Mohamad Farooq

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

Hough transform based multisensor, multitarget, track initiation technique

The Hough transform is suggested in literature as an effective technique for track initiation. However, to date, most papers have focused on simplistic applications of this technique based on one sensor, one target, and no clutter scenarios. We present a detailed methodology applying the Hough transform to the multiple target, multiple sensor data association and track initiation problem. Using a two-tiered methodology, bearing and range tracks are detected in separate Hough spaces. The measurements defining these tracks are subsequently associated for the purpose of track detection and initiation. The effectiveness of this technique is demonstrated through several comprehensive simulation scenarios.

Track quality based multitarget tracking algorithm

In multitarget tracking alongside the problem of measurement to track association, there are decision problems related to track confirmation and termination. In general, such decisions are taken based on the total number of measurement associations, length of no association sequence, total lifetime of the track in question. For a better utilization of available information, confidence of the tracker on a particular track can be used. This quantity can be computed from the measurement-to-track association likelihoods corresponding to the particular track, target detection probability for the sensor-target geometry and false alarm density. In this work we propose a multitarget tracker based on a track quality measure which uses assignment based data association algorithm. The derivation of the track quality is provided. It can be noted that in this case one needs to consider different detection events than that of the track quality measures available in the literature for probabilistic data association (PDA) based trackers. Based on their quality and length of no association sequence tracks are divided into three sets, which are updated separately. The results show that discriminating tracks on the basis of their track quality can lead to longer track life while decreasing the average false track length.

Fuzzy logic approach to data association

This paper proposes a new fuzzy logic approach for solving the data association problem typically encountered in the application of target tracking. A single massive target maneuvering in a heavily-cluttered underwater environment is considered. The proposed fuzzy data association (FDA) approach is combined with an interacting multiple model (IMM) filter. The resultant IMM-FDA tracking algorithm is applied to estimate the state of the maneuvering target, and its performance is compared to that of a combination of an IMM filter and the probabilistic data association (PDA) scheme. The obtained results indicate that the IMM-FDA significantly outperforms the IMM-PDA at the expense of requiring more computational cost and introducing a short processing lag.

Track Quality Based Multitarget Tracking Approach for Global Nearest-Neighbor Association

In multitarget tracking, in addition to the problem of measurement-to-track association, there are decision problems related to track confirmation and termination. In general, such decisions are taken based on the total number of measurement associations, length of no association sequence, and total lifetime of the track in question. For a better utilization of available information, confidence of the tracker on a particular track can be used. This quantity can be computed using the measurement-to-track association likelihoods corresponding to the particular track, target detection probability for the sensor-target geometry, and false alarm density. A track quality measure is proposed here for assignment-based global nearest neighbor (GNN) trackers. It can be noted that to compute track quality measure for assignment-based data association one needs to consider different detection events than those considered for computation of the track quality measures available in the literature, which are designed for probabilistic data association (PDA) based trackers. In addition to the proposed track quality measure, a multitarget tracker based on it is developed, which is particularly suitable in scenarios with temporarily undetectable targets. In this work, tracks are divided into three sets based on their quality and measurement association history: initial tracks, confirmed tracks, and unobservable tracks. Details of the update procedures of the three track sets are provided. The results show that discriminating tracks on the basis of their track quality can lead to longer track life while decreasing the average false track length.

Fuzzy-logic-based target tracking algorithm

Although there is a large body of works on conventional target tracking techniques that are based primarily on Kalman filtering and probabilistic data association, there are very few practical techniques that can be shown to perform well under a high cluttered tracking environment. This is due to the difficulty of the combined target detection and measurement to track association problem. Furthermore, conventional techniques usually make some simplifying assumptions that are difficult to realized in practice, e.g. the clutter density is uniform, measurement noise is stationary, the target track is well defined, etc. Another weakness of the conventional techniques is that even if we have some special knowledge about target attributes, it is not easy to incorporate this knowledge into the tracking problem. This paper first presents an analysis of the target tracking problem using fuzzy logic theory. Subsequently, a number of fuzzy propositions that a fuzzy tracker can use to implement a data association algorithm are formulated. Finally, a fuzzy tracker is implemented based on the fuzzy association rules and Kalman filtering and its performance is compared against the performance of a standard PDA filter.

Application of fuzzy logic to target tracking in a cluttered environment

Most of the real world engineering problems are imprecise and they carry a certain degree of fuzziness in the description of their nature. Fuzzy logic is a design methodology that can be used to solve real life problems. It has the advantage of lower development costs, superior features, and better end product performance. Fuzzy logic makes it possible to describe complex systems using expert experience and knowledge in English-like rules, which are easy to learn and use, even by non-experts. Fuzzy technique does not require system modeling or complex mathematical equations. The design methodology is to first understand and characterize the system behavior by using our basic knowledge and experience and then design the algorithm using the fuzzy rules that describe the relationship between its input and output. This is done by debugging the design through simulations and if the performance is not satisfactory we only need to modify or add some fuzzy rules. There exists considerable literature on target tracking based on the Kalman filtering and probabilistic data association (PDA) techniques. A few of these techniques can yield acceptable results in a high-density clutter environment due to the complexity of combined target and measurement to track association or due to the simplification assumed in these techniques. This paper presents the use of fuzzy association rules involved in data association of target measurements under a high-density clutter. The fuzzy tracker is used to track a target and its performance is compared with a standard PDA filter for various signal-to-noise ratios (SNR).

Tracking highly maneuverable targets in clutter using interacting multiple-model fuzzy-logic-based tracker

The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The algorithm has the ability to estimate the state of a dynamic system with several modes which can switch from one mode to another. It is also considered to be the best compromise between the complexity and the performance. It is mainly used for tracking highly maneuvering targets in the presence of clutter by invoking the Probabilistic Data Association (PDA) in the estimator structure, also called IMM-PDA. Recently, it has been shown that the PDA technique does not perform well when tracking targets at low signal to noise ratios (SNR). An alternative technique to data association is the Fuzzy Data Association (FDA) which has the ability to track targets in clutter and in a low SNR environment. In this paper, an IMM-FDA technique is proposed for tracking highly maneuvering targets in clutter and in a low SNR environment. Simulations have been conducted to compare the performance of the proposed approach with that of the IMM-PDA. A typical scenario for a highly maneuvering target is considered as a tracking example. The simulation results reveal that both the trackers perform well when tracking the maneuvering target at high SNR. At low SNR, only the IMM-FDA is able to track the target accurately.

Dempster-Shafer combination rule induced by multivalued mapping

Based on a multi-valued mapping from a probability space (X,(Omega) ,Rmu) to space S, a probability measure over a class 2s of subsets of S is defined. Then using the product combination rule of multiple information sources, the Dempster-Shafer combination rule is derived. The investigation of the two rules indicates that the Dempster rule and the Dempster-Shafer combination rule are for different spaces. Some problems of the Dempster-Shafer combination rule are interpreted via the product combination rule that is used for multiple independent information sources. A technique to improve the method is proposed. Finally, an error in multi-valued mappings in [20] is pointed out and proved.

Network-Centric Multisensor-Multitarget Tracking Testbed Based on Peer-to-Peer Communication

In this paper we present a multisensor-multitarget tracking testbed for large-scale distributed scenarios. The objective is to develop a testbed capable of handling multiple, heterogeneous sensors in a hierarchical architecture for maritime surveillance. The testbed consists of a scenario generator that can generate simulated data from multiple sensors including radar, sonar, IR and ESM as well as a tracker framework into which different tracking algorithms can be integrated. In the current stage of the project, the IMM/assignment tracker, and the particle filter (PF) tracker are implemented in a distributed architecture and some preliminary results are obtained. Other trackers like the multiple hypothesis tracker (MHT) are also planned for the future