Multi-target filtering with linearised complexity

Abstract

An algorithm for the estimation of multiple targets from partial and corrupted observations is introduced based on the concept of partially-distinguishable multi-target system. It combines the advantages of engineering solutions like MHT with the rigour of point-process-based methods. It is demonstrated that under intuitive assumptions and approximations, the complexity of the proposed multi-target estimation algorithm can be made linear in the number of tracks and linear in the number of observations, while naturally preserving distinct tracks for detected targets, unlike point-process-based methods.

References in corpus (1)

• Multi-object filtering with stochastic populations

Cited by in corpus (3)

• Smoothing and filtering with a class of outer measures
• Physics and Human-Based Information Fusion for Improved Resident Space Object Tracking
• Multi-object filtering with stochastic populations