Robust regression and outlier detection
Robust regression and outlier detection
The ant colony optimization meta-heuristic
New ideas in optimization
Robust Parameter Estimation in Computer Vision
SIAM Review
Ant algorithms for discrete optimization
Artificial Life
M-estimator and D-optimality model construction using orthogonal forward regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ant estimator with application to target tracking
Signal Processing
An ant stochastic decision based particle filter and its convergence
Signal Processing
Ant clustering PHD filter for multiple-target tracking
Applied Soft Computing
An experimental ant colony approach for the geolocation of verbal route descriptions
Knowledge-Based Systems
A new method based on ant colony optimization for the probability hypothesis density filter
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
A real-time moving ant estimator for bearings-only tracking
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Self-adaptive stepsize search for automatic optimal design
Knowledge-Based Systems
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In the oceanic context, the aim of Target Motion Analysis (TMA) is to estimate the state, i.e. location, bearing and velocity, of a sound-emitting object. These estimates are based on a series of passive measures of both the angle and the distance between an observer and the source of sound, which is called the target. These measurements are corrupted by noise and false readings, which are perceived as outliers. Usually, sequences of measurements are taken and statistical methods, like the Least Squares method or the Annealing M-Estimator, are applied to estimate the target's state by minimising the residual in range and bearing for a series of measurements. In this project, an ACO-Estimator, a novel hybrid optimisation algorithm based on Ant Colony Optimisation, has been developed and applied to the TMA problem and its effectiveness was compared with standard estimators. It was shown that the new algorithm outperforms conventional estimators by successfully removing outliers from the measurements.