Track-Before-Detect Adaptive Birth Using Generic Observation Model Labeled Random Finite Sets

Anthony Trezza, Anthony Murray, Asaf Y. Rothschild, Luke Rosenberg, Donald J. Bucci, Pramod K. Varshney

Research output: Chapter in Book/Entry/PoemConference contribution


Labeled random finite set filters are a popular option for multi-target tracking in challenging high-clutter, low probability of detection scenarios. Recent advances have shown that the labeled random finite set formalism can be extended to the track-before-detect setting, but lack a thorough discussion of measurement-driven track initialization. This paper presents an approach for generating a labeled multi-Bernoulli prior density on target states from radar power measurements. The prior density is generated adaptively from radar measurements and the multi-object density of previously persisting targets via a thresholding procedure that assumes a constant backscatter model (i.e., Swerling 0), Gaussian point spread function, and complex Gaussian receiver noise. The threshold per cell is constructed using a fixed false alarm rate that accounts for the contribution of energy from existing targets in the scene. This procedure improves upon existing approaches by enabling tracks to be initialized in the proximity of existing tracks. The proposed approach is demonstrated within a generic observation model labeled random finite set filter over a challenging scenario containing multiple overlapping target returns and target spawning dynamics.

Original languageEnglish (US)
Title of host publication2023 IEEE International Radar Conference, RADAR 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665482783
StatePublished - 2023
Event2023 IEEE International Radar Conference, RADAR 2023 - Sydney, Australia
Duration: Nov 6 2023Nov 10 2023

Publication series

NameProceedings of the IEEE Radar Conference
ISSN (Print)1097-5764
ISSN (Electronic)2375-5318


Conference2023 IEEE International Radar Conference, RADAR 2023


  • Generic observation model
  • Measurement adaptive birth
  • Random finite sets
  • Track-before-detect

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Instrumentation


Dive into the research topics of 'Track-Before-Detect Adaptive Birth Using Generic Observation Model Labeled Random Finite Sets'. Together they form a unique fingerprint.

Cite this