Keynote Presentations

Reliable target tracking requires the recursive estimation of dynamic object states from uncertain and heterogeneous sensor measurements. As the dimensionality of the state space, nonlinearities, and the number of possible measurement-to-track associations increase, conventional Bayesian filtering methods face substantial computational and memory challenges.

This talk explores the potential of quantum computing for target tracking and state estimation in multi-sensor data fusion. Following an introduction to Bayesian state estimation and probabilistic tracking, it discusses efficient density representations based on tensor decompositions and presents quantum concepts relevant to information fusion. Gate-based quantum algorithms are introduced for representing discretised state spaces and simulating drift and diffusion processes.

In addition, the presentation investigates adiabatic quantum computing for combinatorial tracking tasks such as data association, as well as energy-based formulations of Bayesian measurement updates. Quantum-inspired approaches, including wave-function and path-integral concepts, are also considered as novel methods for classical tracking applications.

Traditional identification methods for nonstationary linear systems fall into two categories: structured and unstructured methods. The first class, containing algorithms like LMS and RLS, does not assume any model of system parameters’ variation, which allows estimating parameters of relatively slowly varying systems. When system parameters vary rapidly, better estimation accuracy can be achieved by imposing a stochastic state-space model on coefficient evolution or by representing coefficient trajectories using a deterministic basis expansion, leading, for example, to Kalman filtering and basis-expansion approaches, respectively. However, the price of structured methods lies in increased computational complexity. An alternative is to use a hybrid approach, which applies unstructured methods to obtain approximately unbiased but raw estimates (called pre-estimates), which are further post-processed to obtain final estimates. This talk presents a family of pre-estimation techniques and discusses their properties and limitations. Finally, their application in self-interference cancellation in full-duplex underwater acoustic communication is presented.