Multi Target Observability

In this paper, we mainly focus on the problem of multi-target observability, focusing on the unique state estimation criteria for multiple targets. We derive the condition which is necessary as well as sufficient for observability using bearing angles with multiple higher-order dynamics observed by a single observer. We then establish an alternative notion of observability by analyzing ambiguous target trajectories and deriving the condition which is NECNDSUF (Nec. and Suff.) for multi-target observability, considering three types of measurements: Doppler-only, bearing-only, and combined Doppler and bearing measurements, which offers insights that can improve target distinguishability, trajectory reconstruction, and overall tracking accuracy.

Authors

Publication Details

Journal
ArXiv.org
Published
2025-07-19
Primary Topic
Target Tracking and Data Fusion in Sensor Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multi Target Observability

Rajesh Dey, Dipendra Kumar Mitra, M. Saleem Khan, L. Kumar et al.
ArXiv.org
Target Tracking and Data Fusion in Sensor Networks
article

Multi Target Observability

Rajesh Dey, Dipendra Kumar Mitra, M. Saleem Khan, L. Kumar, D. Banerjee
article en

Abstract

In this paper, we mainly focus on the problem of multi-target observability, focusing on the unique state estimation criteria for multiple targets. We derive the condition which is necessary as well as sufficient for observability using bearing angles with multiple higher-order dynamics observed by a single observer. We then establish an alternative notion of observability by analyzing ambiguous target trajectories and deriving the condition which is NECNDSUF (Nec. and Suff.) for multi-target observability, considering three types of measurements: Doppler-only, bearing-only, and combined Doppler and bearing measurements, which offers insights that can improve target distinguishability, trajectory reconstruction, and overall tracking accuracy.

ArXiv.org
Openalex Percentile: Top 80%
Target Tracking and Data Fusion in Sensor Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.