Faculty Publications
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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 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 Navigation in GPS spoofed environment using m-best positioning algorithm and data association(Institute of Electrical and Electronics Engineers Inc., 2021) Pardhasaradhi, B.; Srihari, P.; Aparna., P.Intentionally misguiding a global positioning system (GPS) receiver has become a potential threat to almost all civilian GPS receivers in recent years. GPS spoofing is among the types of intentional interference, in which a spoofing device transmits spoofed signals towards the GPS receiver to alter the GPS positioning information. This paper presents a robust positioning algorithm, followed by a track filter, to mitigate the effects of spoofing. It is proposed to accept the authentic GPS signals and spoofed GPS signals into the positioning algorithm and perform the robust positioning with all possible combinations of authentic and spoofed pseudorange measurements. The pseudorange positioning algorithm is accomplished using an iterative least squares (ILS). Further, to efficiently represent the robust algorithm, the M-best position algorithm is proposed, in which a likelihood-based cost function optimizes the positions and only provides M-best positions at a given epoch. However, during robust positioning, the positions evolved due to spoofed pseudorange measurements are removed to overcome GPS spoofing. In order to remove the fake positions being evolved owing to wrong measurement associations in the ILS, a gating technique is applied within the Kalman filter (KF) framework. The navigation filter is a three-dimensional KF with a constant velocity (CV) model, all the position estimates evolved at a specific epoch are observations. Besides, to enhance this technique's performance, the track to position association is performed by using two data association algorithms: nearest neighbor (NN) and probabilistic data association (PDA). Simulations are carried out for GPS receiver positioning by injecting different combinations of spoofed signals into the receiver. The proposed algorithm's efficiency is given by a success rate metric (defined as the navigation track to follow the true trajectory rather than spoofing trajectory) and position root mean square error (PRMSE). © 2013 IEEE.Item Spoofer-to-Target Association in Multi-Spoofer Multi-Target Scenario for Stealthy GPS Spoofing(Institute of Electrical and Electronics Engineers Inc., 2021) Pardhasaradhi, B.; Srihari, P.; Aparna., P.Global navigation satellite system (GNSS) based navigation is omnipresent in today's world, providing position, velocity, and time (PVT) information with inexpensive GPS receivers. These receivers are highly vulnerable to intentional interference like GPS spoofing and meaconing. The spoofing of a single GPS receiver using a spoofer setup is widespread, and the concept of spoofing multiple targets with multiple distributed spoofers is also equally adaptable. Traditionally, in distributed spoofers, the multiple spoofers in the surveillance region work independently without knowing other spoofers being installed. Multiple spoofers deployment and its management are optimal for misguiding the multiple GPS receivers in the given surveillance. This paper presents a generalized mathematical model for the multi-spoofer multi-target (MSMT) scenario, spoofer management, and spoofer-to-target association. The received power of spoofed signals is considered as an evaluating parameter for locking the spoofed signals onto the GPS receivers. Three novel centralized networking-based spoofing techniques are proposed to overcome spoofer-to-target association in distributed networking. Firstly, the global nearest neighbor (GNN) based centralized spoofing is proposed. The overall cost of the function is minimized by assigning a unique spoofer-ID to a unique target-ID. In GNN-based centralized spoofing, the overall global cost minimizes, but it does not ensure that every target-to-spoofer assignment is minimum. Secondly, the spoofers of opportunity-based centralized spoofing with the GNN association is proposed to resolve the spoofer-to-target association and to increase the hit ratio. However, it is hard to install more spoofers; therefore, a tunable transmitting power-based centralized spoofing with the GNN association is presented to accomplish efficient spoofer-to-target association and higher hit-ratio. The spoofing efficiency is evaluated using spoofer-to-target association, hit ratio, and position root mean square error (PRMSE). All the proposed algorithms outperform the distributed spoofing. We also observe that the tunable power-based spoofing is an optimal solution in MSMT scenario. © 2013 IEEE.Item GNSS Spoofing Detection and Mitigation in Multireceiver Configuration via Tracklets and Spoofer Localization(Institute of Electrical and Electronics Engineers Inc., 2022) Pardhasaradhi, B.; Gunnery, G.; Vandana, G.S.; Srihari, P.; Aparna., P.Global navigation satellite systems (GNSS) sensors estimate its position, velocity, and time (PVT) using pseudorange measurements. When there is no interference, the pseudoranges are due to authentic satellites, and the bearings is distinguishable. Whereas, in the presence of any intentional interference source like spoofer, the pseudorange measurements owing to spurious signals and all the bearings from the same direction. These spurious attacks yield either no position or falsified position to the GNSS receiver. This paper proposes to install multiple GNSS receivers on a vehicle (assumed to be cooperative) to detect and mitigate the spoofing attack. While installing multiple GNSS receivers, we assume that each GNSS receiver's relative position vector (RPV) is assumed to be known to other GNSS receivers. The installed GNSS receivers use the extended Kalman filter (EKF) framework to estimate their PVT. We proposed to calculate the equivalent-measurement and equivalent-measurement covariance of each GNSS receiver in the Cartesian coordinates in the tracklet framework. These tracklets are translated to the vehicle center using RPV to obtain translated-Tracklets. The translated tracklet based generalized likelihood ratio test (GLRT) is derived to detect the spoofing attack at a given epoch. In addition to that, these translated-Tracklets are processed in a batch least square (LS) framework to obtain the vehicle position. Once the attack is detected at a specific epoch, it quantifies that the position information is false. Moreover, another spoofing test is also formulated using DOA of signals. Once both the tests confirm the spoofing attack, the spoofer localization is performed using pseudo-updated states of GNSS receivers and acquired bearings in the iterative least-squares (ILS) framework. Mitigation of spoofing attack can be achieved either by projecting a null beam in the direction of the spoofer or by launching a counter-Attack on the spoofer. The simulation results demonstrate that the proposed algorithm detects spoofing attacks and ensures continuity in the navigation track. As the number of satellite signals increases, the algorithms provide better position root mean square error (PRMSE) for GNSS receivers track, vehicle track, and spoofer localization. © 2013 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 Position estimation in indoor using networked GNSS sensors and a range-azimuth sensor(Elsevier B.V., 2022) Pardhasaradhi, B.; Gunnery, G.; Raghu, J.; Srihari, P.The global navigation satellite systems (GNSS) receivers suffer from determining an accurate position estimate in the indoor region due to non-line of sight (Non-LOS) conditions. This paper proposes a novel method of position estimation within the indoor region by using distributed GNSS sensors and a range-azimuth sensor setup. All the GNSS sensors are connected to a central node; the inaccurate position estimates evolved from the Kalman filter (KF) framework are transmitted to a central node as primary data. The deviation between the inaccurate position estimate and the GNSS sensor's actual position is the positional deviation (PD) vector, which needs to be estimated. In the same indoor region, a range-azimuth sensor is also deployed. It estimates the GNSS sensor's physical location in its local coordinates, and these estimates are being transmitted to a central node as secondary data. Further, by using the primary and secondary data, we formulated a PD vector compensation followed by a sequential fusion (SF) framework to derive the precise locations of both GNSS sensors and the range-azimuth sensor by recursively estimating the PD vector. Finally, the Cramer–Rao lower bound (CRLB) for the proposed framework is derived. The simulation results are quantified with the root mean square error (RMSE) and CRLB. © 2022 Elsevier B.V.Item Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System(Institute of Electrical and Electronics Engineers Inc., 2024) Mahipathi, A.C.; Pardhasaradhi, B.P.; Gunnery, S.; Srihari, P.; D'Souza, J.; Jena, P.The widespread usage of the Radio Frequency (RF) spectrum for wireless and mobile communication systems generated a significant spectrum scarcity. The Joint Radar-Communication System (JRCS) provides a framework to simultaneously utilize the allocated radar spectrum for sensing and communication purposes. Generally, a Successive Interference Cancellation (SIC) based receiver is applied to mitigate mutual interference in the JRCS configuration. However, this SIC receiver model introduces a communication residual component. In response to this issue, the article presents a novel measurement model based on communication residual components for various radar waveforms. The radar system's performance within the JRCS framework is then evaluated using the Fisher Information Matrix (FIM). The radar waveforms considered in this investigation are rectangular pulse, triangular pulse, Gaussian pulse, Linear Frequency Modulated (LFM) pulse, LFM-Gaussian pulse, and Non-Linear Frequency Modulated (NLFM) pulse. After that, the Kalman filter is deployed to estimate the target kinematics (range and range rate) of a single linearly moving target for different waveforms. Additionally, range and range rate estimation errors are quantified using the Root Mean Square Error (RMSE) metric. Furthermore, the Posterior Cramer-Rao Lower Bound (PCRLB) is derived to validate the estimation accuracy of various waveforms. The simulation results show that the range and range rate estimation errors are within the PCRLB limit at all time instants for all the designated waveforms. The results further reveal that the NLFM pulse waveform provides improved range and range rate error performance compared to all other waveforms. © 2020 IEEE.