β Scribed by Katarina Boman; Petre Stoica
No coin nor oath required. For personal study only.
In this work we study the performance of elevation estimators and lower bounds of the estimation error variance for a low angle target in a smooth sea scenario using an antenna array. The article is structured around some key assumptions on multipath knowledge, signal parameterization and noise covariance. We prove that the CramΓ©r-Rao bound is highly dependent on the multipath model, while it is the same for the different signal parameterizations, and that it is independent of the noise covariance. The CramΓ©r-Rao bound is sometimes too optimistic and not achievable. The tighter Barankin bound is derived to predict the threshold behavior seen at low SNR. Simulations show that the maximum likelihood methods are statistically efficient and achieve the theoretical lower bound on error variance, in the case of high enough SNR. Finally we show that the bounds can be used to design an improved array structure and study the influence of multiple frequencies.
π SIMILAR VOLUMES
## Abstract We present a practical method to estimate the magnetization transfer (MT) boundβpool fraction (__M__^__b__^~__0__~). The method is based on a firstβorder approximation of the saturation equation and allows an in vivo estimate of __M__^__b__^~__0__~, previously estimated only in vitro an
In Bayesian analysis of vector autoregressive models, and especially in forecasting applications, the Minnesota prior of Litterman is frequently used. In many cases other prior distributions provide better forecasts and are preferable from a theoretical standpoint. Several of these priors require nu