Mean-squared-error prediction for bayesian direction-of-arrival estimation

Joshua M. Kantor, Christ Richmond, Daniel Bliss, Bill Correll

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


In this article, we study the mean-squared-error performance of Bayesian direction-of-arrival (DOA) estimation in which prior belief about the target location is incorporated into the estimation process. Our primary result is an extension of the method of interval errors (MIE) to the case of maximum a posteriori (MAP) direction-of-arrival estimation. We work in a general framework in which the prior information used in the MAP estimation may not match the actual target distribution. In particular, when the prior is incorrect, the MAP estimator degrades relative to the performance of a MAP estimator with the correct prior. Our methods are able to accurately predict the performance of a MAP estimator in this more general situation. We apply our methods to investigate the sensitivity of MAP direction-of-arrival estimation to mismatches between the chosen prior and the actual angular distribution of the target.

Original languageEnglish (US)
Article number6566187
Pages (from-to)4729-4739
Number of pages11
JournalIEEE Transactions on Signal Processing
Issue number19
StatePublished - 2013


  • Bayesian
  • MAP
  • direction-of-arrival
  • estimation
  • maximum a posteriori
  • mean-squared error (MSE)
  • method of interval errors
  • method of interval errors (MIE)
  • saddlepoint

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Mean-squared-error prediction for bayesian direction-of-arrival estimation'. Together they form a unique fingerprint.

Cite this