Distributed Detection Over Channels with Memory

Biswas, Nilanjan; Ray, Priyadip; Varshney, Pramod K.

doi:10.1109/lsp.2015.2495205

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Future directions of research will investigate DD via sensor fusion in more challenging and close-to-real contexts. These include multi-bit quantizers [35], robustness to phylayer attacks [36], time-correlated reporting channels [37], multidimensional measurement models [38], incompletelyspecified noise and model pdfs (e.g. unknown AAF), models enjoying sparsity [8] and energy-efficient censoring sensors.…”

Section: Discussionmentioning

confidence: 99%

Distributed Detection in Wireless Sensor Networks Under Multiplicative Fading via Generalized Score Tests

Ciuonzo

Rossi

Varshney

2021

IEEE Internet Things J.

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In this paper, we address the problem of distributed detection of a non-cooperative (unknown emitted signal) target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an (unknown) deterministic signal with attenuation depending on the unknown distance between the sensor and the target, multiplicative fading, and additive Gaussian noise. To model energy-constrained operations within Internet of Things (IoT), one-bit sensor measurement quantization is employed and two strategies for quantization are investigated. The Fusion Center (FC) receives sensor bits via noisy Binary Symmetric Channels (BSCs) and provides a more accurate global inference. Such a model leads to a test with nuisances (i.e. the target position xT ) observable only under H1 hypothesis. Davies' framework is exploited herein to design the generalized forms of Rao and Locally-Optimum Detection (LOD) tests. For our generalized Rao and LOD approaches, a heuristic approach for threshold-optimization is also proposed. Simulation results confirm the promising performance of our proposed approaches.

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Section: Discussionmentioning

confidence: 99%

Distributed Detection in Wireless Sensor Networks Under Multiplicative Fading via Generalized Score Tests

Ciuonzo

Rossi

Varshney

2021

IEEE Internet Things J.

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“…realistic) measurement and channel models is of clear interest, namely: (a) unknown random signal parameters [36], (b) vector measurement models [48], (c) incompletely specified noise PDFs (e.g. unknown variance), (d) models enjoying sparsity [24], [49], (e) energy-efficient censoring sensors [50] and (f ) time-correlated reporting channels [51]. Finally, generalizing the present work to detecting (and tracking) a target with timevarying velocity is also foreseen as an interesting future topic, e.g.…”

Section: Conclusion and Further Directionsmentioning

confidence: 99%

Multi-Bit & Sequential Decentralized Detection of a Noncooperative Moving Target Through a Generalized Rao Test

Cheng¹,

Ciuonzo²,

Rossi³

et al. 2021

IEEE Trans. on Signal and Inf. Process. over Networks

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We consider decentralized detection (DD) of an uncooperative moving target via wireless sensor networks (WSNs), measured in zero-mean unimodal noise. To address energy and bandwidth limitations, the sensors use multi-level quantizers. The encoded bits are then reported to a fusion center (FC) via binary symmetric channels. Herein, we propose a generalized Rao (G-Rao) test as a simpler alternative to the generalized likelihood ratio test (GLRT). Then, at the FC, a truncated one-sided sequential (TOS) test rule is considered in addition to the fixed-sample-size (FSS) manner. Further, the asymptotic performance of a trajectory-clairvoyant (multi-bit) Rao test is leveraged to develop an offline and per-sensor quantizer design. Detection gain measures are also introduced to assess resolution improvements. Simulations show the appeal of G-Rao test with respect to the GLRT, and the gain in detection by using multiple bits for quantization, as well as the advantage of the sequential detection approach.

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“…Further directions will design of Rao test for alternative, more general and realistic measurement & channel models: (a) unknown random signal parameters [26], (b) vector measurement [39], (c) incompletely specified noise PDFs (e.g. unknown variance [40]), (d) models enjoying sparsity [27], (e) energy-efficient censoring sensors [41] and (f ) time-correlated reporting channels [42]. Additionally, the validation of the proposed Rao fusion rule on experimental data, to assess the sensitivity to model mismatch, is of clear interest and left to future work.…”

Section: Conclusion and Further Directionsmentioning

confidence: 99%

“…where I w Kq +κ [∂ ln p w Kq +κ (ζ) /∂ζ)] 2 p w Kq +κ (ζ) latter term appears to have a very simple form in many cases of interest, such as w k ∼ N (0, σ 2 w ) (equal to 1/σ 2 w w k ∼ L(0, β) (equal to 1/β 2 ) and w k ∼ GN (0, α, ) (equal to (1/α 2 ) [ ( − 1) Γ(1 − 1/ )]/Γ(1/ )) [45]. Finally, combining (41) and (42), the FI can be written as in Eq. ( 12).…”

Section: Appendix B Proof Of Eq (12) (Fisher Information)mentioning

confidence: 99%

Multibit Decentralized Detection Through Fusing Smart and Dumb Sensors Based on Rao Test

Cheng

Ciuonzo

Rossi

2020

IEEE Trans. Aerosp. Electron. Syst.

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We consider Decentralized Detection (DD) of an unknown signal corrupted by zero-mean unimodal noise via Wireless Sensor Networks (WSNs). We assume the presence of both smart and dumb sensors: the former transmit unquantized measurements, while the latter employ multi-level quantizations (before transmission through binary symmetric channels) in order to cope with energy and/or bandwidth constraints. The data are received by a Fusion Center (FC), which relies on a proposed Rao test, as a simpler alternative to the Generalized Likelihood Ratio Test (GLRT). The asymptotic performance analysis of the multi-bit Rao test is provided and exploited to propose a (signalindependent) quantizer design approach by maximizing the noncentrality parameter of the test-statistic distribution. Since the latter is a non-linear and non-convex function of the quantization thresholds, we employ the particle swarm optimization algorithm for its maximization. Numerical results are provided to show the effectiveness of Rao test in comparison to GLRT and the boost in performance obtained by (multiple) threshold optimization. Asymptotic performance is also exploited to define detection gain measures allowing to assess gain arising from (a) use of dumb sensors and (b) increasing their quantization resolution.

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Distributed Detection Over Channels with Memory

Cited by 7 publications

References 21 publications

Distributed Detection in Wireless Sensor Networks Under Multiplicative Fading via Generalized Score Tests

Distributed Detection in Wireless Sensor Networks Under Multiplicative Fading via Generalized Score Tests

Multi-Bit & Sequential Decentralized Detection of a Noncooperative Moving Target Through a Generalized Rao Test

Multibit Decentralized Detection Through Fusing Smart and Dumb Sensors Based on Rao Test

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