TerraSAR-X active radar ground calibrator system

In April 2006, the TerraSAR-X satellite was launched. This paper describes the development of a novel and highly integrated, digitally-controlled active SAR system calibrator (DARC). It consists of both an active transponder path for absolute radiometric calibration and a calibrated receiver chain for antenna pattern evaluation of the satellite antenna. A total of 16 active transponder and receiver systems and 17 receiver-only systems will be fabricated for a calibration campaign in 2006.     

Almost instant time inference for hybrid partially dynamic Bayesian networks

A Bayesian network (BN) is a compact representation for probabilistic models and inference. They have been used successfully for many military and civilian applications. It is well known that, in general, the inference algorithms to compute the exact a posterior probability of a target node given observed evidence are either computationally infeasible for dense networks or impossible for general hybrid networks. In those cases, one either computes the approximate results using stochastic simulation methods or approximates the model using discretization or a Gaussian mixture model before applying an exact inference algorithm. This paper combines the concept of simulation and model approximation to propose an efficient algorithm for those cases. The main contribution here is a unified treatment of arbitrary (nonlinear non-Gaussian) hybrid (discrete and continuous) BN inference having both computation and accuracy scalability. The key idea is to precompile the high-dimensional hybrid distribution using a hypercube representation and apply it for both static and dynamic BN inference. Since the inference process essentially becomes a combination of table look-up and some simple operations, the method is shown to be extremely efficient. It can also he scaled to achieve any desirable accuracy given sufficient preprocessing time and memory for the cases where exact inference is not possible     

Recent Breakthroughs in RF Photonics for Radar Systems

Radar systems require transmission of very high purity signals. Photonics is now mature enough to achieve analog transmission with very low noise, strong immunity, and wide-bandwidth even in harsh environments. We present our recent developments of optimized optical links dedicated to radar and multifunction systems     

Performance evaluation for MAP state estimate fusion

This paper presents a quantitative performance evaluation method for the maximum a posteriori (MAP) state estimate fusion algorithm. Under ideal conditions where data association is assumed to be perfect, it has been shown that the MAP or best linear unbiased estimate (BLUE) fusion formula provides the best linear minimum mean squared estimate (LMMSE) given local estimates under the linear Gaussian assumption for a static system. However, for a dynamic system where fusion is recursively performed by the fusion center on local estimates generated from local measurements, it is not obvious how the MAP algorithm will perform. In the past, several performance evaluation methods have been proposed for various fusion algorithms, including simple convex combination, cross-covariance combination, information matrix, and MAP fusion. However, not much has been done to quantify the steady state behavior of these fusion methods for a dynamic system. The goal of this work is to present analytical fusion performance results for MAP state estimate fusion without extensive Monte Carlo simulations, using an approach developed for steady state performance evaluation for track fusion. Two different communication strategies are considered: fusion with and without feedback to the sensors. Analytic curves for the steady state performance of the fusion algorithm for various communication patterns are presented under different operating conditions.     

Optimal number of phased array faces and signal processors for horizon surveillance

The problem of the optimal number of phased array faces for performing 360/spl deg/ horizon surveillance is considered. Assuming the detection performance is the same in all beam positions and the total number of T/R modules is constant, it is shown that the optimal number of array faces is three. This is true whether the arrays are operating simultaneously or sequentially. A parametric analysis is performed between the number of array faces operating simultaneously and the associated cost of simultaneous operation in terms of the size of the array.     

Distributed decision fusion using empirical estimation

The problem of optimal data fusion in multiple detection systems is studied in the case where training examples are available, but no a priori information is available about the probability distributions of errors committed by the individual detectors. Earlier solutions to this problem require some knowledge of the error distributions of the detectors, for example, either in a parametric form or in a closed analytical form. Here we show that, given a sufficiently large training sample, an optimal fusion rule can be implemented with an arbitrary level of confidence. We first consider the classical cases of Bayesian rule and Neyman-Pearson test for a system of independent detectors. Then we show a general result that any test function with a suitable Lipschitz property can be implemented with arbitrary precision, based on a training sample whose size is a function of the Lipschitz constant, number of parameters, and empirical measures. The general case subsumes the cases of nonindependent and correlated detectors.     

Carbon dioxide and oxygen budgets of a plant cultural system in a CELSS--a case of cultivation of lettuce and turnips.

In order to collect basic data about CO2 and O2 budgets of a plant cultural system in a CELSS, the variation of the CO2 absorption rates of lettuce and turnips were observed during the growing period, under different conditions. The O2 release rates were deduced from the CO2 absorption rates multiplied by 32/44. As a result, when the light intensity, the photoperiod and the atmospheric CO2 concentration increased, the rates also increased. The effects on the turnips were more significant than those on the lettuce. Turnips at 310 micromoles/m2/s of PPFD, 24 hours of photoperiod and 1100 ppm of CO2 concentration grew most actively in the present experimental conditions. One turnip absorbed 32.3 g CO2 and released 23.5 g O2 for 6 days between 24 days and 30 days after sowing.     

First estimations of the aerosol optical thickness using Langley Method at Southern Brazil (29.4°S, 53.8°W)

The first estimations of the aerosol optical thickness (AOT) using Langley Method at Southern Space Observatory (SSO) at Southern Brazil (29.4°S, 53.8°W) are presented. In addition to ozone and sulphur dioxide columns, AOT can be obtained using Brewer Spectrophotometer at specific wavelengths: 306.3, 310.1, 313.5, 316.8 and 320.1 nm. The AOT was obtained for the period from November/2002 to May/2003. Very low AOT averages were obtained, whose values were about 0.21 ± 0.03 at 306.3 nm, 0.21 ± 0.02 at 310.1 nm, 0.19 ± 0.02 at 313.5 nm, 0.20 ± 0.02 at 316.8 nm and 0.20 ± 0.02 at 320.0 nm for all period analysed. Different behaviour of AOT were obtained at two daily specific periods of aerosol accumulation, one in the afternoons from November/2002 to February/2003, caused mainly by a mild biomass burning season’s in the region and other in the mornings from March to May/2003, due the high relative humidity presented in the region studied.     

Problem of precision missile guidance: LQR and H/sup /spl infin// control frameworks

Addressed here is the precision missile guidance problem where the successful intercept criterion has been defined in terms of both minimizing the miss distance and controlling the missile body attitude with respect to the target at the terminal point. We show that the H/sup /spl infin// control theory, when suitably modified, provides an effective framework for the precision missile guidance problem. The existence of feedback controllers (guidance laws) is investigated for the case of finite horizon and non-zero initial conditions. Both state feedback and output feedback implementations are explored.     

Limits in tracking with extended Kalman filters

The classical linearized conversion of measurements from polar or spherical coordinates to Cartesian ones generates a bias restricting the use of this conversion to cases where the bias can be neglected. In this work, the validity limits for the classical 2D transformation from polar to Cartesian coordinates, as derived in previous work, are shown to be too restrictive and the limits for the 3D transformation from spherical to Cartesian coordinates are introduced. Furthermore, quantitative measures for the performance degradation of the commonly used extended Kalman filter (EKF) in comparison with the best linear unbiased estimation (BLUE) filter are obtained by simulating typical tracking scenarios.