Digital Beamforming and Traffic Monitoring Using the new FSAR System of DLR

In November 2006 the first X-band test flight of DLR’s new FSAR system has been performed successfully and in February 2007 the first flight campaign has been conducted for acquiring experimental multi-channel data of controlled ground moving targets. In the paper the performed experiments and the used setup of the FSAR X-band section are described and preliminary results in the field of ground moving target indication and digital beamforming are presented

Sequential Detection with Mutual Information Stopping Cost

This paper formulates and solves a sequential detection problem that involves the mutual information (stochastic observability) of a Gaussian process observed in noise with missing measurements. The main result is that the optimal decision is characterized by a monotone policy on the partially ordered set of positive definite covariance matrices. This monotone structure implies that numerically efficient algorithms can be designed to estimate and implement monotone parametrized decision policies.The sequential detection problem is motivated by applications in radar scheduling where the aim is to maintain the mutual information of all targets within a specified bound. We illustrate the problem formulation and performance of monotone parametrized policies via numerical examples in fly-by and persistent-surveillance applications involving a GMTI (Ground Moving Target Indicator) radar

A Large Along-Track Baseline Approach for Ground Moving Target Indication Using TanDEM-X

In the paper a new method for ground moving target indication (GMTI) using two satellites (i.e. the TerraSAR-X and the TanDEM-X satellite) together is presented. The along-track baseline between the satellites is chosen to be in the order of several kilometres, so that each satellite observes the same moving vehicles at different times in the order of one to several seconds. The proposed method allows the estimation of the ground velocity of the moving targets as well as the estimation of the broadside positions without the need of complex bistatic processing techniques

Stochastic Inverse Reinforcement Learning

The goal of the inverse reinforcement learning (IRL) problem is to recover the reward functions from expert demonstrations. However, the IRL problem like any ill-posed inverse problem suffers the congenital defect that the policy may be optimal for many reward functions, and expert demonstrations may be optimal for many policies. In this work, we generalize the IRL problem to a well-posed expectation optimization problem stochastic inverse reinforcement learning (SIRL) to recover the probability distribution over reward functions. We adopt the Monte Carlo expectation-maximization (MCEM) method to estimate the parameter of the probability distribution as the first solution to the SIRL problem. The solution is succinct, robust, and transferable for a learning task and can generate alternative solutions to the IRL problem. Through our formulation, it is possible to observe the intrinsic property for the IRL problem from a global viewpoint, and our approach achieves a considerable performance on the objectworld.Comment: 8+2 pages, 5 figures, Under Revie

ACross-Sectional Analysis of CapRates by MSA

Much attention has been paid to capitalization rates or “cap rates?defined as the net operating income over transaction price, also known as a “going-in?current yield on commercial real estate when calculated at the time of purchase. We know that there are a number of global factors that drive capital markets and required rates of return that help to explain observed cap rates over time, but we know little about factors driving the geographical cross-sectional variation of these cap rates. Why are cap rates for similar sized and type property so much lower or higher in one metropolitan statistical area than another? Using data from Real Capital Analytics for multifamily properties we explore several models that combine the expected influences from housing demand growth, supply constraints, liquidity risk and the interaction of these. We document a very strong and robust relation between supply constraints and cap rates as well as evidence of capital flowing from larger markets to smaller markets in recent years. We also find weak but generally supportive evidence of influences from expected growth rates, liquidity and other risk factors.