Practical metrics for performance evaluation of estimation algorithms are discussed. A variety of metrics useful for evaluating various aspects of the performance of an estimation algorithm is introduced and justified. They can be classified in two different ways: 1) absolute error measures (without a reference), relative error measures (with a reference), or frequency counts (of some events), and 2) optimistic (i.e., how good the performance is), pessimistic (i.e., how bad the performance is), or balanced (neither optimistic nor pessimistic). Pros and cons of these metrics and the widely-used RMS error are explained. The paper advocates replacing the RMS error in many cases by a measure called average Euclidean error
Evaluation of estimation algorithms part I: incomprehensive measures of performance
IEEE Transactions on Aerospace and Electronic Systems ; 42 , 4 ; 1340-1358
2006-10-01
3626198 byte
Article (Journal)
Electronic Resource
English
Evaluation of Estimation Algorithms Part I: Incomprehensive Measures of Performance
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