Abstract
The energy reading has been an efficient and attractive measure for collaborative acoustic source localization in practical application due to its cost saving in both energy and computation capability. The maximum likelihood problems by fusing received acoustic energy readings transmitted from local sensors are derived. Aiming to efficiently solve the nonconvex objective of the optimization problem, we present an approximate estimator of the original problem. Then, a direct norm relaxation and semidefinite relaxation, respectively, are utilized to derive the second-order cone programming, semidefinite programming or mixture of them for both cases of sensor self-location and source localization. Furthermore, by taking the colored energy reading noise into account, several minimax optimization problems are formulated, which are also relaxed via the direct norm relaxation and semidefinite relaxation respectively into convex optimization problems. Performance comparison with the existing acoustic energy-based source localization methods is given, where the results show the validity of our proposed methods.
Highlights
In recent years, with the advances in distributed and collaborative signal processing and communication, sensor networks have become an attractive system for various civil and military applications, especially in surveillance areas, such as environmental monitoring [1,2], traffic monitoring [3,4], source detection, localization, and tracking [5,6,7,8,9,10], etc
We focus on the scenario with any energy decay factor, which is more practical than the free-space assumption, and propose direct norm relaxation and semidefinite relaxation (SDR) based source localization methods under maximum likelihood and minimax criterion, respectively
The differences with our previous conference paper are given in the following aspects: (1) the energy decay factor can be set as any number, (2) a direct norm relaxation is utilized to relax the nonconvex optimization problem into a convex one, and (3) the performance comparison with the existing acoustic energy based source localization methods is carried out
Summary
With the advances in distributed and collaborative signal processing and communication, sensor networks have become an attractive system for various civil and military applications, especially in surveillance areas, such as environmental monitoring [1,2], traffic monitoring [3,4], source detection, localization, and tracking [5,6,7,8,9,10], etc. The position of sensors or a target is derived by fusing the received acoustic energy from local nodes in a sensor network. By considering an acoustic source localization system to learn the migration characteristics of birds, we need to track the position of them In such a system, several nodes with communication capacity are randomly deployed in a surveillance area, and form a sensor network. The sensor network system starts to locate the acoustic target, e.g., the migrated bird All of these nodes in such a sensor network receive the acoustic signal, which is generated by the acoustic source. The fusing algorithms are our main focus in this paper, which are carried out to efficiently combine the energy readings from all the nodes at a fusion center
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