Robust Sequential Testing of Multiple Hypotheses in Distributed Sensor Networks

Leonard, Mark R.; Stiefel, Maximilian; Faub, Michael; Zoubir, Abdelhak M.

doi:10.1109/icassp.2018.8461895

Cited by 10 publications

(4 citation statements)

References 9 publications

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“…In [51]- [53], we successfully proposed to use robust estimators to robustify sequential detectors for distributed sensor networks. We applied the same concept to the distributed D-PPHDF.…”

Section: Simulation Ii: Resultsmentioning

confidence: 99%

Multi-target tracking in distributed sensor networks using particle PHD filters

Leonard

Zoubir

2019

Signal Processing

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Multi-target tracking is an important problem in civilian and military applications. This paper investigates multi-target tracking in distributed sensor networks. Data association, which arises particularly in multi-object scenarios, can be tackled by various solutions. We consider sequential Monte Carlo implementations of the Probability Hypothesis Density (PHD) filter based on random finite sets. This approach circumvents the data association issue by jointly estimating all targets in the region of interest.To this end, we develop the Diffusion Particle PHD Filter (D-PPHDF) as well as a centralized version, called the Multi-Sensor Particle PHD Filter (MS-PPHDF). Their performance is evaluated in terms of the Optimal Subpattern Assignment (OSPA) metric, benchmarked against a distributed extension of the Posterior Cramér-Rao Lower Bound (PCRLB), and compared to the performance of an existing distributed PHD Particle Filter. Furthermore, the robustness of the proposed tracking algorithms against outliers and their performance with respect to different amounts of clutter is investigated.

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“…In [51]- [53], we successfully proposed to use robust estimators to robustify sequential detectors for distributed sensor networks. We applied the same concept to the distributed D-PPHDF.…”

Section: Simulation Ii: Resultsmentioning

confidence: 99%

Multi-target tracking in distributed sensor networks using particle PHD filters

Leonard

Zoubir

2019

Signal Processing

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“…This kind of procedure is known as mulitple testing or mulitple comparison and has been studied extensively in the literature; see [171]- [179] and the references therein. A particularly attractive implementation of a detector based on this approach is the sequential matrix likelihood/probability ratio test [180], whose robust version is based on separate pairs of least favorable distributions for each pairwise test [181], [182]. Sequential tests will be revisited in more detail shortly.…”

Section: B Characterizing Least Favorable Distributionsmentioning

confidence: 99%

Minimax Robust Detection: Classic Results and Recent Advances

Fauß

Zoubir

Poor

2021

IEEE Trans. Signal Process.

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This paper provides an overview of results and concepts in minimax robust hypothesis testing for two and multiple hypotheses. It starts with an introduction to the subject, highlighting its connection to other areas of robust statistics and giving a brief recount of the most prominent developments. Subsequently, the minimax principle is introduced and its strengths and limitations are discussed. The first part of the paper focuses on the two-hypothesis case. After briefly reviewing the basics of statistical hypothesis testing, uncertainty sets are introduced as a generic way of modeling distributional uncertainty. The design of minimax detectors is then shown to reduce to the problem of determining a pair of least favorable distributions, and different criteria for their characterization are discussed. Explicit expressions are given for least favorable distributions under three types of uncertainty: ε-contamination, probability density bands, and f -divergence balls. Using examples, it is shown how the properties of these least favorable distributions translate to properties of the corresponding minimax detectors. The second part of the paper deals with the problem of robustly testing multiple hypotheses, starting with a discussion of why this is fundamentally different from the binary problem. Sequential detection is then introduced as a technique that enables the design of strictly minimax optimal tests in the multi-hypothesis case. Finally, the usefulness of robust detectors in practice is showcased using the example of ground penetrating radar. The paper concludes with an outlook on robust detection beyond the minimax principle and a brief summary of the presented material.

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“…Hypothesis testing [30], [31], which can be considered as a promising solution for solving the main technical challenges of applying ML-based methods for radio map construction, is classified into Bayesian hypothesis testing (BHT) [32]- [35] and statistical hypothesis testing (SHT) [36]- [39]. In BHT, it is necessary to model the prior distribution based on a priori information.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Radio Map Using Statistical Hypothesis Testing

Katagiri

Sato

Inage

et al. 2021

IEEE Trans. Cogn. Commun. Netw.

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Private fifth-generation mobile communication networks (5G) have garnered considerable attention for providing various communication services. In private 5G systems, such as those employed in smart factories, it is necessary to appropriately manage the interference power among diverse systems because each nonpublic operator deploys a gNodeB in the local area and utilizes a similar allocated frequency. Several researchers have discussed the use of a radio map, which stores the spatial distribution of received signal power values as a tool for managing intra-system interference. Radio maps are typically applied to television white space, where the location of the base station (BS) is fixed and the average value of the received signal power exhibits time-invariant characteristics. However, in private 5G networks, the estimation accuracy using a conventional radio map is lowered because the location of the BS may be changed when the factory is rearranged. In this study, a database server is used to infer a significant difference in the average values of the received signal power by means of Welch's -test; the server then updates the radio map based on the test results. The simulation results indicate that the proposed method can detect changes in the BS location and update the radio map accurately.

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Robust Sequential Testing of Multiple Hypotheses in Distributed Sensor Networks

Cited by 10 publications

References 9 publications

Multi-target tracking in distributed sensor networks using particle PHD filters

Multi-target tracking in distributed sensor networks using particle PHD filters

Minimax Robust Detection: Classic Results and Recent Advances

Dynamic Radio Map Using Statistical Hypothesis Testing

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