2016/07/27 by Henry E. Baidoo‐Williams, Henry E. Baidoo-Williams, Baidoo-Williams, Henry E.
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Data Analysis #FOS: Mathematics #FOS: Physical sciences #Microwave Imaging and Scattering Analysis #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Statistics and Probability (physics.data-an) #Target Tracking and Data Fusion in Sensor Networks #math.OC #physics.data-an
paper · pdf · doi:10.48550/arxiv.1608.00427
5 pages, 5 figures
openalex publication_date 2016/07/27 · arxiv created 2016/10/10 · arxiv updated 2016/10/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, we consider a novel and robust maximum likelihood approach to localizing radiation sources with unknown statistics of the source signal strength. The result utilizes the smallest number of sensors required theoretically to localize the source. It is shown, that should the source lie in the open convex hull of the sensors, precisely N+1 are required in ℝN, ~N ∈ \1,⋯,3\. It is further shown that the region of interest, the open convex hull of the sensors, is entirely devoid of false stationary points. An augmented gradient ascent algorithm with random projections should an estimate escape the convex hull is presented.