DETECTION OF A HARMONIC SIGNAL AGAINST THE BACKGROUND OF A NONSTATIONARY GAUSSIANINTERFERENCE WITH COMPLEX SPECTRUM

Background. Modern radar stations for various purposes operate in the conditions of interference created by the imprints of the radar signal from the background surface, from metrological formations (precipitation, clouds, etc.) and artificial radiation sources. Ensuring the operation of the radar in such difficult conditions requires the construction of adaptive signal processing algorithms that have high efficiency and maintain them when changing signal-to-noise situations. Objective. The purpose of the paper is creation of an adaptive algorithm for detecting a harmonic signal against the background of spatially correlated interference and estimating its parameters. Methods. Construction of a two-dimensional autoregressive model of a mixture of correlated spatial noise and harmonic signal and application of the empirical Bayesian approach to the synthesis of an adaptive algorithm for detecting and evaluating signal and noise parameters. Results. A two-dimensional adaptive space-time algorithm for detecting a radar signal reflected from a moving target against the background of a space-correlated interference is synthesized. The analysis of the efficiency of the algorithm by the Monte Carlo method is carried out. Conclusions. It is shown that the empirical Bayesian approach is an effective working methodology in solving the problem of detecting a harmonic signal and estimating its parameters under conditions of interference with a complex frequency spectrum under different conditions of a priori uncertainty of their parameters.

An empirical Bayesian approach to incorporate directional movement information from a forage fish into the Arnason-Schwarz mark-recapture model

Background Over the past two decades, various species of forage fish have been successfully implanted with miniaturized acoustic transmitters and subsequently monitored using stationary acoustic receivers. When acoustic receivers are configured in an array, information related to fish direction can potentially be determined, depending upon the number and relative orientation of the acoustic receivers. However, it can be difficult to incorporate directional information into frequentist mark-recapture methods. Here we show how an empirical Bayesian approach can be used to develop a model that incorporates directional movement information into the Arnason-Schwarz modeling framework to describe survival and migration patterns of a Pacific herring (Clupea pallasii) population in coastal Alaska, USA. Methods We acoustic-tagged 326 adult Pacific herring during April 2017 and 2018 while on their spawning grounds in Prince William Sound Alaska, USA. To monitor their movements, stationary acoustic receivers were deployed at strategic locations throughout the Sound. Receivers located at the major entrances to the Gulf of Alaska were arranged in parallel arrays to determine the directional movements of the fish. Informative priors were used to incorporate the directional information recorded at the entrance arrays into the model. Results A seasonal migratory pattern was found at one of Prince William Sound’s major entrances to the Gulf of Alaska. At this entrance, fish tended to enter the Gulf of Alaska during spring and summer after spawning and return to Prince William Sound during the fall and winter. Fish mortality was higher during spring and summer than fall and winter in both Prince William Sound and the Gulf of Alaska. Conclusions An empirical Bayesian modeling approach can be used to extend the Arnason-Schwarz modeling framework to incorporate directional information from acoustic arrays to estimate survival and characterize the timing and direction of migratory movements of forage fish.

An empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects

We are motivated by biological studies intended to understand global gene expression fold change. Biologists have generally adopted a fixed cutoff to determine the significance of fold changes in gene expression studies (e.g. by using an observed fold change equal to two as a fixed threshold). Scientists can also use a t-test or a modified differential expression test to assess the significance of fold changes. However, these methods either fail to take advantage of the high dimensionality of gene expression data or fail to test fold change directly. Our research develops a new empirical Bayesian approach to substantially improve the power and accuracy of fold-change detection. Specifically, we more accurately estimate gene-wise error variation in the log of fold change. We then adopt a t-test with adjusted degrees of freedom for significance assessment. We apply our method to a dosage study in Arabidopsis and a Down syndrome study in humans to illustrate the utility of our approach. We also present a simulation study based on real datasets to demonstrate the accuracy of our method relative to error variance estimation and power in fold-change detection. Our developed R package with a detailed user manual is publicly available on GitHub at https://github.com/cuiyingbeicheng/Foldseq.

An empirical Bayesian approach for estimating fleet- and vessel-level bycatch rates in fisheries with effort heterogeneity and limited data: a prospective tool for measuring bycatch mitigation performance

Minimizing fishing-induced mortality on bycatch and endangered, threatened or protected species is a necessity for fisheries managers. Estimating individual vessel bycatch rates by dividing the amount of bycatch by effort (nominal rate) can be biased, as it does not consider effort heterogeneity within the fleet and ignores prior knowledge of fleet bycatch rates. We develop an empirical Bayesian approach for estimating individual vessel and fleet bycatch rates that: (i) considers effort heterogeneity among vessels and; (ii) pools data from similar vessels for more accurate estimation. The proposed standardized bycatch rate of a vessel is, therefore, the weighted average of the pool rate and nominal rate of the vessel; where the weights are functions of the vessel’s fishing effort and a constant estimated from the model. We apply this inference method to the estimation of seabird bycatch rates in the component of the Australian Eastern Tuna and Billfish Fishery targeting yellowfin tuna. We illustrate the capability of the method for providing fishery managers with insights on fleet-wide bycatch mitigation performance and the identification of outperforming and underperforming vessels. This method can also be used by fishery managers to develop fleet-wide performance measures or quantitative evaluation standards.