Iterative Recovery Algorithms in Compressed Sensing

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Resource Overview

Iterative recovery algorithms in compressed sensing, a variation of matching pursuit methods, with focus on the CoSaMP sparse recovery algorithm implementation.

Detailed Documentation

In the field of compressed sensing, iterative recovery algorithms represent a widely adopted approach, with matching pursuit serving as one significant variation. Among similar sparse recovery algorithms, the CoSaMP (Compressive Sampling Matching Pursuit) algorithm deserves particular attention. The CoSaMP algorithm achieves high sparse recovery accuracy while simultaneously improving computational efficiency. These algorithms provide practical methodologies and innovative approaches for signal processing research through implementations that typically involve iterative support detection, signal estimation, and residual updates. Key implementation aspects include atom selection based on correlation metrics, orthogonal projection for signal approximation, and dynamic support set maintenance through multiple iterations.

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