Theoretical bounds for estimating the ballistic coefficient of a ballistic object during the re-entry phase have been addressed. One essential characteristic of the vehicle trajectory is its deceleration when it reaches dense atmospheric layers. The intensity of the phenomenon is proportional to a scalar, called the ballistic coefficient. This leads to an highly nonlinear time-varying dynamic. To understand the dimensioning parameters for estimating the ballistic coefficient, accurate approximations of the Fisher information matrix are developed. The main result is a closed-form expression of a lower bound for the variance of the ballistic coefficient estimate.Notations † Let S be the object cross-section, C X its drag coefficient and M its mass, † the ballistic coefficient b is the product C x S/m and is expressed in m 2 kg 21 , † go ¼ 29.8 ms 22 is the gravitational acceleration (at ground level), † alt is the altitude; alt W y in the coordinate system of the
This paper deals with a study of the multisensor management problem. The main tool i s the classical assignment formulation, using KullbackLeibler entropy as costs. In order to use the benefit brought b y the data fusion, coalitions or pseudosensors must be created at each step of time, creating an exponential calculus of all the possible sensor partitions. We compare this method and a predefinite strategy using different scenarios.the efficiency of the method than the mean precision, we have used on the second scenario validation gates. These gates, computed with the same noise generation, allow us to cwluate the number of lost targets for each method. It is not, contrarily to the mean precision, a criterion computed by a mean, thcrcforc it better illustrates the "adaptive" behavior of the method. In part 2 we give a short description of the ISIMKF algorithm equations, required to compute the Kullback-Lciblcr's criterion applied to tracking (section 3). In part 4 we discuss different formulations for the assignment problem (section 4), and we finish with a numerical study (section 5).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.