2 Citations (Scopus)


The paper addresses the transient change detection (TCD) problem, assuming that the duration of change is finite. The TCD criterion minimizes the worst-case probability of missed detection among all tests with a prescribed worst-case probability of false alarm. We study the fixed sample size (FSS) test as a solution to the TCD problem. First, the operating characteristics of the FSS test have been established for arbitrary pre- and post-change distributions. Next, a numerical method of the sample (block) size optimization has been considered for three particular log-likelihood ratio distributions, i.e., Gaussian, $\chi ^{2}$ and exponential. Moreover, simple asymptotic equations for the optimal operating characteristics and block size have been proposed in the Gaussian case. Numerical results are provided to confirm the theoretical findings for the above-mentioned distributions. The accuracy and sharpness of the asymptotic analytical equation is analyzed in the Gaussian case. Finally, the FSS test is compared to the finite moving average (FMA) test obtained by optimizing the CUSUM-type test with respect to the TCD optimality criterion for the above-mentioned distributions. The application of the FSS and FMA tests to the radio-navigation integrity monitoring is also considered.

Original languageEnglish
Pages (from-to)1418-1433
Number of pages16
JournalIEEE Transactions on Signal Processing
Publication statusPublished - 9 Mar 2022


  • Neyman-Pearson test
  • Transient change detection
  • finite moving average
  • fixed sample size test
  • hypothesis testing


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