Donka S. Angelova

Design and comparison of mode-set adaptive IMM algorithms for maneuvering target tracking

The variable-structure multiple model (MM) state estimation approach is utilized for maneuvering aircraft (MA) radar tracking. Two adaptive time-varying mode-set interacting multiple model (IMM) tracking filters are designed and investigated: switching grid (SG) IMM and adaptive grid (AG) IMM algorithms, By Monte Carlo simulations the performances of the algorithms and the respective fixed grid (FG) IMM filter are evaluated and compared over different flight scenarios. It is shown that for the considered specific maneuvering target tracking problem, the SGIMM and AGIMM tracking filters significantly outperform the corresponding fixed structure version (the FGIMM filter) with respect to performance-to-computational load ratio.

Extended Object Tracking Using Monte Carlo Methods

This correspondence addresses the problem of tracking extended objects, such as ships or a convoy of vehicles moving in urban environment. Two Monte Carlo techniques for extended object tracking are proposed: an interacting multiple model data augmentation (IMM-DA) algorithm and a modified version of the mixture Kalman filter (MKF) of Chen and Liu , called the mixture Kalman filter modified (MKFm). The data augmentation (DA) technique with finite mixtures estimates the object extent parameters, whereas an interacting multiple model (IMM) filter estimates the kinematic states (position and speed) of the manoeuvring object. Next, the system model is formulated in a partially conditional dynamic linear (PCDL) form. This affords us to propose two latent indicator variables characterizing, respectively, the motion mode and object size. Then, an MKFm is developed with the PCDL model. The IMM-DA and the MKFm performance is compared with a combined IMM-particle filter (IMM-PF) algorithm with respect to accuracy and computational complexity. The most accurate parameter estimates are obtained by the DA algorithm, followed by the MKFm and PF.

Localization of Mobile Nodes in Wireless Networks with Correlated in Time Measurement Noise

Wireless sensor networks are an inherent part of decision making, object tracking, and location awareness systems. This work is focused on simultaneous localization of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multimodel auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localization accuracy is demonstrated.

Joint target tracking and classification with particle filtering and mixture Kalman filtering using kinematic radar information

This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particle filter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feature) measurements are used. A speed likelihood function for each class is defined using a prior information about speed constraints. Class-dependent speed likelihoods are calculated through the state estimates of each class-dependent tracker. They are combined with the kinematic measurement likelihoods in order to improve the classification process. The two designed estimators are compared and evaluated over rather complex target scenarios. The results demonstrate the usefulness of the proposed scheme for the incorporation of additional speed information. Both filters illustrate the opportunity of the particle filtering and mixture Kalman filtering to incorporate constraints in a natural way, providing reliable tracking and correct classification. Future observations contain valuable information about the current state of the dynamic systems. In the framework of the MKF, an algorithm for delayed estimation is designed for improving the current modal state estimate. It is used as an additional, more reliable information in resolving complicated classification situations.

Contour segmentation in 2D ultrasound medical images with particle filtering

Object segmentation in medical images is an actively investigated research area. Segmentation techniques are a valuable tool in medical diagnostics for cancer tumours and cysts, for planning surgery operations and other medical treatment. In this paper, a Monte Carlo algorithm for extracting lesion contours in ultrasound medical images is proposed. An efficient multiple model particle filter for progressive contour growing (tracking) from a starting point is developed, accounting for convex, non-circular forms of delineated contour areas. The driving idea of the proposed particle filter consists in the incorporation of different image intensity inside and outside the contour into the filter likelihood function. The filter employs image intensity gradients as measurements and requires information about four manually selected points: a seed point, a starting point, arbitrarily selected on the contour, and two additional points, bounding the measurement formation area around the contour. The filter performance is studied by segmenting contours from a number of real and simulated ultrasound medical images. Accurate contour segmentation is achieved with the proposed approach in ultrasound images with a high level of speckle noise.

Application of a Monte Carlo method for tracking maneuvering target in clutter

The Monte Carlo methods provide a possibility for improved sub-optimal Bayesian estimation. In preceding studies the authors have suggested a new implementation of the general bootstrap simulation approach — the bootstrap multiple model (BMM) filter for tracking a maneuvering target. In the present paper this algorithm is further extended for operating in a cluttered environment. Probabilistic data association (PDA), taking into account the possible measurement-to-target association hypotheses, is incorporated into the BMM algorithm to overcome the measurement–origin uncertainty. By simulation the proposed BMM PDA algorithm is evaluated and compared with the well-known interacting multiple model (IMM) PDA filter. The obtained results demonstrate a superior tracking performance of the BMM PDA algorithm at the cost of an increase in computation.

Autonomous crowds tracking with box particle filtering and convolution particle filtering

Autonomous systems such as Unmanned Aerial Vehicles (UAVs) need to be able to recognise and track crowds of people, e.g. for rescuing and surveillance purposes. Large groups generate multiple measurements with uncertain origin. Additionally, often the sensor noise characteristics are unknown but measurements are bounded within certain intervals. In this work we propose two solutions to the crowds tracking problem- with a box particle filtering approach and with a convolution particle filtering approach. The developed filters can cope with the measurement origin uncertainty in an elegant way, i.e. resolve the data association problem. For the box particle filter (PF) we derive a theoretical expression of the generalised likelihood function in the presence of clutter. An adaptive convolution particle filter (CPF) is also developed and the performance of the two filters is compared with the standard sequential importance resampling (SIR) PF. The pros and cons of the two filters are illustrated over a realistic scenario (representing a crowd motion in a stadium) for a large crowd of pedestrians. Accurate estimation results are achieved.

Algorithms for Mobile Nodes Self-Localisation in Wireless Ad Hoc Networks

This paper addresses the problem of position localisation of mobile nodes in ad hoc wireless networks based on received signal strength indicator measurements. Node mobility is modelled as a linear system driven by a discrete command Markov process. Self-localisation of mobile nodes is performed via an interacting multiple model filter consisting of a bank of unscented Kalman filters (IMM-UKF). Estimation of the mobility state, which comprises the position, speed and acceleration of the mobile nodes is accomplished. The performance of the IMM- UKF filter is investigated and compared to a multiple model particle filter (MM PF) by Monte Carlo simulation

Monte Carlo algorithm for ballistic object tracking with uncertain drag parameter

The problem of tracking a reentry ballistic object by processing radar measurements is considered in the paper. Sequential Monte Carlo-based filter is proposed for dealing with high nonlinearity of the object dynamics. A multiple model configuration is incorporated into the algorithm for overcoming the uncertainty about the object ballistic characteristics. The performance of the suggested multiple model particle filter (PF) is evaluated by Monte Carlo simulation.

Mobility tracking in cellular networks with sequential Monte Carlo filters

This paper considers mobility tracking in wireless communication networks based on received signal strength indicator measurements. Mobility tracking involves on-line estimation of the position and speed of a mobile unit. Mobility tracking is formulated as an estimation problem of a hybrid system consisting of a base state vector and a modal state vector. The command is modeled as a first-order Markov process which can take values from a finite set of acceleration levels, in order to cover the wide range of acceleration changes, a set of acceleration values is pre-determined. Sequential Monte Carlo algorithms-a particle filter (PF) and a Rao-Blackwellised particle filter (RBPF) is proposed and their performance evaluated over a synthetic data example.