Parameterizable FPGA-Based Kalman Filter Coprocessor Using Piecewise Affine Modeling

Mills, Aaron; Jones, Phillip H.; Zambreno, Joseph

doi:10.1109/ipdpsw.2016.101

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…Equation (18) gives the simplified form of updated covariance equation. Figure 2 gives the detailed description of parameters used in Kalman filtering algorithm for tracking application.…”

Section: B Correction (Or) Data Assimilationmentioning

confidence: 99%

“…The prediction or model forecast process estimates the present system state and its uncertainty to the next system state, based on the dynamic model of the system. In this section, the equations of multidimensional Kalman filters are derived in the matrix form because the implementation of Kalman Filter in matrix equations reduces calculation time [18]. For example, the state vector that describes the airplane position, velocity and acceleration is nine-dimensional which is given by,…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of path tracking using Multi-Dimensional Kalman Filter based on state estimations

Babu¹,

Parthasarathy²

2021

Preprint

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<div>In the presence of uncertainty, one of the most difficult issues for tracking in control systems is to estimate the accuracy and precision of hidden variables. Kalman filter is considered as the widely adapted estimation algorithm for tracking applications. However, tracking of multiple objects is still a challenging task to achieve better results for prediction and correction. To solve this problem, a multi-dimensional Kalman filter is proposed using state estimations for tracking multiple objects. This paper also presents the performance analysis of proposed tracking model for linear measurements. The steady?state and covariance equations are derived and their co-efficients are updated. The multi-dimensional Kalman filter is evaluated mathematically for linear dynamic systems. The path tracking based on Kalman filter and multi-dimensional Kalman filter is also analyzed. The true and filtered responses of our proposed filtering algorithm for multiple object tracking are observed. The output covariance produces steady state values after four number of samples. The simulation results shows that the performance of our proposed filtering algorithm is 2x times effective than conventional Kalman filter for objects moving in linear motion and proves that proposed filter is suitable for real?time implementation.</div><div><br> </div>

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“…Equation (18) gives the simplified form of updated covariance equation. Figure 2 gives the detailed description of parameters used in Kalman filtering algorithm for tracking application.…”

Section: B Correction (Or) Data Assimilationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Analysis of path tracking using Multi-Dimensional Kalman Filter based on state estimations

Babu¹,

Parthasarathy²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Besides MPC, the Kalman Filter is another algorithm which often appears in control systems, and is also quite computationally expensive, especially for embedded systems. We proposed a mixed hardware-software FPGA-based Kalman Filter in prior work [16] which was successful in providing a speedup over software alone.…”

Section: Related Workmentioning

confidence: 99%

A software configurable and parallelized coprocessor architecture for LQR control

Zhang

Mills

Zambreno

et al. 2015

2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig)

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h i g h l i g h t s • A software-configurable FPGA-based LQR coprocessor is proposed for Cyber-Physical Systems. • Improved hardware-level parallelism is achieved by refactoring standard LQR equations. • Control of an inverted pendulum is used to compare software and hardware performance. • A 3.4 to 100 factor speedup over a 666 MHz embedded ARM processor is demonstrated. • Details are provided for interfacing the compute logic with a physical motor.

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“…In the following sections, which elaborate on the work from [48], we describe an implementation of the FPGA architecture supporting the piecewise affine approach, provide an assessment of the scalability of the implementation, and explore the performance tradeoffs between the typical mixed hardware-software EKF approach and the proposed piecewise affine approach. The majority of the literature available on hardware-accelerated Kalman Filtering is applicationspecific.…”

Section: Resultsmentioning

confidence: 99%

Design and implementation of an FPGA-based piecewise affine Kalman Filter for Cyber-Physical Systems

Mills¹

Self Cite

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Parameterizable FPGA-Based Kalman Filter Coprocessor Using Piecewise Affine Modeling

Cited by 9 publications

References 17 publications

Performance Analysis of path tracking using Multi-Dimensional Kalman Filter based on state estimations

Performance Analysis of path tracking using Multi-Dimensional Kalman Filter based on state estimations

A software configurable and parallelized coprocessor architecture for LQR control

Design and implementation of an FPGA-based piecewise affine Kalman Filter for Cyber-Physical Systems

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