Adaptive polarimetry design for a target in compound-Gaussian clutter
Signal Processing
Notes on the tightness of the hybrid Cramér-Rao lower bound
IEEE Transactions on Signal Processing
Channel estimation in OFDM systems with unknown interference
IEEE Transactions on Wireless Communications
An EM-based frequency offset estimator for OFDM systems with unknown interference
IEEE Transactions on Wireless Communications
Uplink synchronization in OFDMA spectrum-sharing systems
IEEE Transactions on Signal Processing
OFDM MIMO radar for low-grazing angle tracking
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
MIMO radar detection and adaptive design under a phase synchronization mismatch
IEEE Transactions on Signal Processing
Frame detection and timing acquisition for OFDM transmissions with unknown interference
IEEE Transactions on Wireless Communications
Theory and Use of the EM Algorithm
Foundations and Trends in Signal Processing
| Hi-index | 35.69 |
Compound-Gaussian models are used in radar signal processing to describe heavy-tailed clutter distributions. The important problems in compound-Gaussian clutter modeling are choosing the texture distribution, and estimating its parameters. Many texture distributions have been studied, and their parameters are typically estimated using statistically suboptimal approaches. We develop maximum likelihood (ML) methods for jointly estimating the target and clutter parameters in compound-Gaussian clutter using radar array measurements. In particular, we estimate i) the complex target amplitudes, ii) a spatial and temporal covariance matrix of the speckle component, and iii) texture distribution parameters. Parameter-expanded expectation-maximization (PX-EM) algorithms are developed to compute the ML estimates of the unknown parameters. We also derived the Cramer-Rao bounds (CRBs) and related bounds for these parameters. We first derive general CRB expressions under an arbitrary texture model then simplify them for specific texture distributions. We consider the widely used gamma texture model, and propose an inverse-gamma texture model, leading to a complex multivariate t clutter distribution and closed-form expressions of the CRB. We study the performance of the proposed methods via numerical simulations