Almost-sure identifiability of multidimensional harmonic retrieval

Tao Jiang, Nicholas D. Sidiropoulos, Jos M.F. Ten Berge

Research output: Contribution to journalArticlepeer-review

127 Scopus citations


Two-dimensional (2-D) and, more generally, multidimensional harmonic retrieval is of interest in a variety of applications, including transmitter localization and joint time and frequency offset estimation in wireless communications. The associated identifiability problem is key in understanding the fundamental limitations of parametric methods in terms of the number of harmonics that can be resolved for a given sample size. Consider a mixture of 2-D exponentials, each parameterized by amplitude, phase, and decay rate plus frequency in each dimension. Suppose that I equispaced samples are taken along one dimension and, likewise, J along the other dimension. We prove that if the number of exponentials is less than or equal to roughly I J/4, then, assuming sampling at the Nyquist rate or above, the parameterization is almost surely identifiable. This is significant because the best previously known achievable bound was roughly (I + J)/2. For example, consider I = J = 32; our result yields 256 versus 32 ide ntifiable exponentials. We also generalize the result to N dimensions, proving that the number of exponentials that can be resolved is proportional to total sample size.

Original languageEnglish (US)
Pages (from-to)1849-1859
Number of pages11
JournalIEEE Transactions on Signal Processing
Issue number9
StatePublished - Sep 2001

Bibliographical note

Funding Information:
Manuscript received November 14, 2000; revised May 7, 2001. This work was supported by NSF/CAREER 0096165 and NSF/Wireless 0096164. A partial summary of results appears in Proc. ICASSP, May 7–11, 2000, Salt Lake City, UT. The associate editor coordinating the review of this paper and approving it for publication was Dr. Kristine L. Bell.


  • Array signal processing
  • Frequency estimation
  • Harmonic analysis
  • Multidimensional signal processing
  • Spectral analysis


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