Faculty Publications
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Item 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 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.