This work presents a method for information fusion in source localization applications. The method utilizes the concept of optimal mass transport in order to construct estimates of the spatial spectrum using a convex barycenter formulation. We introduce an entropy regularization term to the convex objective, which allows for low-complexity iterations of the solu- tion algorithm and thus makes the proposed method applicable also to higher-dimensional problems. We illustrate the proposed method’s inherent robustness to misalignment and miscalibration of the sensor arrays using numerical examples of localization in two dimensions
This paper is concerned with the localization problem of a source belonging to a domain monitored by...
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
In recent years, the widespread availability of embedded processors and easily accessible wireless n...
During recent decades, there has been a substantial development in optimal mass transport theory and...
We propose an entropy-based sensor selection heuristic for localization. Given 1) a prior probabilit...
When monitoring spatial phenomena, which are often modeled as Gaussian Processes (GPs), choosing se...
We propose a novel entropy-based sensor selection heuristic for localization. Given 1) a prior proba...
In this work, we propose new methods for information fusion and tracking in direction of arrival (DO...
This letter deals with noncooperative localization of a single target using censored binary observat...
Entropy based sensor selection heuristics is proposed for localization applications. Given 1) the pr...
Bayesian optimal sensor placement, in its full generality, seeks to maximize the mutual information ...
This paper presents a unified framework for smooth convex regularization of discrete optimal transpo...
We consider the joint optimization of sensor placement and transmission structure for data gathering...
This paper considers localization of a source or a sensor from distance measurements. We argue that ...
The problem of target localization involves estimating the position of a target from multiple noisy ...
This paper is concerned with the localization problem of a source belonging to a domain monitored by...
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
In recent years, the widespread availability of embedded processors and easily accessible wireless n...
During recent decades, there has been a substantial development in optimal mass transport theory and...
We propose an entropy-based sensor selection heuristic for localization. Given 1) a prior probabilit...
When monitoring spatial phenomena, which are often modeled as Gaussian Processes (GPs), choosing se...
We propose a novel entropy-based sensor selection heuristic for localization. Given 1) a prior proba...
In this work, we propose new methods for information fusion and tracking in direction of arrival (DO...
This letter deals with noncooperative localization of a single target using censored binary observat...
Entropy based sensor selection heuristics is proposed for localization applications. Given 1) the pr...
Bayesian optimal sensor placement, in its full generality, seeks to maximize the mutual information ...
This paper presents a unified framework for smooth convex regularization of discrete optimal transpo...
We consider the joint optimization of sensor placement and transmission structure for data gathering...
This paper considers localization of a source or a sensor from distance measurements. We argue that ...
The problem of target localization involves estimating the position of a target from multiple noisy ...
This paper is concerned with the localization problem of a source belonging to a domain monitored by...
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for a...
In recent years, the widespread availability of embedded processors and easily accessible wireless n...