Faculty Publications
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Publications by NITK Faculty
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Item Motion tracking and data aggregation in distributed sensor networks using mobile agents(2005) Shivakumar, S.; Vinaykumar, K.In this paper a distributed, energy aware and collaborative approach for motion tracking and data aggregation in distributed sensor networks is proposed based on mobile agents. Proposed approach is simulated using network simulator (NS2) and the results are compared with that of the client-server model approach based on energy consumed for tracking and data aggregation. Results show that mobile agent based performs very much better than that of client server approach.Item Waveform agile sensing approach for tracking benchmark in the presence of ECM using IMMPDAF(Czech Technical University, 2017) Satapathi, G.S.; Srihari, P.This paper presents an efficient approach based on waveform agile sensing, to enhance the performance of benchmark target tracking in the presence of strong interference. The waveform agile sensing library consists of different waveforms such as linear frequency modulation (LFM), Gaussian frequency modulation (GFM) and stepped frequency modulation (SFM) waveforms. Improved performance is accomplished through a waveform agile sensing technique. In this method, the selection of waveform to be transmitted at each scan is determined, by jointly computing ambiguity function of waveform and Cramer-Rao Lower Bound (CRLB) matrix of measurement errors. Electronic counter measures (ECM) comprises of stand-off jammer (SOJ) and self-screening jammer (SSJ). Interacting multiple model probability data association filter (IMMPDAF) is employed for tracking benchmark trajectories. Experimental results demonstrate that, waveform agile sensing approach require only 39:98 percent lower mean average power compared to earlier studies. Further, it is observed that the position and velocity root mean square error values are decreasing as the number of waveforms are increasing from 5 to 50.Item Soft and evolutionary computation based data association approaches for tracking multiple targets in the presence of ECM(Elsevier Ltd, 2017) Satapathi, G.S.; Srihari, P.This paper proposes two novel soft and evolutionary computing based hybrid data association techniques to track multiple targets in the presence of electronic countermeasures (ECM), clutter and false alarms. Joint probabilistic data association (JPDA) approach is generally used for tracking multiple targets. Fuzzy clustering means (FCM) technique was proposed earlier as an efficient method for data association, but its cluster centers may fall to local minima. Hence, new hybrid data association approaches based on fuzzy particle swarm optimization (Fuzzy-PSO) and fuzzy genetic algorithm (Fuzzy-GA) clustering techniques have been presented as robust methods to overcome local minima problem. The data association matrix is evaluated for all tracks using validated measurements obtained by phased array radar for four different cases applying four data association methods (JPDA, FCM, Fuzzy-PSO, and Fuzzy-GA). Therefore, two hybrid data association approaches are designed and tested for multi-target tracking using intelligent techniques. Experimental results indicate that Fuzzy-GA data association technique provides improved performance compared to all other methods in terms of position and velocity RMSE values (38.69% and 33.19% average improvement for target-1;31.17% and 9.68% average improvement for target-2) respectively for crossing linear targets case. However, FCM technique gives better performance in terms of execution time (94.88% less average execution time) in comparison with other three techniques(JPDA, Fuzzy-GA, and Fuzzy-PSO) for the case of linear crossing targets. Thus accomplishing efficient and alternative multiple target tracking algorithms based on expert systems. The results have been validated with 100 Monte Carlo runs. © 2017 Elsevier LtdItem Robust infrared target tracking using discriminative and generative approaches(Elsevier B.V., 2017) Asha, C.S.; Narasimhadhan, A.V.The process of designing an efficient tracker for thermal infrared imagery is one of the most challenging tasks in computer vision. Although a lot of advancement has been achieved in RGB videos over the decades, textureless and colorless properties of objects in thermal imagery pose hard constraints in the design of an efficient tracker. Tracking of an object using a single feature or a technique often fails to achieve greater accuracy. Here, we propose an effective method to track an object in infrared imagery based on a combination of discriminative and generative approaches. The discriminative technique makes use of two complementary methods such as kernelized correlation filter with spatial feature and AdaBoost classifier with pixel intesity features to operate in parallel. After obtaining optimized locations through discriminative approaches, the generative technique is applied to determine the best target location using a linear search method. Unlike the baseline algorithms, the proposed method estimates the scale of the target by Lucas-Kanade homography estimation. To evaluate the proposed method, extensive experiments are conducted on 17 challenging infrared image sequences obtained from LTIR dataset and a significant improvement of mean distance precision and mean overlap precision is accomplished as compared with the existing trackers. Further, a quantitative and qualitative assessment of the proposed approach with the state-of-the-art trackers is illustrated to clearly demonstrate an overall increase in performance. © 2017 Elsevier B.V.Item Rough fuzzy joint probabilistic association fortracking multiple targets in the presence of ECM(Elsevier Ltd, 2018) Satapathi, G.S.; Srihari, P.A novel rough fuzzy joint probabilistic data association algorithm (RF-JPDA) is presented to improve the performance of multitarget tracking in the presence of clutter, electronic countermeasures (ECM) and false alarms. The possibility data association matrix is evaluated by applying upper and lower approximations of validated measurements which are obtained from the radar. Four case studies are taken to validate the proposed data association algorithm. The proposed technique performance has been compared with conventional joint probabilistic data association filter (JPDA), fuzzy clustering means (FCM), and fuzzy Genetic Algorithm (Fuzzy-GA) approaches. A hybrid data association approach is formulated and examined for multi-target tracking using intelligent technique. Further, it is evident from the experimental results that RF-JPDA approach is providing enhanced performance in terms of position root mean square error (RMSE), velocity RMSE and execution time for all cases. The average position and velocity RMSE of RF-JPDA are 42.3% and 16.98% less when compared to conventional JPDA. Thus accomplishing novel and effective multiple target tracking algorithm based on expert systems. © 2018 Elsevier LtdItem Comprehensive Track Segment Association for Improved Track Continuity(Institute of Electrical and Electronics Engineers Inc., 2018) Raghu, J.; Srihari, P.; Tharmarasa, R.; Kirubarajan, T.Track breakages are common in target tracking due to highly maneuvering targets, association with false alarms or incorrect target-originated measurements, low detection probability, close target formations, large measurement errors, and long sampling intervals, among other causes. Existing track segment association (TSA) algorithms solve this breakage problem by predicting old track segments and retrodicting young track segments to a common time followed by two-dimensional (2-D) assignment. This approach presents two disadvantages. First, these algorithms predict or retrodict from the actual point of termination or beginning of their respective tracks, that is, they neither check if the cause of a track termination was incorrect association nor redress such an erroneous association. Second, these algorithms do not utilize the measurement information during the breakage period. Often, track terminations are due to incorrect measurement association. To solve the first problem, this paper proposes a 2-D assignment-based TSA algorithm that releases incorrectly associated measurements by going backward and forward in time along old and young track segments, respectively, and then performing prediction and retrodiction. Furthermore, to address both shortcomings in existing TSA algorithms simultaneously, we propose a novel multiframe assignment-based TSA algorithm that estimates the track during the breakage period, utilizing both unassociated and released measurements simultaneously. Moreover, the proposed algorithms can handle target maneuvers subject to a single turn during the breakage period. In the proposed solution, model parameters, such as starting time of the turn, ending time of the turn, and turn rate are obtained by maximizing the likelihood that a given measurement-tuple originated from the track couple under consideration. Simulation results demonstrate that the proposed TSA algorithm is superior to existing ones in terms of association accuracy and computational cost. © 1965-2011 IEEE.Item Distributed Fusion of Optimally Quantized Local Tracker Estimates for Underwater Wireless Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2022) Reddy, B.N.B.; Pardhasaradhi, B.; Gunnery, G.; Srihari, P.Multi-sensor underwater surveillance has been a significant research problem for civilian and naval applications. Due to limited bandwidth considerations, the underwater wireless sensor networks (UWSNs) use measurement quantization to transmit information from individual sensors to the fusion center to perform centralized tracking/fusion. However, at the measurement level, quantization of azimuth information is complex due to its non-linear behavior. To address this problem, this paper proposes to perform the distributed tracking and quantizing the local estimates (state and covariance) to provide improved bandwidth and reduce computational load. The local tracker estimates the updated state and covariance of a target's time-varying dynamics in the given surveillance from the obtained measurements using extended Kalman filter (EKF) and global nearest neighbor (GNN) data association. The measurement model contains both detections of target and false alarms. This paper uses optimal quantization rather than linear quantization owing to its minimal bandwidth requirement. Once the quantized local tracks are obtained at the fusion center, these tracks are quantified using track-to-track association (T2TA) in the S-D assignment framework. The associated tracks are fused using correlation-free fusion algorithms like covariance intersect (CI), sampling covariance intersects (SCI), ellipsoidal intersect (EI), and arithmetic average (AA) algorithms to achieve the global track. The position root mean square error (PRMSE), bandwidth, and error ellipses are used to quantify the performance of the proposed framework. The simulation results show that the PRMSE of the optimally quantized fusion estimates yields good agreement with the unquantized method. Simulation results further reveals that, optimal quantization utilizes lower bandwidth compared to linear quantization. In addition, optimally quantized local estimates accomplishes promising covariance regions at the fusion center. © 2013 IEEE.Item Tracking of Radar Targets With In-Band Wireless Communication Interference in RadComm Spectrum Sharing(Institute of Electrical and Electronics Engineers Inc., 2022) Gunnery, G.; Pardhasaradhi, B.; Prashantha Kumar, P.; Srihari, P.Radar and communication system (RadComm) spectrum sharing has received considerable attention from the research community in recent years. This paper considers the distributed radars present in the surveillance region with multiple in-band wireless communication transmitters (IWCTs). A new measurement model is proposed by considering both radar returns and returns due to IWCTs. The tracking performance is evaluated using the global nearest neighbor (GNN) tracker with an extended Kalman filter (EKF) for the received measurement set. A single radar case is considered, where near geometry scenario (IWCTs are placed near the radar and target) and far-geometry scenario (IWCTs are placed far from the radar and target) are considered to evaluate the tracking performance. It is observed that a large number of tracks are resulted due to IWCTs, and identifying the actual target track is ambiguous in a single radar case. Therefore, in the second case, multiple radars are placed to investigate the problem comprehensively. The track-to-track association (T2TA) is performed to identify the true target track on multiple tracks produced owing to the presence of IWCTs and the resulting tracks from all radars pertaining to the true targets. Once the true target tracks from each radar are identified, using the T2TA, the track-to-track fusion (T2TF) is carried out to improve the estimates of the true target. The simulation results are quantified with position root mean square error (PRMSE). The posterior Cramer-Rao lower bounds (PCRLBs) quantifying the achievable estimation accuracies are also presented. The simulation results reveal that the association and fusion of tracks from multiple radars identify the true target track with good accuracy and overcome the inability to determine the true track, as in the case of a single radar. Further, the results disclose that, as the number of radars increases, the T2TA and fusion improved the PRMSE. © 2022 IEEE.Item Sequential Fusion Based Approach for Estimating Range Gate Pull-Off Parameter in a Networked Radar System: An ECCM Algorithm(Institute of Electrical and Electronics Engineers Inc., 2022) Lingadevaru, P.; Pardhasaradhi, B.; Srihari, P.Networked radar is an emerging and effective alternative to traditional radar systems to provide improved performance by fusing information from multiple radars. Further, networked radar systems (NRS) have found numerous deployments in military and civilian infrastructures in recent years. Electronic countermeasures (ECM) like jamming, Range Gate Pull-Off (RGPO), and Velocity Gate Pull-Off (VGPO) generally pose a high risk to the radar systems by injecting intentional interference. This paper proposes networked radar to detect the RGPO ECM attack and estimate the range gate deception parameter of the deceived local track in an NRS. Each radar comprises a local tracker to provide the local estimates (updated state and updated covariance), and these estimates are then sent to the fusion node. Thereafter, a track-to-track association (T2TA) is formulated at the fusion node to detect the deceived tracks using all the available local tracks. For the deceived track, the pseudo-measurements are created using the inverse Kalman filter-based tracklets. All the local tracks except deceived track are compensated and sequentially fused to create a reference measurement. After that, the deception parameter of the deceived track is estimated by using pseudo-measurement and the reference measurement by employing the recursive least square estimator (RLSE). In addition, the proposed algorithm is analyzed for single and multiple RGPO based ECM scenarios. Further, the Cramer Rao Lower Bound (CRLB) for the proposed methodology is derived. The results are quantified with a Position Root Mean Square Error (PRMSE), CRLB, innovation test, normalized estimation error squared (NEES) test, and confidence interval. The simulation results demonstrate that the proposed estimation technique provides good performance in the presence of all the local tracks are being attacked by RGPO ECM. Besides, it is evident from the results that estimator efficiency is falling below the 5% tail probability of the chi-square distribution. © 2013 IEEE.Item Multitarget Detection and Tracking by Mitigating Spot Jammer Attack in 77-GHz mm-Wave Radars: An Experimental Evaluation(Institute of Electrical and Electronics Engineers Inc., 2023) Kumuda, D.K.; Vandana, G.S.; Pardhasaradhi, B.; Raghavendra, B.S.; Srihari, P.; Cenkarmaddi, L.R.Small form factor radar sensors at millimeter wavelengths find numerous applications in the industrial and automotive sectors. These radar sensors provide improved range resolution, good angular resolution, and enhanced Doppler resolution for short range and ultrashort ranges. However, it is challenging to detect and track the targets accurately when a radar is interfered by another radar. This article proposes an experimental evaluation of a 77-GHz IWR1642 radar sensor in the presence of a second 77-GHz AWR1642 radar sensor acting as a spot jammer. A real-time experiment is carried out by considering five different targets of various cross sections, such as a car, a larger size motorcycle, a smaller size motorcycle, a cyclist, and a pedestrian. The collected real-time data are processed by four different constant false alarm rate detectors, cell averaging (CA)-CFAR, ordered statistics (OS)-CFAR, greatest of CA (GOCA)-CFAR, and smallest of CA (SOCA)-CFAR. Following that, data from these detectors are fed into two different clustering algorithms (density-based spatial clustering of applications with noise (DBSCAN) and K-means), followed by the extended Kalman filter (EKF)-based tracker with global nearest neighbor (GNN) data association, which provide tracks of various targets with and without the presence of a jammer. Furthermore, four different metrics [tracks reported (TR), track segments (TSs), false tracks (FTs), and track loss (TL)] are used to evaluate the performance of various tracks generated for two clustering algorithms with four detection schemes. The experimental results show that the DBSCAN clustering algorithm outperforms the K-means clustering algorithm for many cases. © 2001-2012 IEEE.