Cyclic Cramér-Rao-type bounds for periodic parameter estimation

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

Cyclic lower bounds establish fundamental limits in parametric statistical models with a periodic nature. Non-Bayesian lower bounds on the mean-cyclic-error (MCE) of any cyclic-unbiased estimator, in the Lehmann sense, are useful for performance analysis and system design in periodic estimation. In this paper, we derive two cyclic versions of the Cramér-Rao bound (CRB) based on Mardia's circular information inequality. The proposed cyclic CRBs are lower bounds on the MCE of any cyclic-unbiased estimator. One of these bounds has been recently developed by using the cyclic Hammersley-Chapman-Robbins lower bounds on the MCE. The derivations presented in this paper relates between the cyclic CRB and existing results from directional statistics on manifolds. The properties of the proposed cyclic CRBs are examined. In particular, it is shown that the cyclic CRBs are always lower than the convectional CRB and that the two cyclic CRBs are asymptotically achievable by the performance of the maximum likelihood (ML) estimator. The proposed cyclic CRBs and the performance of the ML estimator are compared in terms of MCE in estimation of the mean of von Mises distributed measurements and for phase estimation with additive white Gaussian noise (AWGN).

اللغة الأصليةإنجليزيّة أمريكيّة
عنوان منشور المضيفFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
الصفحات1797-1804
عدد الصفحات8
رقم المعيار الدولي للكتب (الإلكتروني)9780996452748
حالة النشرنُشِر - 1 أغسطس 2016
الحدث19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, ألمانيا
المدة: ٥ يوليو ٢٠١٦٨ يوليو ٢٠١٦

سلسلة المنشورات

الاسمFUSION 2016 - 19th International Conference on Information Fusion, Proceedings

!!Conference

!!Conference19th International Conference on Information Fusion, FUSION 2016
الدولة/الإقليمألمانيا
المدينةHeidelberg
المدة٥/٠٧/١٦٨/٠٧/١٦

All Science Journal Classification (ASJC) codes

  • !!Statistics, Probability and Uncertainty
  • !!Computer Science Applications
  • !!Computer Vision and Pattern Recognition
  • !!Signal Processing

بصمة

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