Prospects of Normalizing Flow-Based Differentiable Particle Filters in Target Modelling
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Accurate target modelling is essential for modern air surveillance, particularly in
the face of increasingly dynamic and unpredictable target behaviour, such as that
exhibited by hypersonic missiles and UAVs. Traditional tracking methods, including
Kalman Filters, Interacting Multiple Model (IMM) filters, and Particle Filters
(PF), rely heavily on known target dynamics and are often limited in adaptability.
This thesis explores the potential of Differentiable Particle Filters (DPFs), and
in particular Normalizing Flow-based DPFs (NF-DPFs), as a data-driven alternative
for modelling and tracking targets with unknown or highly non-linear dynamics.
To address these challenges, a NF-DPF was implemented for radar-based target
tracking. The filter was designed as a fully differentiable architecture that learns
both the proposal distribution and the system dynamics using conditional normalizing
flows based on the RealNVP framework. It incorporates optimal transport-based
differentiable resampling and was trained end-to-end using a combination of a blockwise
Evidence Lower Bound (ELBO) loss and the RMSE in position, velocity, and
acceleration. The training was conducted on target data generated from stochastic
differential equations (SDEs) simulating a range of motion patterns, including
turning and accelerating trajectories. The NF-DPF was benchmarked against a
bootstrap PF and a IMM filter across three experiments with increasing data complexity
and variations in filter state dimensionality.
Results showed that the NF-DPF provided competitive performance in low-dimensional
settings and under specific dynamic behaviours, particularly turning trajectories
that were not well captured by the IMM’s filters. However, it struggled to outperform
the IMM in more complex scenarios involving higher-dimensional state representations.
This performance gap was largely attributed to the increased difficulty
of learning high-dimensional transition and proposal distributions, combined with
computational limitations such as a restricted number of particles.
The results highlight both the potential and current limitations of NF-DPFs for
target modelling and contribute to the growing body of research exploring the integration
of flexible, learning-based models into particle filtering frameworks for
defence and surveillance applications.
Beskrivning
Ämne/nyckelord
Differentiable Particle Filter, Target Tracking, Normalizing Flow