Real time Kalman filter implementation on FPGA environment

Kara, Gokhan; Orduyilmaz, Adnan; Serin, Mahmut; Yıldırım, Alper

doi:10.1109/siu.2017.7960633

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Currently, feedback signals are implemented by tracking the phase of the oscillator by locking to the frequency of motion, using either lock-in amplifiers or, more recently, with a Kalman filter [31]. The Kalman filter, a filtering technique used in engineering applications [32][33][34][35], can be implemented in real-time to accurately estimate the state of the particle's position and velocity. This state information is then used to apply the modulating feedback signal [13,36].…”

Section: Introductionmentioning

confidence: 99%

Optimal control for feedback cooling in cavityless levitated optomechanics

et al. 2019

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We consider feedback cooling in a cavityless levitated optomechanics setup, and we investigate the possibility to improve the feedback implementation. We apply optimal control theory to derive the optimal feedback signal both for quadratic (parametric) and linear (electric) feedback. We numerically compare optimal feedback against the typical feedback implementation used for experiments. In order to do so, we implement a state estimation scheme that takes into account the modulation of the laser intensity. We show that such an implementation allows us to increase the feedback strength, leading to faster cooling rates and lower center-of-mass temperatures.

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Section: Introductionmentioning

confidence: 99%

Optimal control for feedback cooling in cavityless levitated optomechanics

et al. 2019

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“…The Kalman filter uses a series of measurements observed over time, which include statistical noise and other inaccuracies, to produce an estimate of unknown variables [619]. FPGAs were used for high performance/efficiency implementation of Kalman filters [620][621][622], IMU (Inertial Measurement Unit) Sensors fusion [623][624][625] and real-time filtering [626][627][628]. Median filters implementation in FPGAs includes application for image processing [629][630][631][632], low power median filters [633,634] and high speed/real-time applications [635][636][637].…”

Section: Digital Filtersmentioning

confidence: 99%

Field Programmable Gate Array Applications—A Scientometric Review

2019

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Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

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“…Kalman filters, the discrete-time counterparts to continuous-time Kalman-Bucy filters, have been used extensively in many aerospace and defence applications [40,41], including navigation systems for the Apollo Project and the wellknown Global Positioning System (GPS) [42]. FPGA based Kalman filters have also recently been developed for applications including antilock braking systems [43], radar tracking systems [44] and displacement measuring interferometry [45]. Kalman filtering has also been applied in various areas within the physical sciences such as atomic magnetometry [46], tracking dusty plasmas [47] and noise cancellation in gravitational wave detection [48].…”

mentioning

confidence: 99%

Real-time Kalman filter: Cooling of an optically levitated nanoparticle

2018

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We demonstrate that a Kalman filter applied to estimate the position of an optically levitated nanoparticle, and operated in real-time within a Field Programmable Gate Array (FPGA), is sufficient to perform closed-loop parametric feedback cooling of the centre of mass motion to sub-Kelvin temperatures. The translational centre of mass motion along the optical axis of the trapped nanoparticle has been cooled by three orders of magnitude, from a temperature of 300K to a temperature of 162 ± 15mK.

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Real time Kalman filter implementation on FPGA environment

Cited by 5 publications

References 4 publications

Optimal control for feedback cooling in cavityless levitated optomechanics

Optimal control for feedback cooling in cavityless levitated optomechanics

Field Programmable Gate Array Applications—A Scientometric Review

Real-time Kalman filter: Cooling of an optically levitated nanoparticle

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