Abstract
Tracking using only bearing measurements has elemental drawbacks like poor observability of the process and significant preliminary faults. Due to this reason, passive target tracking using bearing and frequency measurements is of good significance for developing a vigorous and swift-tracking system in a passive framework. Centered on the comparative analysis of traditional non-linear target tracking problems, a modern filtering technique called Doppler-Bearing Iterated Extended Kalman Filter (DB-IEKF) is projected in this correspondence. In this research, a new DB-IEKF framework for solving the problem of nonlinear filtering is presented with different optimization methods and compared with Doppler-Bearing Extended Kalman Filter (DBEKF). Besides, new optimization methods are also incorporated in this research to lessen the complexity in the optimization techniques, which reduces the computing complication. DB-IEKF has proved to be a vital tool for estimating the state of the target while tracking using nonlinear systems. The DB-IEKF, on the other hand, does not acquire optimal features that are comparable to those of the extended Kalman filter, and it may perform badly. By considering the DB-IEKF as an optimization problem, it is possible to enhance its efficiency and resilience in a variety of situations. DB-IEKF was carried out using different optimization methods in MATLAB software and proved the performance of each optimization technique concerning DB-IEKF and compared those methods with DBEKF.
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Jagan, B.O.L., Rao, S.K. Evaluation of DB-IEKF Algorithm Using Optimization Methods for Underwater Passive Target Tracking. Mobile Netw Appl 27, 1070–1080 (2022). https://doi.org/10.1007/s11036-021-01862-x
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DOI: https://doi.org/10.1007/s11036-021-01862-x