<![CDATA[ IEEE Signal Processing Letters - new TOC ]]>
http://ieeexplore.ieee.org
TOC Alert for Publication# 97 2017July 20<![CDATA[Designing Incoherent Frames With Only Matrix–Vector Multiplications]]>24912651269359<![CDATA[Two-Valued Periodic Complementary Sequences]]>24912701274120<![CDATA[Performance Tradeoff in a Unified Passive Radar and Communications System]]>24912751279319<![CDATA[An IHT Algorithm for Sparse Recovery From Subexponential Measurements]]> $\ell _1$-minimization as a recovery algorithm. We show in this letter that such a statement remains valid if one uses a new variation of iterative hard thresholding as a recovery algorithm. The argument is based on a modified restricted isometry property featuring the $\ell _1$-norm as the inner norm.]]>24912801283107<![CDATA[Adaptive Kalman Filtering by Covariance Sampling]]>a priori. In particular, the adjustment of measurement noise covariance is deemed paramount as it directly affects the estimation accuracy and plays the key role in applications such as sensor selection and sensor fusion. This letter proposes a novel adaptive scheme by approximating the measurement noise covariance distribution through finite samples, assuming the noise to be white with a normal distribution. Exploiting these samples in approximation of the system state a posteriori leads to a Gaussian mixture model (GMM), the components of which are acquired by Kalman filtering. The resultant GMM is then reduced to the closest normal distribution and also used to estimate the measurement noise covariance. Compared to previous adaptive techniques, the proposed method adapts faster to the unknown parameters and thus provides a higher performance in terms of estimation accuracy, which is confirmed by the simulation results.]]>24912881292255<![CDATA[Comparison of Different Methodologies of Parameter-Estimation From Extreme Values]]>24912931297376<![CDATA[Non-Orthogonal Multiple Access Combined With Random Linear Network Coded Cooperation]]>24912981302300<![CDATA[Cramer–Rao Bound for Noncoherent Direction of Arrival Estimation in the Presence of Sensor Location Errors]]>24913031307187