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Rectified ALS Algorithm for Multidimensional Harmonic Retrieval

Abstract : MultiDimensional (MD) Harmonic Retrieval is a challenging multi-parameter estimation problem and is useful for a plethora of operational applications as for instance channel sounding or MIMO radar processing. The MD-harmonic model follows a structured Canonical Polyadic Decomposition (CPD) in the sense that the factors of the CPD are Vandermonde. A standard and popular estimation scheme to derive the CPD is the Alternating Least Squares (ALS) algorithm. Unfortunately, the ALS algorithm does not exploit the a priori known factor structure , which considerably degrades the estimation performance. In this work, a modified ALS-type algorithm is proposed. This new algorithm, called Rectified ALS (RecALS), is able to take into account the Vandermonde structure of the factors. The RecALS algorithm belongs to the Lift-and-Project family and exploits iterated projections on the set of Toeplitz rank-1 matrices. It exhibits a fast convergence and is very accurate in the sense that its Mean Square Error (MSE) is close to the Cramér-Rao Bound for a wide range of Signal to Noise Ratio (SNR).
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Contributor : Remy Boyer <>
Submitted on : Wednesday, May 11, 2016 - 6:04:21 PM
Last modification on : Wednesday, October 14, 2020 - 1:56:03 PM
Long-term archiving on: : Wednesday, November 16, 2016 - 1:54:31 AM


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Rémy Boyer, Pierre Comon. Rectified ALS Algorithm for Multidimensional Harmonic Retrieval. 9th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2016), Jul 2016, Rio de Janeiro, Brazil. ⟨10.1109/sam.2016.7569678⟩. ⟨hal-01314702⟩



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