<![CDATA[ IEEE Transactions on Signal Processing - new TOC ]]>
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TOC Alert for Publication# 78 2017April 20<![CDATA[Invariant Adaptive Detection of Range-Spread Targets Under Structured Noise Covariance]]>651230483061787<![CDATA[Proximity Without Consensus in Online Multiagent Optimization]]>651230623077883<![CDATA[Asymptotically Locally Optimal Weight Vector Design for a Tighter Correlation Lower Bound of Quasi-Complementary Sequence Sets]]>$mathbf {w}$ and is expressed in terms of three additional parameters associated with QCSS: the set size $K$ , the number of channels $M$, and the sequence length $N$. It is known that a tighter GLB (compared to the Welch bound) is possible only if the condition $Mgeq 2$ and $Kgeq overline{K}+1$, where $overline{K}$ is a certain function of $M$ and $N$, is satisfied. A challenging research problem is to determine if there exists a weight vector that gives rise to a tighter GLB for all (not just some) $Kgeq overline{K}+1$ and $Mgeq 2$, especially for large $N$, i.e., the condition is asymptotically both necessary and s-
fficient. To achieve this, we analytically optimize the GLB which is (in general) nonconvex as the numerator term is an indefinite quadratic function of the weight vector. Our key idea is to apply the frequency domain decomposition of the circulant matrix (in the numerator term) to convert the nonconvex problem into a convex one. Following this optimization approach, we derive a new weight vector meeting the aforementioned objective and prove that it is a local minimizer of the GLB under certain conditions.]]>651231073119700