Guidance system

About: Guidance system is a research topic. Over the lifetime, 4282 publications have been published within this topic receiving 45964 citations.

Adjoint Method for Missile Performance Analysis on State-Space Models

Abstract: The adjoint method is a method widely used in the preliminary design of guidance loops to obtain quick estimates of the performance of a guided weapon while avoiding time consuming Monte Carlo simulation experiments. Traditionally, the adjoint method is presented as a set of rules for transforming the original (linearized) model of the guidance loop to an adjoint model that is used to obtain the performance estimate for the original model in a single simulation run. An attempt is made to derive the adjoint method in the general setting of state-space models. This does not only lead to an elegant and clear exposition of the adjoint method, but it also extends the area of application of the adjoint method to more general situations that were not previously covered in the literature. One possible extension is illustrated with the analysis of the performance of a rolling missile against a target that performs a maneuver with a random start time. I. Introduction T HE adjoint method has a well-established place in the arsenal of tools available for guidance-loop performance analysis, in particular in preliminary phases of guided missile design. The success of the method is mainly because it is relatively simple to use and because it enables a quick performance assessment for a wide range of engagement conditions. The method as a general analysis method for control systems under stochastic disturbances was introduced by Laning and Battin. 1 However, some authors, such as Zarchan, 2 place the origin of the method back to the work of the 19th century mathematician Vitto Voltera. Zarchan mentions the application of the method in computing ballistic dispersions as early as the 1920s. Although the method is also applicable for the analysis of systems under deterministic disturbances, it is the stochastic case that demonstrates the real power of the method. In this case, the method can be efficiently used to avoid time consuming Monte Carlo simulations when linear approximation results can deliver sufficient accuracy. The traditional presentation of the adjoint method in the literature 2,3 is based on the input‐output (transfer function-type) system representation with a strong emphasis on the procedural aspects of the method: inverting the sense of the signal flow, substituting time with time-to-go, etc. In this work, we pursue in detail the derivation of the construction rules for the adjoint system based on state-space models. We consider both the deterministic and the stochastic case for continuoustime models. In the stochastic case, our approach is shown to extend the potential of the method beyond applications currently reported in the literature that assume uncorrelated inputs and look only at the variance of the chosen output signal. We show here that the adjoint method can perfectly accommodate correlations between the different inputs and that a possible outcome is the influence of the random inputs on the covariance of two different outputs at the a priori fixed moment of time. In this sense, it is shown that the analysis power of the adjoint method is almost identical to that of another method for preliminary design analysis called the covariance ma

Real-Time Multiple-Objective Path Search for In-Vehicle Route Guidance Systems

Abstract: The application of multiple-objective route choice for in-vehicle route guidance systems is discussed. A bi-objective path search algorithm is presented and its use demonstrated. A concept of trip quality is introduced that is composed of two objectives: minimizing travel time and minimizing trip complexity. Trade-offs between the objectives are examined. The concept is illustrated through simulation modeling on a test network. The experiments serve to demonstrate the effects on the trip performance of pretrip routing and dynamic routing strategies under full market penetration (an idealized condition) and under varying levels of demand and trade-offs between time and complexity.

Digital flight guidance system

Abstract: A digital flight guidance system includes a pair of processors, each with its own memory means and arranged with common input and output means for providing monitored guidance of an aircraft when the craft is in cruise modes or when the craft is performing a critical maneuver such as landing, terrain following, or the like

An Optical System for Guidance of Terrain Following in UAVs

Abstract: There is considerable interest in designing guidance systems for UAVs that use passive sensing (such as vision), rather than active sensing which can be bulky, expensive and stealth-compromising. Here we describe an optical sensor, based partly on principles of insect vision and optic flow analysis, for measurement and control of height above the ground. A video camera is used in conjunction with a specially shaped reflective surface to simplify the computation of optic flow, and extend the range of aircraft speeds over which accurate data can be obtained. The imaging system also provides a useful geometrical remapping of the environment, which facilitates obstacle avoidance and computation of 3-D terrain maps.

Navigation and guidance control system of AUV with trajectory estimation of linear modelling

Abstract: This paper put forwards a study on the development of navigation and guidance systems for AUV. The restriction in AUV model and estimation on the degree of freedom are recognized as the common problem in AUV's navigation and guidance systems. In this respect a linear model, derived from the linearization using the Jacobian matrix, will be utilized. The so obtained linear model is then estimated by the Ensemble Kalman Filter (EnKF). The implementation of EnKF algorithm on the linear model is carried out by establishing two simulations, namely by generating 300 and 400 ensembles, respectively. The simulations exhibit that the generation of 400 ensembles will give more accurate results in comparison to the generation of 300 ensembles. Furthermore, the best simulation yields the tracking accuracy between the real and simulated trajectories, in translational modes, is in the order of 99.88%, and in rotational modes is in the order of 99.99%.