Daniel Danu

Estimation and decision fusion: A survey

Data fusion has been applied to a large number of fields and the corresponding applications utilize numerous mathematical tools. This survey limits the scope to some aspects of estimation and decision fusion. In estimation fusion our main focus is on the cross-correlation between local estimates from different sources. On the other hand, the problem of decision fusion is discussed with emphasis on the classifier combining techniques

Estimation and Decision Fusion: A Survey

Data fusion has been applied to a large number of fields and the corresponding applications utilize numerous mathematical tools. This survey limits the scope to some aspects of estimation and decision fusion. In estimation fusion our main focus is on the cross-correlation between local estimates from different sources. On the other hand, the problem of decision fusion is discussed with emphasis on the classifier combining techniques.

Fusion of over-the-horizon radar and automatic identification systems for overall maritime picture

Over-the-horizon (OTH) radar and automatic identification system (AIS) are commonly used in the surveillance of maritime areas. This paper presents a method, which includes tracking and association algorithms, for fusing the information from these two types of systems into an overall maritime picture. Data to be fused consists of asynchronous track estimates from the OTH system and measurements obtained from AIS. The data available at the fusion center, as output of real world systems, contained incomplete information, compared to theoretical tracking and fusion algorithms. A method to estimate the missing information in the input data is described. Results obtained using real data as well as simulated data are presented. This type of fusion provides overall pictures of maritime areas, with benefits for surveillance against military threats, as well as threats to exclusive economic zones.

Assignment-based particle labeling for PHD particle filter

The probability hypothesis density (PHD) filter is an estimator that approximates, on a given scenario, the multitarget distribution through its first-order multitarget moment. This paper presents two particles labeling algorithms for the PHD particle filter, through which the information on individual targets identity (otherwise hidden within the first-order multitarget moment) is revealed and propagated over time. By maintaining all particles labeled at any time, the individual target distribution estimates are obtained under the form of labeled particle clouds, within the estimated PHD. The partitioning of the PHD into distinct clouds, through labeling, provides over time information on confirmed tracks identity, tracks undergoing initiation or deletion at a given time frame, and clutter regions, otherwise not available in a regular PHD (or track-labeled PHD). Both algorithms imply particles tagging since their inception, in the measurements sampling step, and their re-tagging once they are merged into particle clouds of already confirmed tracks, or are merged for the purpose of initializing new tracks. Particles of a confirmed track cloud preserve their labels over time frames. Two data associations are involved in labels management; one assignment merges measurement clouds into particle clouds of already confirmed tracks, while the following 2D-assignment associates particle clouds corresponding to non-confirmed tracks over two frames, for track initiation. The algorithms are presented on a scenario containing two targets with close and crossing trajectories, with the particle labeled PHD filter tracking under measurement origin uncertainty due to observations variance and clutter.

Performance Evaluation of Multi-platform Distributed Data Fusion Methods for Multi-target Tracking

While sensor accuracy cannot be increased beyond a limit, the performance of target tracking algorithms can be greatly enhanced by employing multiple sensors with overlapping coverage regions. An efficient data fusion algorithm is the key to this improvement. In the current work we discuss in detail three distributed data fusion algorithms, namely, track-to-track fusion, tracklet fusion, and associated measurement fusion. These algorithms fuse the information from the sensors at different stages of processing. Their performances are compared with that of centralized fusion, which fuses the unprocessed information (measurements) from the sensors. The sensors measurements are considered asynchronous, though the fusion times are synchronized on all sensors. The scenarios used for comparison contain multiple targets with close and crossing trajectories, involving the data association assessment as well. The fusion performance metrics used in this evaluation are also explained. They are estimated using the Monte Carlo method.

Track Fusion with Feedback for Local Trackers Using MHT

With current processing power, Multiple Hypothesis Tracking (MHT) becomes a feasible and powerful solution; however a good hypothesis pruning method is mandatory for efficient implementation. The availability of a continuously increasing number of tracking systems raises interest in combining information from these systems. The purpose of this paper is to propose a method of information fusion for such trackers that use MHT locally with local information sent in the form of sensor global hypotheses and the fusion center combining them into fused global hypotheses. The information extracted from the best fused global hypotheses, in the form of ranking of received sensor global hypotheses, is sent back to local trackers, for optimized pruning. Details of the method, in terms of sensor global hypotheses generation, evaluation, pruning at local sensors, association and fusion of sensor global hypotheses at fusion center, and usage of the information received as feedback from the fusion center are presented.

Track-to-Track Association using Informative Prior Associations

In a single-frame track-to-track association, due to the local sensors track swapping (switching of the track from an estimated target to another estimated target, under measurement uncertainty conditions), the identities of the fused tracks over several frames are not preserved. The main goal of the proposed track-to-track association method is to link the histories of fused tracks over several frames and avoid track swapping at the fusion center level (e.g. to preserve the continuity of the fused tracks through their identities). In this method, the previous association hypotheses are taken as priors in a multiple-hypothesis association chain. The continuity of the fused tracks over several frames is achieved through the prediction of the fused tracks obtained from a set of best association hypotheses at each frame. Through this, if in computing the fused tracks estimation errors, their identities are taken into account (e.g. the errors of a fused track over all the frames are computed with respect to the same true target), this procedure will improve also the fused track state estimation error. The method and implementation proposed is intended to be used to identify the histories of two or more tracks at the fusion center, and possibly to improve the track-to-track association.