Distributed predictive subspace pursuit

In a compressed sensing setup with jointly sparse, correlated data, we develop a distributed greedy algorithm called distributed predictive subspace pursuit. Based on estimates from neighboring sensor nodes, this algorithm operates iteratively in two steps: first forming a prediction of the signal and then solving the compressed sensing problem with an iterative linear minimum mean squared estimator. Through simulations we show that the algorithm provides better performance than current state-of-the-art algorithms.