M. Farooq

Royal Military College of Canada, Kingston, Ontario, Canada

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Publications (15)1.3 Total impact

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    ABSTRACT: 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.
    IEEE Transactions on Aerospace and Electronic Systems 01/2012; 48(2):1179-1191. · 1.30 Impact Factor
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    ABSTRACT: Data fusion has been applied to a large number of fields and the corresponding applications utilize numerous mathematical tools. This survey focuses on some aspects of estimation and decision fusion. In estimation fusion, we discuss the development of fusion architectures and algorithms with emphasis on the cross-correlation between local estimates from different sources. On the other hand, the techniques for decision fusion are discussed with emphasis on the classifier combining techniques. In addition, methods using neural networks for data fusion are briefly discussed.
    Neurocomputing - IJON. 01/2008; 71(13):2650-2656.
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    ABSTRACT: 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.
    Information Fusion, 2007 10th International Conference on; 08/2007
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    ABSTRACT: In this paper, a new joint target tracking and classification technique based on Observable Operator Models (OOM) is considered. The OOM approach, which has been proposed as a better alternative to the Hidden Markov Model (HMM), is used to model the stochastic process of target classification. These OOMs afford both mathematical simplicity and algorithmic efficiency compared to HMM. Conventional classification techniques use only the feature information from target signatures. The proposed OOM based classification technique incorporates the target-to-sensor orientation together with a sequence of feature information from multiple sensors. The target-to-sensor orientation evolves over time and the target aspect is important in determining the target classes. The multi-aspect classification is modeled using OOM to handle unknown target orientation. This algorithm exploits the inter-dependency of target state and the target class, which improves both the state estimates and classification of each target. Measurement ambiguity is present in both kinematic and feature measurement and therefore, the OOM based classifier is integrated into the multiframe data association framework that is used to resolve measurement origin uncertainties. This technique enables one to overcome ambiguity in feature measurements while improving track purity. A two dimensional example demonstrates the merits of the proposed OOM based joint target tracking and classification algorithm.
    Proc SPIE 06/2006;
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    ABSTRACT: Invited Panel Discussion Topics: Research Challenges in Sensor Management; Fundamental Statistics for Resource Management; Issues in Formulating Utility Functions for Sensor Management; Resource Management for Distributed Attention in Sensor Networks; Research Challenges in Network and Service Management for Distributed Net-Centric Fusion; Resource Management in Sensor Networks; Performance Metrics for Combed Tracking and Sensor Management.
    Proc SPIE 06/2006;
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    ABSTRACT: 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
    Electrical and Computer Engineering, 2006. CCECE '06. Canadian Conference on; 06/2006
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    ABSTRACT: 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.
    Proc SPIE 06/2006;
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    ABSTRACT: In this paper, a new joint target tracking and classification technique based on Observable Operator Models (OOM) is considered. The OOM approach, which has been proposed as a better alternative to the Hidden Markov Model (HMM), is used to model the stochastic process of target classification. These OOMs afford both mathematical simplicity and algorithmic efficiency compared to HMM. Conventional classification techniques use only the feature information from target signatures. The proposed OOM based classification technique incorporates the target-to-sensor orientation together with a sequence of feature information from multiple sensors. The target-to-sensor orientation evolves over time and the target aspect is important in determining the target classes. The multi-aspect classification is modeled using OOM to handle unknown target orientation. This algorithm exploits the inter-dependency of target state and the target class, which improves both the state estimates and classification of each target. Measurement ambiguity is present in both kinematic and feature measurement and therefore, the OOM based classifier is integrated into the multiframe data association framework that is used to resolve measurement origin uncertainties. This technique enables one to overcome ambiguity in feature measurements while improving track purity. A two dimensional example demonstrates the merits of the proposed OOM based joint target tracking and classification algorithm.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
    05/2006;
  • A. Sinha, T. Kirubarajan, M. Farooq
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    ABSTRACT: Data association is one of the main components of target tracking. While, in its simplest form, data association links a list of tracks to a list of measurements or links two lists of measurements (2-D association), the more complex problem involves assignment of multiple number of such lists (S-D association where S >= 3). In target tracking, the presence of false detections (false alarms) and the absence of detections from some targets (missed detections) complicate the problem of data association further. In this work, we explore the possibility of applying track ordering in priority queues to solve the association problem more efficiently. The basic component of our algorithm is to form priority queues by permutations of the tracks. Each queue is served on a first-come-first-served basis, i.e., each track is assigned to the best measurement available based on its turn in the queue. It can be shown that the best solution to the 2-D problem can be obtained from one of these queues. However, the solution is computationally expensive even for a moderate number of targets. In this paper we show that due to redundancy only a small fraction of the total number of permutations is required to be evaluated to obtain the best solution.
    Proc SPIE 09/2005;
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    ABSTRACT: Particle filter based estimation is becoming more popular because it has the capability to effectively solve nonlinear and non-Gaussian estimation problems. However, the particle filter has high computational requirements and the problem becomes even more challenging in the case of multitarget tracking. In order to perform data association and estimation jointly, typically an augmented state vector of target dynamics is used. As the number of targets increases, the computation required for each particle increases exponentially. Thus, parallelization is a possibility in order to achieve the real time feasibility in large-scale multitarget tracking applications. In this paper, we present a real-time feasible scheduling algorithm that minimizes the total computation time for the bus connected heterogeneous primary-secondary architecture. This scheduler is capable of selecting the optimal number of processors from a large pool of secondary processors and mapping the particles among the selected processors. Furthermore, we propose a less communication intensive parallel implementation of the particle filter without sacrificing tracking accuracy using an efficient load balancing technique, in which optimal particle migration is ensured. In this paper, we present the mathematical formulations for scheduling the particles as well as for particle migration via load balancing. Simulation results show the tracking performance of our parallel particle filter and the speedup achieved using parallelization.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
    08/2005;
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    ABSTRACT: In this paper we present the development of a multisensor-multitarget tracking testbed for large-scale distributed (or network-centric) scenarios. The project, which is in progress at McMaster University and the Royal Military College of Canada, is supported by the Department of National Defence and Raytheon Canada. 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 first 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.
    Proc SPIE 05/2005;
  • A. Sinha, T. Kirubarajan, M. Farooq
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    ABSTRACT: The combinatorial optimization problem of multidimensional assignment has been treated with renewed interest because of its extensive application in target tracking, cooperative control, robotics and image processing. In this work, we particularly concentrate on data association in multisensor-multitarget tracking algorithms, in which solving the multidimensional assignment is an essential step. Current algorithms generate good suboptimal solutions to these problems in pseudo-polynomial time. However, in dense scenarios these methods can become inefficient because of the resulting dense candidate association tree. Also, in order to generate the top m (or ranked) solutions these algorithms need to solve a number of optimization problems, which increases the computational complexity significantly. In this paper we develop a Randomized Heuristic Approach (RHA) for multidimensional assignment problems with decomposable costs (likelihoods). Unlike many assignment algorithms the RHA does not need the complete candidate assignment tree to start with. Instead, it constructs this tree as required. Results show that the RHA requires only a small fraction of the assignment tree and these results in a considerable reduction of computational cost. Results show that the RHA, on an average, produces better solutions than those produced by Lagrange relaxation-based multidimensional assignment algorithm which has higher computational complexity. Also, using the different solutions obtained in RHA iterations, top m solutions can be constructed with no further computational requirement. These solutions can be utilized in a soft decision based algorithm which performs much better than hard decision based algorithm, as shown in this paper by a ground target tracking example.
    Proc SPIE 05/2005;
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    ABSTRACT: Not Available
    Information Fusion, 2003. Proceedings of the Sixth International Conference of; 02/2003
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    ABSTRACT: Multiple-sensor data fusion is becoming increasingly important among the defense community as technology evolves. The use of multiple-sensor information reduces the ambiguity and presents the operator with an enhanced tactical picture of the surveillance volume. The crucial step in the fusion process is the data association of the information received from the sensors. If the associations are made incorrectly then the fused data could potentially give rise to estimates that might be worse than those of a single sensor. In this paper, we explore the problem of associating Electronic Support Measure (ESM) tracks with one or more possible radar tracks. We examine the performance of different track-to-track dat association algorithms through simulations, which is based on the Probability of False Association (Pfa) and Probability of Correct (Pc) association.
    Proc SPIE 08/2001;
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    D Gendron, K Benameur, M Farooq
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    ABSTRACT: In this paper, we present different approaches for the association of tracks for airborne sensors. The proposed approaches explore the effects of the choice of coordinate systems on the tracking filters and the association process. The performance of the association techniques is analysed in terms of the probability of correct classification (P c) and the probability of false association (P fa). This practical aspect of the multi-target multi-sensor tracking problem is presented for the association of radar tracks to ESM tracks in different scenarios.
    01/2001;