2020
DOI: 10.1177/1550147720940204
|View full text |Cite
|
Sign up to set email alerts
|

Decision fusion for composite hypothesis testing in wireless sensor networks over a shared and noisy collision channel

Abstract: We consider the composite hypothesis testing problem of time-bandwidth-constrained distributed detection. In this scenario, the probability distribution of the observed signal when the event of interest is happening is unknown. In addition, local decisions are censored and only those uncensored local decisions will be sent to the fusion center over a shared and noisy collision channel. The fusion center also has a limited time duration to collect transmitted decisions and make a final decision. Two ty… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
5
0

Year Published

2020
2020
2022
2022

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(5 citation statements)
references
References 38 publications
0
5
0
Order By: Relevance
“…Finally, the final extraction result was obtained by decision fusion. Decision fusion is generally achieved through algebraic operations such as maximum, mean and majority vote (MV) [23][24][25]. We adopt the MV method for decision fusion.…”
Section: Methodsmentioning
confidence: 99%
“…Finally, the final extraction result was obtained by decision fusion. Decision fusion is generally achieved through algebraic operations such as maximum, mean and majority vote (MV) [23][24][25]. We adopt the MV method for decision fusion.…”
Section: Methodsmentioning
confidence: 99%
“…First, the design complexity of the quantizer thresholds grows exponentially [13], [14] and, second, the sensor LR cannot be evaluated due to unknown target parameters [14]. Hence, the bit reported is either the outcome of a raw-measurement quantization [15], [16], [17] or represents the inferred binary-valued event (via sub-optimal detection statistics [18]).…”
Section: Phenomenon Of Interest (Poi)mentioning
confidence: 99%
“…The uncooperative-target case has been recently analyzed also in an online setup with a sequential version of the above fusion (one-bit) rule [27]. Furthermore, [28], [31] have applied the Rao test to collision-aware reporting for fusion design. Finally, locally most-powerful tests have been applied to decentralized detection of sparse signals in (generalized) Gaussian noise [29], [30].…”
Section: B Related Workmentioning
confidence: 99%