of frame-based cameras, including motion blur and insufficient frame rates in dynamic settings, readily lead to the failure of conventional velocity estimation techniques. Mammals exhibit a remarkable ability to accurately estimate their ego-velocity during aggressive movement. Hence, integrating this capability into robots shows great promise for addressing these challenges. In this letter, we propose a brain-inspired framework for linear-angular velocity estimation, dubbed NeuroVE. The NeuroVE framework employs an event camera to capture the motion information and implements spiking neural networks (SNNs) to simulate the brain's spatial cells' function for velocity estimation. We formulate the velocity estimation as a time-series forecasting problem. To this end, we design an Astrocyte Leaky Integrate-and-Fire (ALIF) neuron model to encode continuous values. Additionally, we have developed an Astrocyte Spiking Long Short-term Memory (ASLSTM) structure, which significantly improves the time-series forecasting capabilities, enabling an accurate estimate of ego-velocity. Results from both simulation and real-world experiments indicate that NeuroVE has achieved an approximate 60% increase in accuracy compared to other SNN-based approaches.

The use of accelerometer and gyro observations in a visual SLAM implementation is beneficial especially in high dynamic situations. The downside of using inertial is that traditionally high prediction rates are required as observations are provided at high sample rates. An accurate orientation and velocity estimate must also be maintained at all times in order to integrate the inertial observations and correct for the effect of gravity. This paper presents a way to pre-integrate the high rate inertial observations without the need for an initial orientation or velocity estimate. This allows for a slower filter prediction rate and use of inertial observations when the initial velocity and attitude of the platform are unknown. Additionally the initial velocity and roll and pitch of the platform become observable over time and an estimate of these values is provided by the filter. An estimate of the gravity vector is also provided. Results are presented using a delayed state information smoother implementation however due to the linearity of the equations this technique can be applied to extended Kalman filter (EKF) implementations just as easily.

State estimation of river system includes the flow and stage estimation and used in the estimation of the final sensor's velocity which is proportional to the river velocity. The estimation process includes the system model and measurements. The measurements can be carried out using Eulerian sensor or Lagrangian sensor. The Lagrangian sensor is considered in this research since it able to observe the overall system compared to the Eulerian sensor that perform measurement in fixed position. Besides that, the estimation can be carried out using DA method such as Extended Kalman Filter (EKF) and Particle Filter (PF). The Smooth Variable Structure Filter (SVSF) is introduced to give better response in terms of error reduction by having convergence rate and the smoothing boundary layer width. The convergence analysis of SVSF shows the effectiveness of the proposed method in terms of mathematical representation. The simulation results proved the SVSF have better response compared to the forward simulation, EKF and PF. This shown by 25.6% for RMSE, 25.92% for SD, 15.34% for MAE and the computational time is reduced by 95.51 % if compared with PF.

The first ground test results of the UCLA formation flight control computer (FFCC) are presented. The FFCC are designed to calculate real time, relative states of multiple aircraft accurately enough to fly two F-18 A/C autonomously in formation flight for a drag reduction experiment. This paper compares three schemes of differing complexity for the estimation of relative position, velocity, and attitude of two FFCC. Each scheme processes measurements from a GPS receiver and an INS on each vehicle. In the first scheme, each FFCC estimates its own absolute state using an extended Kalman filter (EKF) and the relative state is found by differencing the absolute state estimates from each filter. In the second scheme, one vehicle processes the single vehicle EKF while the second processes the differential range and Doppler measurements to estimate the relative state. In the third scheme, differential carrier phase measurements are added to the relative state estimation filter. The third scheme meets the target values for flying two NASA F-18 A/C in flight formation. Relative position is estimated within 10 cm, and relative attitude within 0.5 deg.

This paper presents a fully autonomous and low-cost quadrotor helicopter system in GPS-denied environments. An extended Kalman filter (EKF) framework is employed to fuse the raw data from inertial measurement unit (IMU), optical flow, ultrasonic rangefinder to achieve real-time state estimation of the quadrotor. An improved cascaded proportional-integral-derivative (PID) control scheme is proposed to achieve high-precision flight control for the quadrotor. A lightweight, powerful app of DroneKit-Python is integrated to directly guide the flight mission of the quadrotor on an onboard computer. The onboard computer which communicates with the flight controller using the MAVLink protocol augments the performance of the autopilot by a low-latency link. Finally, the autonomous quadrotor system is justified by real-world experiments, and the experimental results show that the quadrotor can realize high-precision autonomous localization in an indoor environment.

Multi-sensor fusion and precise point positioning real-time kinematic (PPP-RTK) techniques are gaining popularity in vehicle navigation studies. The integration of the Global Navigation Satellite System (GNSS), inertial navigation system (INS), and vision can significantly improve the accuracy and continuity of positioning. The methods used to integrate different measurements are usually divided into an extended Kalman filter and factor graph optimization (FGO). We propose an integration method of PPP-RTK/INS/Vision that utilizes both the square-root information filter (SRIF) and FGO, combining the merits of the two algorithms. The proposed PPP-RTK/INS/Vision integration consists of two parts: the first is reweighted PPP-RTK/INS tightly coupled integration based on SRIF, and the second is GNSS/INS/Vision integration based on a two-step FGO method with feature selection in the vision component. The first positioning result is integrated with the FGO-based visual INS with uniform depth distribution to generate the final navigation information. The real-world experiment, carried out in an urban area, includes many GNSS-challenged environments, such as high buildings, tunnels, and viaducts. The results demonstrate that the proposed method achieves a root mean square of 0.57, 0.65, and 0.86 m in the north, east, and down components of the entire trajectory, respectively. Compared to the PPP-RTK/INS/Vision tightly coupled integration with a multi-state constrained Kalman filter, it shows an 11.0% improvement in vertical accuracy and comparable horizontal accuracy. Moreover, in the GNSS-challenged environment, the performance of the proposed method is more outstanding in the vertical component, with an improvement of 83.0% in the vertical direction and similar horizontal accuracy. Our study provides a novel approach to integrating both measurements and optimal estimation methods.

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs’ localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios.

The algorithm flow of an inertial-based Pedestrian Navigation System (PNS) can be divided into a trajectory-generation stage and trajectory-calibration stage. The Zero-velocity UPdaTe (ZUPT)-aided Extended Kalman Filter (EKF) algorithm is commonly used to resolve the trajectory of a walking person, but it still suffers from long-term drift. Many methods have been developed to suppress these drifts and thus to calibrate the trajectory generated by the previous stage. However, these methods have certain requirements, such as explicit map information or frequent location revisits, which are hard to satisfy in such situations as Search and Rescue (SAR) operations. A new approach is proposed in this paper that requires no explicit presupposition. This approach is based on a particle filter framework, with the weight of particles being adaptively adjusted according to the a priori knowledge of building structures and human behaviours. The distribution of particle weights is designed with awareness of the regular structures of buildings. The time-varying parameter of the distribution is acquired from a Hidden Markov Model (HMM) based on the foregoing odometry, which has a close relation with human behaviour. HMM is trained offline based on samples acquired in advance. Many real-world experiments under various scenarios were performed, and the results indicate good accuracy and robustness of the proposed approach.

Presents the results obtained in our research about application of modern nonlinear filtering techniques to GPS based position estimation. The stand-alone GPS based position estimation problem using GPS pseudo-range and Doppler shifts measurements are described. A model for position and velocity estimation are developed. The model is nonlinear and has variable measurement number for coping with an arbitrary number of satellites. Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. In this work the use of an alternative filter: the unscented Kalman filter (UKF) is proposed. The first experimental results that comprise the comparison of estimation results obtained with a simple model using different filters are then presented. Future research directions are also discussed.

Improvement of stereo vision-based position and velocity estimation and tracking using a stripe-based disparity estimation and inverse perspective map-based extended Kalman filter

An estimation fusion algorithm based on a group of hybrid coordinate (HC) filters is presented to the position and velocity estimation using angle-only measurements extracted from multiple maneuverable aircrafts with onboard passive sensor. The algorithm is a hierarchical architecture which consists of several local processors and a global processor. In each local processor, an extended Kalman (EK) filter utilizes the reference Cartesian coordinate (RCC) system for state and state covariance extrapolation and utilizes the modified spherical coordinate (MSC) system for state and state covariance updating. In the global processor, a recursive least squares (RLS) estimator sequentially computes a global estimate in the local inertial Cartesian coordinate (LICC) system. The estimator is developed by utilizing RCC updated state covariance to compute each filter weight for combining the outputs of local processors. The typical case of target motion analysis is investigated through computer simulation. In simulation study the proposed algorithm is compared to each HC EK filter. The EK filter encounters slow convergence problem under both circular arc and waving flight scenarios. By using a RLS estimator, the convergence is greatly accelerated. The algorithm markedly improves the tracking accuracy as well.

An effective fusion strategy for the observation of the vehicle sideslip angle is proposed in this paper. This approach builds an estimation model based on vehicle dynamics by extended Kalman filter (EKF), and an estimation model based on vehicle kinematics by unscented Kalman filter (UKF). In this research, a correction module is developed to correct the vehicle kinematics-based model observer for the accumulated inaccuracies. Based on this, a frequency domain fusion strategy is presented by fusing the benefits of the two approaches. Finally, simulation utilising the Carsim and Simulink platform is used to confirm the proposed observation strategy. The simulation findings demonstrate that the method can maintain great precision and stability across a wide driving range and under various extreme operating circumstances.