A Bayes filter is a widely used estimation algorithm, but it has inherent limitations.
Performance can degrade when the dynamics are highly nonlinear or when the probability …

Sequential Bayesian filters in non-linear dynamic systems require the recursive estimation of
the predictive and posterior probability density function (pdf). This paper introduces a …

We enable efficient and effective coordination in unpredictable environments, ie, in
environments whose future evolution is unknown a priori and even adversarial. We are …

Uncertainty quantification plays a key role in the development of autonomous systems,
decision-making, and tracking over wireless sensor networks (WSNs). However, there is a …

We study the problem of multi-agent coordination in unpredictable and partially observable
environments, that is, environments whose future evolution is unknown a priori and that can …

In the field of manoeuvring target tracking, track breakage is a common issue due to strong
manoeuvrability, missed detection, large measurement error, and long sampling interval …

Tracking manoeuvring targets often relies on complex models with non-stationary
parameters. Gaussian process (GP) based model-free methods can achieve accurate …

The adaptive kernel Kalman filter (AKKF) is an effective Bayesian inference method for non-
linear system estimation/tracking. With the AKKF, the posterior distributions of hidden states …

The performance of existing maneuvering target tracking methods for highly maneuvering
targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven …

The performance of tracking algorithms strongly depends on the chosen model assumptions
regarding the target dynamics. If there is a strong mismatch between the chosen model and …