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Simultaneous intent prediction and state estimation using an intent-driven intrinsic coordinate model

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Ahmad, BI 
Godsill, S 

Abstract

The motion of an object (e.g. ship, jet, pedestrian, bird, drone, etc.) is usually governed by premeditated actions as per an underlying intent, for instance reaching a destination. In this paper, we introduce a novel intent-driven dynamical model based on a continuous-time intrinsic coordinate model. By combining this model with particle filtering, a seamless approach for jointly predicting the destination and estimating the state of a highly manoeuvrable object is developed. We examine the proposed inference technique using real data with different measurement models to demonstrate its efficacy. In particular, we show that the introduced approach can be a flexible and competitive alternative, in terms of prediction and estimation performance, to other existing methods for various measurement models including nonlinear ones.

Description

Keywords

tracking algorithms, intent prediction, particle filters, variable rate models, intrinsic coordinate model

Journal Title

IEEE International Workshop on Machine Learning for Signal Processing, MLSP

Conference Name

2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)

Journal ISSN

2161-0363
2161-0371

Volume Title

2020-September

Publisher

IEEE

Rights

All rights reserved