Accession Number : AD1021532


Title :   Radar Methods in Urban Environments


Descriptive Note : Technical Report,01 Aug 2011,31 Jul 2016


Corporate Author : The Washington University Saint Louis United States


Personal Author(s) : Nehorai,Arye


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1021532.pdf


Report Date : 26 Oct 2016


Pagination or Media Count : 16


Abstract : We developed a new algorithm for sparse imaging of perfect electric conducting targets based on higher order sources ormultipoles. We devised a strategy to select the optimal orders without needing any a priori information on the target by taking advantage of some analytical considerations in a canonical case. We developed a data-driven method for target detection in nonstationary environments. We employed drift detection algorithms to detect changes in the environment, which are utilized to intelligibly update the statistical detection algorithms to gain improved detection performance. We developed a method for joint design of amplitudes and frequency-hopping codes for frequency-hopping waveforms using a collocated multiple input/multiple output radar system. We formulated a novel game theory based model and propose two joint design algorithms. We considered a system of two point scatterers under a general multistatic configuration and investigated the effect of multiple scattering in the detection and estimation of scatterers. We investigated the self-calibration problem for perturbed nested arrays in the presence of gain and phase errors, and developed robust algorithms to estimate the direction-of-arrivals (DOAs). We developed an adaptive Gaussian mixture learning algorithm in posterior-based distributed particle filtering, in which posteriors are approximated as Gaussian mixtures for wireless communication. We developed polynomial-time algorithms to compute the performance bounds on convex block-sparsity recovery based on fixed point theory and semi-definite programming. Recently, we also developed new target detection methods using weather radars and electromagnetic vector sensors. We analyzed the asymptotic DOA estimation performance of sparse linear arrays, and derived the corresponding Cramr-Rao bound.


Descriptors :   radar , urban areas , algorithms , targets , Gaussian processes , waveforms


Distribution Statement : APPROVED FOR PUBLIC RELEASE