An Instantaneous Impact Point Guidance for Rocket with Aerodynamics Control

This paper presents analytic expressions for the time derivatives of a Keplerian instantaneous impact point of a rocket. The derivatives can be obtained by differentiating the series of equations associated with the Keplerian instantaneous impact point of a rocket and are expressed as linear combinations of disturbing acceleration components whose coefficients are functions of the current position and velocity of the rocket. The formulae introduced in this paper have been carefully verified using different cases. The verification results for the proposed derivatives prove that they successfully represent the motion of the instantaneous impact point under various conditions for the disturbing acceleration. The results of this paper could be applied to problems arising in rockets/launch vehicles operations, such as the steering of a rocket or the augmentation of a range safety system.

This paper proposed an analytical iterative guidance method with the desired instantaneous impact point constraint for solid rockets in “burn-coast-burn” trajectory mode. Solid rocket motors expect to remove the thrust termination mechanism to increase the structural strength and launch reliability, which induce new difficulties and challenges to the guidance problems. In terms of the “Hohmann transfer” principle, a pointing algorithm is deduced in depth to establish the theoretical relations among the ignition time, the required velocity vector, and the orbital element constraints and provides the analytical expression of the ignition time. Then, an analytic solution of the required velocity vector is derived based on orthogonal and nonorthogonal velocity vectors, and a complete guidance logic is used to solve the target orbit elements satisfying the desired instantaneous impact point. Finally, the application of the developed theoretical algorithm in this paper is conducted using a two-stage solid rocket. The proposed guidance method is verified by Monte Carlo simulations, and the testing results demonstrate the adaptability, strong robustness, and excellent performance for different desired impact point missions.

Near time-optimal feedback instantaneous impact point (IIP) guidance law for rocket

Analyzing a player's technique in table tennis requires knowledge of the ball's 3D trajectory and spin. While, the spin is not directly observable in standard broadcasting videos, we show that it can be inferred from the ball's trajectory in the video. We present a novel method to infer the initial spin and 3D trajectory from the corresponding 2D trajectory in a video. Without ground truth labels for broadcast videos, we train a neural network solely on synthetic data. Due to the choice of our input data representation, physically correct synthetic training data, and using targeted augmentations, the network naturally generalizes to real data. Notably, these simple techniques are sufficient to achieve generalization. No real data at all is required for training. To the best of our knowledge, we are the first to present a method for spin and trajectory prediction in simple monocular broadcast videos, achieving an accuracy of 92.0% in spin classification and a 2D reprojection error of 0.19% of the image diagonal.

A fusion tracking filter for the KSLV (Korea Space Launch Vehicle) flight safety system is developed to integrate multiple tracking sensor data: Radar, Electro Optical Tracking System, INS, and GPS data. Multiple sensor data are pre-filtered by individual sub filters and then fused by a covariance fusion filter. The estimation accuracy and robustness of position and instantaneous impact point are analyzed using numerical simulations.

The development and verification of a dedicated GPS sensor for sounding rocket missions is described. It is based on the hardware design of a terrestrial low cost L1 C/A code receiver but operates an enhanced software that has been specifically adapted for high dynamics applications. Besides the navigation and timing function provided by traditional Global Positioning System receivers, the prediction of the instantaneous impact point (IIP) has for the first time been integrated into the receiver software. Making use of a newly developed perturbed-parabolic trajectory model the receiver can directly perform real-time IIP predictions with an accuracy that is compatible with operational ground software and is only limited by atmospheric forces. It is expected that the availability of onboard IIP prediction will both simplify existing range safety systems and contribute to a future increase of the onboard autonomy of sounding rocket missions. The overall receiver performance is demonstrated with hardware-in-theloop simulations and actual flight data for representative mission profiles.

In this paper, a system solution is designed to improve the service efficiency of intersection. For the purpose, a method of trajectory prediction for vehicle is put forward, which is based on the basis of realize multi-targets tracking. The characteristics of traffic video, structural of road profile and movement of vehicles have been analyzed. We designed the multi-targets tracking algorithm based on particle filter for traffic video sequence, in order to improve the accuracy and rapidity, the heuristic method for multi-targets data correlation is proposed. Based on the results of tracking, we analyze the rule of movement parameters of vehicle as the path has changed, according to the features of structured road the vehicle running status parameters is simplified, and a Hidden Markov Model (HMM) has trained, which is used for vehicle trajectory prediction. In the end, Analysis the basic reason of traffic conflict and the dynamic model of traffic conflict zone is designed. Experimental results demonstrate that, the method of tracking based on particle filter has good tracking performance in traffic video; the vehicle trajectory can be predicted rapidly in the required time, and traffic conflict zone is produced accurately.

Trajectory prediction is a prerequisite for missile and high-speed vehicle guidance interception. To address the trajectory problem for the near space hypersonic unpowered gliding vehicle. Firstly, the trajectory prediction mechanism is analyzed. Based on the existing NSHV trajectory prediction method, the key techniques of trajectory prediction are discussed from the perspectives of prediction parameter selection and prediction parameter description. Then, a trajectory prediction method based on Hough transform is proposed. The core of the method is to select the longitude latitude, and high direction positions as prediction parameters, and use the Hough transform to fit the prior basis functions. In the latitude and longitude direction, the rationality of predicting parameters is discussed from two aspects: position extreme point and lateral maneuverability. In the height direction, for the NSHV oscillation drop characteristic, two Hough transforms are used to separate the periodic term from the linear term. In addition, we give the approximate interval of the parameter values of the method, and design a parameter adaptive scheme. Finally, trajectory prediction is performed on two different control patterns. The error of the prediction time of 100s is within 25km, and the results show the effectiveness of the proposed method.

Connected and Automated Vehicles (CAV) have been rapidly developed, which, inevitably, renders that human-driven and autonomous vehicles share the road. Thus, trajectory prediction is an important research topic, which helps each CAV to efficiently follow a Human-Driven Vehicle (HV). In a wider scope, trajectory prediction, also, helps to improve the throughput of traffic flow and enhance its stability. To realize the trajectory prediction of Connected and Automated Vehicles to Human-Driven Vehicles, a car-following model, which is based on trajectory data, was established. Adding deep neural networks and an Attention mechanism, this paper established a data-driven car-following model, based on CNN-BiLSTM-Attention for CAV, to predict trajectory, by referring to the modeling idea of the traditional car-following model. The trajectory data in the next-generation-simulation (NGSIM) datasets that match the car-following characteristics were selected. In addition, noise-reduction pre-processing of the trajectory data was performed, to make it match the actual car-following situation. Experiments, for selecting the optimal structure of the model and the method of trajectory prediction, were carried out. The data-driven car-following models, such as LSTM, GRU, and CNN-BiLSTM, were selected for comparative analysis of trajectory prediction. The results show that the CNN-BiLSTM-Attention model has the smallest MAE and MSE as well as the largest R2. The CNN-BiLSTM-Attention model has the highest accuracy in vehicle-trajectory prediction. The model can, effectively, realize vehicle-trajectory prediction and provide a theoretical basis for vehicle-trajectory-based velocity guidance of Human-Driven Vehicles. In the future, the model can, also, provide the theoretical basis for Connected and Automated Vehicles, to make car-following decisions in mixed traffic flow.

Accurate detection of human motion states and prediction of intentions are crucial for the natural walking assistance provided by lower-limb exoskeletons. Several methods for gait task recognition, phase estimation, and motion trajectory prediction have been proposed, but few studies have considered all of these simultaneously. This article proposes a novel scheme for gait recognition and trajectory prediction, capable of detecting gait events, recognizing multiple gait tasks, estimating gait phases, and predicting trajectories. First, this scheme proposes a gait task recognition method based on gait events and a multilayer perceptron neural network (MLPNN), which reduces computational cost and improves recognition stability. Next, a gait phase estimation method based on resettable symmetric adaptive oscillators (RSAOs) is introduced, which offers faster convergence speed and smaller error compared to traditional adaptive oscillators (AOs). Subsequently, a trajectory prediction method with adaptive dynamic motion primitives (ADMP) and feedback regulation is proposed. Finally, gait task recognition, phase estimation, trajectory prediction, and comprehensive online experiments are carried out respectively. The online experimental results show that the recognition accuracy of gait tasks can reach 99.2%, the phase estimation error is less than 0.3652 rad, and the trajectory estimation error is less than 1.1814°.

Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions

Pedestrian trajectory prediction is fundamental to a wide range of scientific research work and industrial applications. Most of the current advanced trajectory prediction methods incorporate context information such as pedestrian neighbourhood, labelled static obstacles, and the background scene into the trajectory prediction process. In contrast to these methods which require rich contexts, the method in our paper focuses on predicting a pedestrian's future trajectory using his/her observed part of the trajectory only. Our method, which we refer to as LVTA, is a Location-Velocity-Temporal Attention LSTM model where two temporal attention mechanisms are applied to the hidden state vectors from the location and velocity LSTM layers. In addition, a location-velocity attention layer embedded inside a tweak module is used to improve the predicted location and velocity coordinates before they are passed to the next time step. Extensive experiments conducted on three large benchmark datasets and comparison with eleven existing trajectory prediction methods demonstrate that LVTA achieves competitive prediction performance. Specifically, LVTA attains 9.19 pixels Average Displacement Error (ADE) and 17.28 pixels Final Displacement Error (FDE) for the Central Station dataset, and 0.46 metres ADE and 0.92 metres FDE for the ETH&UCY datasets. Furthermore, evaluation on using LVTA to generate trajectories of different prediction lengths and on new scenes without the need of retraining confirms that it has good generalizability.

A fast and accurate algorithm is established in this paper to increase the precision of ballistic trajectory prediction. The algorithm is based on the six-degree-of-freedom (6DOF) trajectory equations, to estimate the projectile attitude angles in every measuring time. Hereby, the algorithm utilizes the Davidon-Fletcher-Powell (DFP) method to solve nonlinear equations and Doppler radar trajectory test information containing only position coordinates of the projectile to reconstruct the angular information. The ″position coordinates by the test″ and ″angular displacements by reconstruction″ at the end phase of the radar measurement are used as an initial value for the trajectory computation to extrapolate the trajectory impact point. The numerical simulations validate the proposed method and demonstrate that the estimated impact point agrees very well with the real one. Morover, other artillery trajectory can be predicted by the algorithm, and other trajectory models, such as 4DOF and 5DOF models, can also be incorporated into the proposed algorithm.

Data-driven trajectory prediction with weather uncertainties: A Bayesian deep learning approach

This paper presents the results from a test of the performance of several general trajectory prediction methods and proposes a hybrid trajectory prediction model that aims to increase the safety of flights en route and improve airspace management capabilities by predicting the aircraft’s four-dimensional trajectory (4DT) more accurately. The automatic dependent surveillance-broadcast (ADS-B) data from 589 trajectories of cruising aircraft from the Guangzhou area were extracted for experiments. Numerous trajectory prediction methods, including velocity trend extrapolation, long short-term memory (LSTM), stateful-LSTM, back propagation (BP) neural network, a one-dimensional convolutional neural network (1D-ConvNet), Kalman filter, and flight plan interpolation were used for prediction experiments, and their performance at different time spans of prediction is obtained. By extracting the best methods using different time spans of prediction, a hybrid prediction model is proposed based on the reconstruction of these methods. For the data in this paper, the mean squared error (MSE) of the hybrid prediction model is significantly reduced compared to other methods in different time spans of prediction, which has great significance for future trajectory prediction in a structured airspace.