Decentralized Subspace Tracking via Gossiping

We consider a fully decentralized model for adaptively tracking the signal’s principal subspace, which arises in multi-sensor array detection and estimation problems. Our objective is to equip the network of dispersed sensors with a primitive for online spectrum sensing, which does not require a central fusion node. In this model, each node updates its local subspace estimate with its received data and a weighted average of the neighbors’ data. The quality of the estimate is measured by the total subspace mismatch of the individual subspace component estimates, which converge asymptotically in the Lyapunov sense.

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