Kalman Filter Embedded in FPGA to Improve Tracking Performance in Ballistic Rockets

Fonseca, João Viana da; Oliveira, Roberto Célio Limão de; Abreu, José Alano Peres de; Ferreira, Elma Vilma Silva; Machado, Madson Cruz

doi:10.1109/uksim.2013.149

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…These have been implemented for image processing [527,1582,1583], pattern recognition [476,1584], real-time processing [671,1477,1585], melanoma cancer detection [510,511,1586,1587], speech recognition [1587,1588] and so forth. Digital signal processing techniques implementations in FPGAs covers FFT [537,[1589][1590][1591][1592][1593], DWT [597,[1594][1595][1596][1597], time-to-digital converters [1598][1599][1600][1601][1602], DCT [580,[1603][1604][1605] and digital filters like FIR filter [56,612,[1606][1607][1608], Kalman filter [620,[1609][1610][1611], median filter [1612,1613], LMS [1614]…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Field Programmable Gate Array Applications—A Scientometric Review

2019

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“…For Simulation, real data taken from the paper [9]. The figure 2, its different value taken as input and in output we get very nearest estimation that both the lines are inclined.…”

Section: Real Data Simulationmentioning

confidence: 98%

“…So here we assumed data and get hardware analysis. Then after real data simulation is done but in that matrix size is 2x2 and data taken for rocket [9]. In that paper no hardware summary is given.…”

Section: B Hardware Utilization Summarymentioning

confidence: 99%

HDL implementation of Kalman Filter for GNSS receiver

Rawal

2015

2015 IEEE International Advance Computing Conference (IACC)

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This paper presents HDL implementation of Kalman Filter. Kalman Filter is a mathematical tool, which uses sequence of noisy measurement taken over time to predict unknown state vector parameter. In this paper implementation in HDL has been done using new method that is chebyshev inversion method. The approach of new method is for reducing hardware and complexity. Kalman Filter has very complex matrix calculation and matrix inversion. To perform matrix inversion in HDL(Hardware Description Language) using less hardware is difficult. Chebyshev method is less complex so hardware can reduced for calculation of Kalman Filter equations Here simulation is done in FPGA(Field Programmable Gate Array) Vertex-6.

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“…The basic Kalman Filter (KF) algorithm that is restricted for only linear models consists of two steps, a predict step followed by an update step. The KF and its variants have numerous applications in the physical sciences, primarily in the guidance [17], navigation and control of vehicles [18], time-series analysis [19] and robotics [20].…”

Section: Bayesian Filteringmentioning

confidence: 99%

Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter

Malek,

Bayen,

Glaser

2019

Preprint

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Accurate runoff forecasting is crucial for reservoir operators as it allows optimized water management, flood control and hydropower generation. Land surface models in mountainous regions depend on climatic inputs such as precipitation, temperature and solar radiation to model the water and energy dynamics and produce runoff as output. With the rapid development of cheap electronics applied in various systems, such as Wireless Sensor Networks (WSNs), satellite and airborne technologies, the prospect of practically measuring spatial Snow Water Equivalent in a dense temporal scale is increasing. We present a framework for updating the Precipitation Runoff Modeling System (PRMS) with Snow Water Equivalent (SWE) maps and runoff measurements on a daily timescale based on the Ensemble Kalman Filter (ENKF). Results show that by assimilating SWE daily, the modeled SWE gets updated accordingly, however no improvement is observed at the runoff model output. Instead, a deterioration consistently occurs. Augmenting the state space with model parameters and runoff model output allows for filter update with previous day measured runoff using the joint state-parameter method, and showed a considerable improvement in the daily runoff output of up to 60% reduction in RMSE for the wet water year 2011 relative to the no assimilation scenario, and improvement of up to 28% compared to a naive autoregressive AR(1) filter. Additional simulation years showed consistent improvement compared to no assimilation, but varied relative to the previous day autoregressive forecast during the dry year 2014.

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Kalman Filter Embedded in FPGA to Improve Tracking Performance in Ballistic Rockets

Cited by 8 publications

References 10 publications

Field Programmable Gate Array Applications—A Scientometric Review

Field Programmable Gate Array Applications—A Scientometric Review

HDL implementation of Kalman Filter for GNSS receiver

Daily Data Assimilation of a Hydrologic Model Using the Ensemble Kalman Filter

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