M. Farooq

Royal Military College of Canada, Kingston, Ontario, Canada

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Publications (39)7.84 Total impact

  • A. Sinha · Z. Ding · Thia Kirubarajan · M. Farooq
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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 04/2012; 48(2):1179-1191. DOI:10.1109/TAES.2012.6178056 · 1.39 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 08/2008; 71(13):2650-2656. DOI:10.1016/j.neucom.2007.06.016 · 2.01 Impact Factor
  • Source
    D. Danu · A. Sinha · T. Kirubarajan · M. Farooq · D. Brookes
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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: 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.
    Proceedings of SPIE - The International Society for Optical Engineering 06/2006; DOI:10.1117/12.694899 · 0.20 Impact Factor
  • S. Sutharsan · A. Sinha · T. Kirubarajan · M. Farooq
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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.
    Proceedings of SPIE - The International Society for Optical Engineering 06/2006; DOI:10.1117/12.667793 · 0.20 Impact Factor
  • A. Sinha · Z. J. Ding · Thia Kirubarajan · M. Farooq
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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.
    Proceedings of SPIE - The International Society for Optical Engineering 06/2006; DOI:10.1117/12.666319 · 0.20 Impact Factor
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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
  • S. Sutharsan · A. Sinha · T. Kirubarajan · M. Farooq
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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.
  • Hongyan Sun · M. Farooq
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    ABSTRACT: In this paper, the combination rules, such as the Dempster-Shafer's (D-S) combination rule, the Yager's combination rule, the Dubois and Prade's (D-P) combination rule, the DSm's combination rule and the disjunctive combination rule, are applied to the situation assessment and target identification problems. Given two independent sources of information with different resolutions, the results from each combination rule of evidence are analyzed. It is observed from these results that the DSm's rule is the fastest in arriving at a decision compared to the other three rules, while the disjunctive combination rule is the slowest. The Yager's rule yields the same identification results for the situation assessment as the Dubois and Prade's rule. Moreover, the decision-making of the D-S' rule is faster than that of the Yager's as well as of the Dubois and Prade's rules, however, slower than that of the DSm's rule
    Proceedings of SPIE - The International Society for Optical Engineering 01/2006; DOI:10.1117/12.669579 · 0.20 Impact Factor
  • 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.
    Proceedings of SPIE - The International Society for Optical Engineering 09/2005; DOI:10.1117/12.619175 · 0.20 Impact Factor
  • S. Sutharsan · A. Sinha · T. Kirubarajan · M. Farooq
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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.
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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.
    Proceedings of SPIE - The International Society for Optical Engineering 05/2005; DOI:10.1117/12.606780 · 0.20 Impact Factor
  • 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.
    Proceedings of SPIE - The International Society for Optical Engineering 05/2005; DOI:10.1117/12.605462 · 0.20 Impact Factor
  • B. Ragel · M. Farooq
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    ABSTRACT: The inertial navigation system (INS) that is used to determine the position, velocity and/or acceleration of an autonomous underwater vehicle (AUV) deteriorates with time. In order to reduce the error caused by the INS deterioration an external aiding source can be employed. The differential global positioning system (DGPS) is one of the external aiding sources that can be used to reduce the error caused by the INS. Precise location of an AUV is a must when it comes to clearing sea mines or mapping the sea floor. In this paper we discuss how the DGPS can be incorporated with the INS in order to improve the positioning accuracy of AUV.
    Circuits and Systems, 2004. MWSCAS '04. The 2004 47th Midwest Symposium on; 08/2004
  • Ahmed S. Gad · M. Farooq
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    ABSTRACT: The problem of multisensor-multitarget tracking is mainly dependent on the data association. In this paper, the fuzzy logic-based single target tracker is extended to the multitarget case. Multitarget scenario incorporating four targets both maneuvering and non-maneuvering in the same surveillance volume is analyzed. The proposed multitarget tracker, also called the Multitarget Tracking - Fuzzy Data Association "MTT-FDA" tracker, employs fuzzy variables capable of resolving the problem of multiple crossing targets. These variables are the rate of change of the target states over a sliding window. It has been observed through simulations that a window size of five time scans is sufficient to yield acceptable results. Moreover, the proposed tracker was exercised against the realistic multitarget data set. The results reveal that the proposed fuzzy tracker yields superior performance compared to other existing tracking schemes.
    Proceedings of SPIE - The International Society for Optical Engineering 08/2004; DOI:10.1117/12.544438 · 0.20 Impact Factor
  • Source
    A. Gad · M. Farooq · J. Serdula · D. Peters
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    ABSTRACT: Due to the current advances in technology, it is both possible as well as feasible to track multiple targets in a network centric environment. To this end, the development and the design of a network centric environment must be based on solid understanding of the theoretical foundations and should yield required performance in a control simulation. The paper provides an overview of various issues involved in a network centric sensor data fusion environment. Besides discussing the conventional techniques to resolve the sensor integration, registration, association, and fusion issues, non-conventional approaches, such as Fuzzy Logic and Viterbi based methods are also be explored in this paper. In addition, the design of a versatile simulation environment based on a multi-tiered fusion architecture to evaluate the sensor integration techniques is presented.
  • A. Gad · M. Farooq
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    ABSTRACT: This paper presents an overview of the application of fuzzy logic in power and control systems. More than eighty papers have been reviewed and briefly summarized.
    Circuits and Systems, 2003 IEEE 46th Midwest Symposium on; 01/2004
  • Hongyan Sun · M. Farooq
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    ABSTRACT: In this paper, the conjunctive and disjunctive combination rules of evidence, namely, the Dempster-Shafer' s (D-S) combination rule, the Yager's combination rule, the Dubois and Prade's (D-P) combination rule, the DSm's combination rule and the disjunctive combination rule, are studied for the two independent sources of information. The properties of each combination rule of evidence are discussed in detail, such as the role of evidence of each source of information in the combination judgment, the comparison of the combination judgment belief and ignorance of each combination rule, the treatment of conflict judgments given by the two sources of information, and the applications of combination rules. Zadeh' s example is included in the paper to evaluate the performance as well as efficiency of each combination rule of evidence for the conflict judgments given by the two sources of information.
    Proceedings of SPIE - The International Society for Optical Engineering 01/2004; DOI:10.1117/12.544033 · 0.20 Impact Factor
  • Ahmed S. Gad · Mohammad Farooq
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    ABSTRACT: The research in multitarget tracking has mainly focused on the development and implementation of efficient data association algorithms with acceptable performance. The Viterbi Data Association (VDA) algorithm has proven to have low computation cost and hence a good candidate for the extension to multiple target tracking case. In this paper, the VDA is implemented for tracking both the single and multiple maneuvering targets in clutter. The track initiator and the adaptive sliding window techniques are used so that the VDA can maintain a lock on the track. The performance of the algorithm is assessed via Mote-Carlo simulations. The computation complexity analysis reveals that the VDA is computationally more efficient over the known tracking techniques.
    Proceedings of SPIE - The International Society for Optical Engineering 08/2003; DOI:10.1117/12.489650 · 0.20 Impact Factor