ArticleSensors (Basel, Switzerland)2019
Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Advancing ADAS Perception: A Sensor-Parameterized Implementation of the GM-PHD Filter.Sensors (Basel, Switzerland) · 2024Article
- Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors.Sensors (Basel, Switzerland) · 2020Article
- Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System.Sensors (Basel, Switzerland) · 2020Article
- Improved Correlation Filter Tracking with Enhanced Features and Adaptive Kalman Filter.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target's position, one wants to estimate the true trajectory and reconstruct the full phase space including velocity and acceleration. The shadowing filter offers a robust methodology to achieve such an estimation and reconstruction. Here, we highlight and validate important merits of this methodology for real-life applications. In particular, we explore the filter's performance when dealing with correlated or uncorrelated noise, irregular sampling in time and how it can be optimised even when the true dynamics of the system are not known.
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Registered trials
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