State estimation of spiralling target using UKF with generalized sinusoidal target acceleration model

Hit-to-kill interception of high velocity spiraling target requires accurate state estimation of relative kinematic parameters describing spiralling motion. In this pa- per, spiraling target motion is captured by representing target acceleration through sinusoidal function in inertial frame. A nine state unscented Kalman filter (UKF) formulation is presented here with three relative positions, three relative velocities, spiraling frequency of target, inverse of ballistic coefficient and maneuvering coef-ficient. A key advantage of the target model presented here is that it is of generic nature and can capture spiraling as well as pure ballistic motions without any change of tuning parameters. Extensive Six-DOF simulation experiments, which includes a modified PN guidance and dynamic inversion based autopilot, show that near Hit-to-Kill performance can be obtained with noisy RF seeker measurements of gimbal angles, gimbal angle rates, range and range rate.

Nonlinear and linear autopilot performance comparison of tactical flight vehicle

n this paper, three-axis autopilot of a tactical flight vehicle has been designed for surface to air application. Both nonlinear and linear design synthesis and analysis have been carried out pertaining to present flight vehicle. Lateral autopilot performance has been compared by tracking lateral acceleration components along yaw and pitch plane at higher angles of attack in presence of side force and aerodynamic nonlinearity. The nonlinear lateral autopilot design is based on dynamic inversion and time scale separation principle. The linear lateral autopilot design is based on three-loop topology. Roll autopilot robustness performance has been enhanced against unmodeled roll disturbances by backstepping technique. Complete performance comparison results of both nonlinear and linear controller based on six degrees of freedom simulation along with stability and robustness studies with respect to plant parameter variation have been discussed in the paper.

Generalized State Estimation and Model Predictive Guidance for Spiraling and Ballistic Targets

An extended Kalman filter based generalized state estimation approach is presented in this paper for accurately estimating the states of incoming high-speed targets such as ballistic missiles. A key advantage of this nine-state problem formulation is that it is very much generic and can capture spiraling as well as pure ballistic motion of targets without any change of the target model and the tuning parameters. A new nonlinear model predictive zero-effort-miss based guidance algorithm is also presented in this paper, in which both the zero-effort-miss as well as the time-to-go are predicted more accurately by first propagating the nonlinear target model (with estimated states) and zero-effort interceptor model simultaneously. This information is then used for computing the necessary lateral acceleration. Extensive six-degrees-of-freedom simulation experiments, which include noisy seeker measurements, a nonlinear dynamic inversion based autopilot for the interceptor along with appropriate actuator and sensor models and magnitude and rate saturation limits for the fin deflections, show that near-zero miss distance (i.e., hit-to-kill level performance) can be obtained when these two new techniques are applied together. Comparison studies with an augmented proportional navigation based guidance shows that the proposed model predictive guidance leads to a substantial amount of conservation in the control energy as well.

Fusing the Navigation Information of Dual Foot-Mounted Zero-Velocity-Update-Aided Inertial Navigation Systems

A range constraint method viz. centroid method is proposed to fuse the navigation information of dual (right and left) foot-mounted Zero-velocity-UPdaTe (ZUPT)-aided Inertial Navigation Systems (INSs). Here, the range constraint means that the distance of separation between the position estimates of right and left foot ZUPT-aided INSs cannot be greater than a quantity known as foot-to-foot maximum separation. We present the experimental results which illustrate the applicability of the proposed method. The results show that the proposed method significantly enhances the accuracy of the navigation solution when compared to using two uncoupled foot-mounted ZUPT-aided INSs. Also, we compare the performance of the proposed method with the existing data fusion methods.

Composite Guidance for Impact Angle Control Against Higher Speed Targets

In the literature, the impact angle control problem has been addressed mostly against lower speed or stationary targets. However, in the current defense scenario, targets of much higher speeds than interceptors are a reality. Moreover, approaching a higher speed target from a specified angle is important for effective seeker acquisition and enhanced warhead effectiveness. This paper proposes a composite proportional navigation guidance law using a combination of the standard proportional navigation and the recently proposed retroproportional navigation guidance laws for intercepting higher speed nonmaneuvering targets at specified impact angles in three-dimensional engagements. An analysis of the set of achievable impact angles by the composite proportional navigation guidance law is presented. It is shown that there exists an impulse bias that, when added to the composite proportional navigation guidance command, expands this set further by reversing the direction of the line-of-sight angular rotation vector. A bound on the magnitude of the bias is also derived. Finally, an implementation of this impulse bias, in the form of a series of pulses, is proposed and analyzed. Simulation results are also presented to support the analysis.

Composite Guidance for Impact Angle Control Against Higher Speed Targets

In the literature, the impact angle control problem has been addressed mostly against lower speed or stationary targets. However, in the current defense scenario, targets of much higher speeds than interceptors are a reality. Moreover, approaching a higher speed target from a specified angle is important for effective seeker acquisition and enhanced warhead effectiveness. This paper proposes a composite proportional navigation guidance law using a combination of the standard proportional navigation and the recently proposed retroproportional navigation guidance laws for intercepting higher speed nonmaneuvering targets at specified impact angles in three-dimensional engagements. An analysis of the set of achievable impact angles by the composite proportional navigation guidance law is presented. It is shown that there exists an impulse bias that, when added to the composite proportional navigation guidance command, expands this set further by reversing the direction of the line-of-sight angular rotation vector. A bound on the magnitude of the bias is also derived. Finally, an implementation of this impulse bias, in the form of a series of pulses, is proposed and analyzed. Simulation results are also presented to support the analysis.

De La navigation de l'Amazone : reponse a un memoire de M. Maury, officier de la marine des Etats-Unis

Pedro de Ângelis, militar e político italiano, nasceu em Nápolis, em 1784, e morreu em Buenos Aires, em 1854. Acompanhou o Rei Murat no exílio, após sua queda, e o preceptor de seus filhos. Retornou à Itália no regime constitucionalista, exilando-se novamente com o estabelecimento do absolutismo. A convite de Rivadavia, fixou-se em Buenos Aires, participando com destaque da política da República do Prata, contrária aos direitos do Brasil. Tornou-se, todavia, profundo conhecedor da história e da política da história e da política da América do Sul. De la navigation de l’Amazone, publicação em francês, editada em Montevidéu, segundo Borba de Moraes, “foi escrita a pedido do Governo brasileiro”, que foi o financiador da obra, em resposta aos planos de Maury para colonizar o Vale do Amazonas com afro-norte-americanos. “Apesar da sua parcialidade, a obra pode ser considerada valiosa contribuição para o conhecimento do Brasil”, como afirma Palau. Foi, também, publicada em espanhol, francês e português.

Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation

This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called the freezing robot problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m², while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m². We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.