Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    DSP Architectures of Covariance Intersection Fusion Algorithm for Automotive Sensor Fusion
    (Institute of Electrical and Electronics Engineers Inc., 2023) Praharshita, D.S.L.; Achala, G.; Srihari, P.; Shripathi Acharya, U.S.; Pardhasaradhi, B.
    The data fusion from sensors within the automotive vehicle is vital for improved accuracy and safety. The centralized and information matrix fusion (IMF) algorithms are famous for providing an optimal fusion estimate. However, the IMF is not viable in automotive sensor fusion applications due to the limited bandwidth and low hardware resources. Hence, distributed fusion technology is widely adopted in the automotive sensor applications to achieve high-speed and low-area realizations. This paper proposes three digital signal processing (DSP) architectures for covariance intersection (CI) fusion algorithm: Pipelined-traditional CI, adder-ladder CI, and pipelined adder-ladder CI. The proposed DSP architectures are evaluated with hardware resource consumption (multipliers, adders, and delays), maximum achievable frequency, and latency of the architecture. In addition, proposed CI algorithms for Digital Signal Processing (DSP) architectures are compared with IMF DSP architectures. The hardware resources and optimal pipeline stages required for CI with respect to N number of sensors are provided. The traditional pipeline algorithm requires N number of stages where as the proposed pipelined version of adder-ladder CI requires a N-1 pipeline stage with additional 7N-1 and 7N-3 delay elements for even and odd number of sensors to achieve the overall system operating frequency to an operation of multiplier. The proposed DSP architectures are suitable for automotive sensor fusion due to their high operating frequency and low hardware resources. © 2023 IEEE.
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    Performance Analysis of VMD to Decompose, Detrend and Denoise Power System Signals
    (Institute of Electrical and Electronics Engineers Inc., 2024) Rathod, N.S.; Shubhanga, K.N.
    Variational Mode Decomposition (VMD) has gained significant attention as an effective tool for signal processing, particularly in the fields of biomedical and speech processing. This paper explores the application of VMD to decompose complex power system signals which are non-stationary and nonlinear. Standard Empirical Mode Decomposition (EMD) and its variants often encounter challenges like mode mixing, boundary problems, and parameter dependency on noise levels, which may adversely affect the accuracy and reliability of the decomposition results. Since VMD effectively addresses these challenges by providing a more robust framework for decomposition, the resulting Intrinsic Mode Functions (IMFs) have been successfully used for mode estimation, detrending and denoising of power system signals. While denoising, to automate the process of identifying noisy IMFs reliably, Noise Identification Indices (NIIs) have been used. This study employs datasets from 3-machine, 9-bus power system and real-world ISO New England (ISO-NE) power system signals to demonstrate the efficacy and applicability of VMD in practical scenarios. These findings show up the potential application of VMD for analysing power system signals to advance signal processing techniques across various fields. © 2024 IEEE.