Poisson Point Processes

Streit, Roy L.

doi:10.1007/978-1-4419-6923-1

Cited by 195 publications

(51 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is convenient to assume that Nt is Poisson distributed and the speaker locations are Independent and Identically Distributed (i.i.d.). Under these assumptions, (3) is a Poisson Point Processs (PPPs) [21], and the joint posterior pdf of ⇠ t is fully described by the density of Xt, also known as the intensity function [21]. Therefore, it is sufficient to derive an estimator of the intensity function in order to estimate the pdf.…”

Section: Tracking Frameworkmentioning

confidence: 99%

“…To account for missing detections of speakers, a Bernoulli thinning process [21] is applied to the predictions that accepts Xt with probability of detection, p d , and rejects the state with probability,…”

Section: B Speaker Detection and Missing Detectionsmentioning

confidence: 99%

“…However, the resulting process is not a PPP, such that the intensity recursion is not analytically tractable [21]. Nonetheless, the process can be approximated by its first-order moment, resulting in the PHD filter with prediction in (5) and update [12], [18]:…”

Section: Speaker Update and Cluttermentioning

confidence: 99%

“…The partition function ensures that the GM components are weighted correctly such that the expected value of the number of speakers, E[Nt], is given by [21] …”

Section: Estimated Number Of Speakersmentioning

confidence: 99%

See 3 more Smart Citations

Bearing-only acoustic tracking of moving speakers for robot audition

Evers

Moore

Naylor

et al. 2015

2015 IEEE International Conference on Digital Signal Processing (DSP)

View full text Add to dashboard Cite

Abstract-This paper focuses on speaker tracking in robot audition for human-robot interaction. Using only acoustic signals, speaker tracking in enclosed spaces is subject to missing detections and spurious clutter measurements due to speech inactivity, reverberation and interference. Furthermore, many acoustic localization approaches estimate speaker direction, hence providing bearing-only measurements without range information. This paper presents a probability hypothesis density (PHD) tracker that augments the bearing-only speaker directions of arrival with a cloud of range hypotheses at speaker initiation and propagates the random variates through time. Furthermore, due to their formulation PHD filters explicitly model, and hence provide robustness against, clutter and missing detections. The approach is verified using experimental results.

show abstract

Section: Tracking Frameworkmentioning

confidence: 99%

Section: B Speaker Detection and Missing Detectionsmentioning

confidence: 99%

Section: Speaker Update and Cluttermentioning

confidence: 99%

“…The partition function ensures that the GM components are weighted correctly such that the expected value of the number of speakers, E[Nt], is given by [21] …”

Section: Estimated Number Of Speakersmentioning

confidence: 99%

See 2 more Smart Citations

Bearing-only acoustic tracking of moving speakers for robot audition

Evers

Moore

Naylor

et al. 2015

2015 IEEE International Conference on Digital Signal Processing (DSP)

View full text Add to dashboard Cite

show abstract

“…PU activity is notable for having peaks and troughs determining BUSY and IDLE behaviors and statuses in the burst of transmission; in this regard, statistical and probabilistic predictor models have been presented in existing articles on the web that attempt to describe with greater assertiveness the PU's performance in the spectrum at the present time, to attempt to describe their future behavior; these proposals are based on methodologies such as Markov chains [12], Poisson processes [13], time series [14], so that the functional effect provided by these models are occupation or non-occupation probabilities of the channel, which predicts trends and patterns of behavior, to finally estimate the future statuses of PU, which will yield increased CR performance, better use of the channel and efficient data transmission for heterogeneous users without interference to the PUs. However, while it is true that there are multiple ways to characterize channels, this is still an open research line as the characterization of the spectrum from the practical point of view requires methodologies that are truly intelligent with self-learning capacity and thus have a radio that fits the varying …”

Section: Abstract-the Current Shortage and Inefficient Use Of The Frementioning

confidence: 99%

User Characterization through Dynamic Bayesian Networks in Cognitive Radio Wireless Networks

Sarmiento¹,

Ordóñez²,

Rivas³

2016

IJET

View full text Add to dashboard Cite

Abstract-The current shortage and inefficient use of the frequency spectrum lead researchers to seek technological solutions to this problem [1], thus Cognitive Radio (CR)[2] is proposed, allowing a more efficient management of the existing resources so they can be exploited opportunistically by cognitive users. This paper presents the design and use of a Bayesian network for the characterization of the primary user (PU) in wireless networks (GSM 824.9 MHz) in order to generate a PU activity predictor, which could serve to the central entity of a cognitive network in making spectral decisions. From the results found, it is concluded that the artificial intelligence technique based on Bayesian networks allows to model and predict the behavior of the primary user above 80% for short future lapses of time. Keyword -Cognitive radio, primary user, Bayesian networks, prediction, characterization.I. INTRODUCTION Bayesian networks that are part of artificial intelligence [3], [4] are a graphical representation of nodes interconnected cyclically with each other. The nodes represent a set of variables that forms a system, and which are represented by percentages of probability. The edges represent conditional dependencies between nodes thus being able to have a parent and a son, which concludes in a topology or network structure. This model allows an inference estimating the probability a-posteriori of consulted variables from the a-priori probability of the observed variables, resulting in the classification, prediction and diagnosis of variables of the modeling system. Several research papers [5], [6], [7], [8], [9] reinforce the intention of combining cognitive radio with AI, to apply their methods and techniques in different areas of knowledge and works such as signal processing required for DSA (Dynamic spectrum Access)networks, techniques to improve DSS (Dynamic spectrum sharing)spectrum utilization, spectrum sensing techniques, etc. A CR device interacts with its environment and modifies its functional parameters (auto-reconfiguration) with cognitive ability to meet the quality of service (QoS) required by cognitive users; thus, the CR can make use of artificial intelligence as an intelligent and intuitive form [3] necessary for future estimation of spectral holes, from the behavior of the PUs in the channel.The features of cognitive radio depend on the behavior and activities of licensed users (PU) [10], so that nonlicensed users (SU) should identify the spectrum gaps to seize the opportunities of transmission in order to maximize the use of the band, hence the CR can use it without affecting the transmission of the PU. So the accurate prediction or modeling of PU activities becomes necessary, leading to an efficient use of the spectrum for cognitive users [11] without affecting primary node performance.PU activity is notable for having peaks and troughs determining BUSY and IDLE behaviors and statuses in the burst of transmission; in this regard, statistical and probabilistic predictor models have been presented ...

show abstract