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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.
This chapter discusses different methods for multiple source localization and presents a comparison among them.
This paper proposes an efficient approach to the multiple source localization and contour mapping problem of radiation fields using Unmanned Aerial Vehicles (UAVs).
Multiple source localization.
Application Case 1 (Multiple source localization (MSL) In MSL, the goal is to l on the signals they emit.
With this regard, multiple source localization was addressed in the past years under AMI and AMIDA EC projects for diarization tasks (http://www.amiproject.org/).org/
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It also presents the mathematical model of the method based on steered response power - phase transform (SRP-PHAT) algorithm for multiple sources localization under reverberant conditions.
Figure 4 Multiple sources localization results: (a) Concurrent estimates of two sources located at (4, 3) and (12, 10) and (b) the corresponding evolution of the attenuation model parameters.
However, in certain application cases, unique identification is either inherently impossible, such as in multiple-source localization, or infeasible because of application-specific constraints.
Corresponding application cases include multiple-source localization, in which the agents do not have identification sequences at all, and scenarios in which it is infeasible to send sufficiently long identification sequences, e.g., for highly resource-limited agents.
Moreover, the framework developed in this work can also be extended, for example, to the multiple-source localization problem, which can be regarded as a special case of the problem at hand in which all sources have the same transmit ID.
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