Low-Complexity Square-Root Unscented Kalman Filter Design Methodology

Dutt, Rashi; Acharyya, Amit

doi:10.1007/s00034-023-02437-9

Cited by 3 publications

(1 citation statement)

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“…This is achieved through direct recursion updating in the form of the square root of the covariance matrix. Moreover, this approach guarantees the non-negativity of the covariance matrix, effectively prevents filter divergence, and fosters the convergence speed and numerical stability of the filter [35].…”

Section: Square Root Unscented Kalman Filtermentioning

confidence: 99%

Performance Enhancement and Evaluation of a Vector Tracking Receiver Using Adaptive Tracking Loops

Gao,

Chen,

Yan

et al. 2024

Remote Sensing

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The traditional receiver employs scalar tracking loops, resulting in degraded navigation performance in weak signal and high dynamic scenarios. An innovative design of a vector tracking receiver based on nonlinear Kalman filter (KF) tracking loops is proposed in this paper, which combines the strengths of both vector tracking and KF-based tracking loops. First, a comprehensive description of the vector tracking receiver model is presented, and unscented Kalman filter (UKF) is applied to nonlinear tracking loop. Second, to enhance the stability and robustness of the KF tracking loop, we introduce square root filtering and an adaptive mechanism. The tracking loop based on square root UKF (SRUKF) can dynamically adjust its filtering parameters based on signal noise and feedback Doppler error. Finally, the proposed method is implemented on a software-defined receiver (SDR), and the field vehicle experiment demonstrates the superiority of this method over other tracking methods in complex dynamic environments.

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Section: Square Root Unscented Kalman Filtermentioning

confidence: 99%