Exact and Approximate Solutions of Source Localization Problems

By Amir Beck, P. Stoica, Jian Li. IEEE Transactions on Signal Processing, 2008

619 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c9a8e8ed97220e08bbf3a6f323a1d7c5ac1a96d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Alejandro Ribeiro
2. Martial Coulon
3. Henk Wymeersch
4. Erik G. Larsson
5. Brian M. Sadler
6. Alec Koppel
7. Georgios B. Giannakis
8. Aryan Mokhtari

In Civil and Structural Engineering

1. Dominic K. C. Ho
2. Alejandro Ribeiro
3. Erik G. Larsson
4. Brian M. Sadler
5. Alec Koppel

In Electrical and Electronic Engineering

1. Henk Wymeersch
2. Alec Koppel
3. Dominic K. C. Ho
4. Alejandro Ribeiro
5. Erik G. Larsson
6. Brian M. Sadler
7. A. Haimovich
8. Emil Björnson

Citations in the corpus

• Direct Localization for Massive MIMO (2016, methodology): “The TOA-based SR-LS performs poorly due to the positive bias of the TOA estimates.”
• Bias Reduction for an Explicit Solution of Source Localization Using TDOA (2012, background): “Closed-form solution is attractive because it is not iterative and avoids the local convergence problem.”
• Proximity Without Consensus in Online Multiagent Optimization (2016, methodology): “Proceeding as in [41], Section II-B, approximate this transformation by a convex unconstrained problem by defining matrix A ∈ R ×(p+1) whose ith row associated with sensor i is given as Ai = [−2li ; 1], and vector b ∈ R with ith entry as bi = r2 i − ‖li‖(2) and relaxing the constraint ‖x‖2 = α.”