Multiple acoustic source localization based on multiple hypotheses testing using particle approach

Lee, Yeongseon; Wada, Ted S.; Juang, Biing‐Hwang

doi:10.1109/icassp.2010.5496229

Cited by 11 publications

(11 citation statements)

References 6 publications

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“…The MSL problem has been under study for several years, e.g., in [6][7][8]. However, neither a thorough graphtheoretical analysis nor a formulation of a minimum mean square error (MMSE)-optimal or maximum likelihood (ML)-optimal algorithm has yet been presented.…”

Section: Related Workmentioning

confidence: 99%

Localization of ambiguously identifiable wireless agents: complexity analysis and efficient algorithms

Schlupkothen

Ascheid

2018

EURASIP J. Adv. Signal Process.

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In the localization of wireless agents, ambiguous measurements have significant implications regarding the complexity and quality of the agents' positioning. Ambiguous measurements occur, for example, in multiple source localization (MSL), in which the goal is to localize the sources of signals, although the signals themselves cannot be used to differentiate among their sources. The indifferentiability of the sources results in a combinatorial optimization problem that must be solved before a localization result can be obtained. Similar effects arise, for example, in the localization of highly resource-limited wireless agents that are subject to severe size and energy constraints, meaning that neither unique identification sequences (CDMA) nor unique frequency or time resources (FDMA, TMDA) can be used. This application scenario constitutes a more general and complex joint problem of localization and ambiguity resolution that also encompasses MSL. In this work, we focus on this more general problem and its corresponding application case while maintaining applicability to the MSL problem. More precisely, we prove the N P-hardness of the joint localization and ambiguity resolution problem and derive a solution framework that facilitates a comprehensive and concise formulation thereof. Thereby, we derive a minimum mean square error (MMSE)-optimal algorithm based on mixed-integer nonlinear programming and propose a relaxation of the problem with the aim of reducing the computational complexity. Additionally, simplifications are derived for the case in which bidirectional measurements are available or enforced, e.g., by the applied communication or ranging protocol.

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Section: Related Workmentioning

confidence: 99%

Localization of ambiguously identifiable wireless agents: complexity analysis and efficient algorithms

Schlupkothen

Ascheid

2018

EURASIP J. Adv. Signal Process.

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“…. , m} that results in the greatest increase in the overall measurement likelihood; where δ k is the full Newton step (19) and m is a pre-specified positive integer;…”

Section: Implementation -Gauss-newton Approach With An Adaptive Step mentioning

confidence: 99%

“…Furthermore, unlike existing approaches (e.g. [2,10,19,20]), the TALA does not require measurements to be of a specific type (i.e. TOA measurements) and can be implemented for systems composed of either homogeneous or heterogeneous sensors.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these papers have addressed simplified scenarios with either perfect measurements [17,18] or a known number of emitters [13]. The remaining papers have developed techniques specifically for a TOA measurement model [2,10,19,20], and these approaches cannot be easily modified to deal with other measurement models (e.g. hybrid AOA/TOA measurements).…”

Section: Introductionmentioning

confidence: 99%

“…To-date, only a small number of papers have considered the problem of localising multiple emitters [2,10,13,[17][18][19][20]. Some of these papers have addressed simplified scenarios with either perfect measurements [17,18] or a known number of emitters [13].…”

Section: Introductionmentioning

confidence: 99%

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Novel maximum likelihood approach for passive detection and localisation of multiple emitters

Hernandez¹

2017

EURASIP J. Adv. Signal Process.

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In this paper, a novel target acquisition and localisation algorithm (TALA) is introduced that offers a capability for detecting and localising multiple targets using the intermittent "signals-of-opportunity" (e.g. acoustic impulses or radio frequency transmissions) they generate. The TALA is a batch estimator that addresses the complex multi-sensor/multi-target data association problem in order to estimate the locations of an unknown number of targets. The TALA is unique in that it does not require measurements to be of a specific type, and can be implemented for systems composed of either homogeneous or heterogeneous sensors. The performance of the TALA is demonstrated in simulated scenarios with a network of 20 sensors and up to 10 targets. The sensors generate angle-of-arrival (AOA), time-of-arrival (TOA), or hybrid AOA/TOA measurements. It is shown that the TALA is able to successfully detect 83-99% of the targets, with a negligible number of false targets declared. Furthermore, the localisation errors of the TALA are typically within 10% of the errors generated by a "genie" algorithm that is given the correct measurement-to-target associations. The TALA also performs well in comparison with an optimistic Cramér-Rao lower bound, with typical differences in performance of 10-20%, and differences in performance of 40-50% in the most difficult scenarios considered. The computational expense of the TALA is also controllable, which allows the TALA to maintain computational feasibility even in the most challenging scenarios considered. This allows the approach to be implemented in time-critical scenarios, such as in the localisation of artillery firing events. It is concluded that the TALA provides a powerful situational awareness aid for passive surveillance operations.

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The EVALITA Dependency Parsing Task: From 2007 to 2011

Bosco

Mazzei

2013

Lecture Notes in Computer Science

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Multiple acoustic source localization based on multiple hypotheses testing using particle approach

Cited by 11 publications

References 6 publications

Localization of ambiguously identifiable wireless agents: complexity analysis and efficient algorithms

Localization of ambiguously identifiable wireless agents: complexity analysis and efficient algorithms

Novel maximum likelihood approach for passive detection and localisation of multiple emitters

The EVALITA Dependency Parsing Task: From 2007 to 2011

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