Tracking with bistatic radar measurements is a challenging problem due to the nonlinear relationship between the radar measurements and the Cartesian coordinates, especially for long distances. This nonlinearity leads, for 3-D bistatic radar, to a non-ellipsoidal measurement uncertainty region in Cartesian coordinates, similar to a thin contact lens, that causes consistency problems for a tracking filter. The recently developed conversion of the bistatic radar measurements into Cartesian coordinates enables to maintain consistency by using a converted measurement Kalman filter. However, such a filter suffers from a poor bistatic range accuracy, limiting the multitarget tracking performance in a dense environment of targets or clutter. A solution is suggested by using a filter in the measurement coordinate system and converting its results into Cartesian coordinates. Consistent Cartesian estimation is obtained together with significantly improved filtered bistatic range accuracy. The latter is important in data association, resulting in a measurement-to-track association gates that are over an order of magnitude smaller compared to the Cartesian filters. It is shown that if the data association is performed in sensor coordinates, the (Cartesian) volume of the contact lens association region is over 20 times smaller than if it is performed in Cartesian coordinates because the ellipsoid in the latter case is extremely conservative.

Chalcogenide-based ultrathin all-dielectric metamaterials with multifunctional polarized/unpolarized wide/narrow-band perfect mid-infrared absorption spectrally tunable with the azimuthal angle of incidence.

A specially designed phase mask embedded in the lens assembly of an imaging system is shown to provide different response in the three major color bands, R, G and B of a detector array. Each channel provides optimal performance for different depth of field regions, such that the three channels jointly provide an imaging system with wide depth of field. The approach is useful in particular for Barcode imagers.

This paper considers the problem of estimating the trajectory of a ballistic target in the reentry phase using 2D measurements (azimuth and elevation angles) from a moving passive sensor. Previous works investigated the estimation problem of an object in the thrusting and initial ballistic phase from a single fixed passive sensor. This work shows that the 3D trajectory in the reentry phase can be obtained by estimating the target's state at the end of the observation interval. The 7-dimensional motion parameter vector (velocity azimuth angle, velocity elevation angle, drag coefficient, target speed and 3D position) is estimated by the Maximum Likelihood (ML) estimator with numerical search. Then we can predict the future position at an arbitrary time and the impact point of the target. The observability of the system for a sensor on a fast aircraft moving with constant velocity or maneuvering is verified via the invertibility of the Fisher Information Matrix. This is a major extension of the applicability of the recent observability proof for a stationary passive sensor observing a target in a gravitational field. The Cramer--Rao lower bound for the estimated parameters is evaluated and it shows that the estimates are statistically efficient. The angle estimation performance for the ML estimator is also compared with that of the polynomial fitting method. Simulation results illustrate the effectiveness of the proposed method.