Efficient Multidimensional Harmonic Retrieval: A Hierarchical Signal Separation Framework

This paper presents a low-complexity one-dimensional (1-D) Unitary Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)-based algorithm for multidimensional harmonic retrieval (MHR) problems based on an HIerarchical Signal Separation (HISS) technique, which interleaves the parameter estimation and filtering processes. The filtering process not only progressively partitions the signals with close parameters into separate groups, but also reduces the power of the additive noise, both of which entail higher parameter estimation accuracy. The pairing of the estimated parameters is also automatically achieved. Simulations show that the new algorithm provides satisfactory performance compared with previous works but with drastically reduced computations.