Open AccessDynamic error spectrum for estimation performance evaluation: a case study on interacting multiple model algorithm
Author(s) -
Mao Yanhui,
Han Chongzhao,
Duan Zhansheng
Publication year - 2014
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2013.0134
Subject(s) - estimator , measure (data warehouse) , metric (unit) , algorithm , computer science , mean squared error , spectrum (functional analysis) , variety (cybernetics) , performance metric , mathematical optimization , mathematics , artificial intelligence , statistics , data mining , operations management , physics , management , quantum mechanics , economics
The commonly used root‐mean‐square error for estimation performance evaluation is easily dominated by large error terms. So many new alternative absolute metrics have been provided in X. R. Li's work. However, each of these metrics only reflects one narrow aspect of estimation performance, respectively. A comprehensive measure, error spectrum, was presented aggregating all these incomprehensive measures. However, when being applied to dynamic systems, this measure will have three dimensions over the total time span, which is not intuitive and difficult to be analysed. To overcome its drawbacks, a new metric, dynamic error spectrum (DES), is proposed in this study to extend the error spectrum measure to dynamic systems. Three forms under different application backgrounds are given, one of which is balanced taking into account both good and bad behaviour of an estimator and so can provide more impartial evaluation results. It can be applied to a variety of dynamic systems directly. Then the challenge in performance evaluation of the interacting multiple model (IMM) algorithm is considered, and the IMM algorithm is chosen as the testing case to illustrate the superiority of the DES metric. The simulation results validate its utility and effectiveness.