1
e Extended Kalman Filter (EKF) is the non-linear version of the Kalman Filter that is suited to work with systems whose model contains non-linear behavior. e algorithm linearizes the non-linear model at the current estimated point in an iterative manner as a process evolves. Although EKF can be used in a very wide range of applications, the Radar Tracker example was chosen to demonstrate Altera’s unique solution. Typically EKF requires significant computational efforts due to multiple matrix operations, including matrix inversion. is design demonstrates the possibility of offloading a CPU by executing a portion of the algorithm in FPGA fabric. EKF algorithm can be partitioned to two distinctive parts: the application specific part, which includes non-linear models of the system, and the generic part, which doesn’t change from system to system. e Extended Kalman Filter reference design utilizes hybrid implementation architecture, which maps the application specific part to the Cortex™-A9 CPU in order to preserve maximum development, tuning and debug flexibility. e generic part is implemented in the FPGA fabric to maximize system performance. It is mapped to the Matrix Processor IP, which is a generic co-processing unit that executes a variety of matrix processing operations. e Matrix Processor can be run-time programmable to execute a sequence of different matrix operations using variable matrix sizes. April 2014 Altera Corporation 101 Innovation Drive San Jose, CA 95134 www.altera.com © 2014 Altera Corporation. All rights reserved. ALTERA, ARRIA, CYCLONE, HARDCOPY, MAX, MEGACORE, NIOS, QUARTUS and STRATIX words and logos are trademarks of Altera Corporation and registered in the U.S. Patent and Trademark Office and in other countries. All other words and logos identified as trademarks or service marks are the property of their respective holders as described at www.altera.com/common/ legal.html. Altera warrants performance of its semiconductor products to current specifications in accordance with Altera’s standard warranty, but reserves the right to make changes to any products and services at any time without notice. Altera assumes no responsibility or liability arising out of the application or use of any information, product, or service described herein except as expressly agreed to in writing by Altera. Altera customers are advised to obtain the latest version of device specifications before relying on any published information and before placing orders for products or services. gen-1011-1.0 ISO 9001:2008 Registered Highly parameterizable Extended Kalman Filter engine Radar tracker hardware demonstration Overall system performance increase (> x2) Small FPGA footprint (< 10% Cyclone® V SoC) FPGA floating point matrix processor ARM Cortex-A9 and FPGA fabric performance Altera’s System-in-the-Loop with MATLAB® Designed with Simulink®/Advanced DSP Builder Extended Kalman Filter Reference Design Datasheet Description Features Applications Radar and Sonar Guidance and Navigation Inertial Navigation Sensors Sensor Fusion Motor Control Figure 1: Extended Kalman Filter Hybrid Architecture h(x) Measurements Inputs Estimates Outputs H A Predicted Measurement Predicted States f(x) H Jacobian A Jacobian Matrix Processor Figure 2: Extended Kalman Filter Reference Design Block Diagram Targets Simulator Radar Simulator System-in- the-Loop Radar Measurements Target Estimated States Matlab API (System Console) Radar Tracker. Extended Kalman Filter Visualization System-in- the-Loop Target Management Extended Kalman Filter Mathworks Matlab For additional information, please contact us at [email protected] or contact your local Altera sales representative.

Extended Kalman Filter - Intel FPGA and SoC · PDF fileAltera’s System-in-the-Loop with MATLAB® Designed with Simulink®/Advanced DSP Builder Extended Kalman Filter ... Extended

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Page 1: Extended Kalman Filter - Intel FPGA and SoC · PDF fileAltera’s System-in-the-Loop with MATLAB® Designed with Simulink®/Advanced DSP Builder Extended Kalman Filter ... Extended

The Extended Kalman Filter (EKF) is the non-linear version of the Kalman Filter that is suited to work with systems whose model contains non-linear behavior. The algorithm linearizes the non-linear model at the current estimated point in an iterative manner as a process evolves. Although EKF can be used in a very wide range of applications, the Radar Tracker example was chosen to demonstrate Altera’s unique solution. Typically EKF requires significant computational efforts due to multiple matrix operations, including matrix inversion. This design demonstrates the possibility of offloading a CPU by executing a portion of the algorithm in FPGA fabric.

EKF algorithm can be partitioned to two distinctive parts: the application specific part, which includes non-linear models of the system, and the generic part, which doesn’t change from system to system.

The Extended Kalman Filter reference design utilizes hybrid implementation architecture, which maps the application specific part to the Cortex™-A9 CPU in order to preserve maximum development, tuning and debug flexibility. The generic part is implemented in the FPGA fabric to maximize system performance. It is mapped to the Matrix Processor IP, which is a generic co-processing unit that executes a variety of matrix processing operations. The Matrix Processor can be run-time programmable to execute a sequence of different matrix operations using variable matrix sizes.

April 2014 Altera Corporation

101 Innovation DriveSan Jose, CA 95134www.altera.com

© 2014 Altera Corporation. All rights reserved. ALTERA, ARRIA, CYCLONE, HARDCOPY, MAX, MEGACORE, NIOS, QUARTUS and STRATIX words and logos are trademarks of Altera Corporation and registered in the U.S. Patent and Trademark Office and in other countries. All other words and logos identified as trademarks or service marks are the property of their respective holders as described at www.altera.com/common/legal.html. Altera warrants performance of its semiconductor products to current specifications in accordance with Altera’s standard warranty, but reserves the right to make changes to any products and services at any time without notice. Altera assumes no responsibility or liability arising out of the application or use of any information, product, or service described herein except as expressly agreed to in writing by Altera. Altera customers are advised to obtain the latest version of device specifications before relying on any published information and before placing orders for products or services. gen-1011-1.0

ISO9001:2008Registered

■ Highly parameterizable Extended Kalman Filter engine ■ Radar tracker hardware demonstration■ Overall system performance increase (> x2)■ Small FPGA footprint (< 10% Cyclone® V SoC)■ FPGA floating point matrix processor■ ARM Cortex-A9 and FPGA fabric performance■ Altera’s System-in-the-Loop with MATLAB®■ Designed with Simulink®/Advanced DSP Builder

Extended Kalman FilterReference Design Datasheet

Description Features

Applications

■ Radar and Sonar■ Guidance and Navigation ■ Inertial Navigation Sensors■ Sensor Fusion■ Motor Control

Figure 1: Extended Kalman Filter Hybrid Architecture

h(x)

MeasurementsInputs

EstimatesOutputs

H A

Pred

icted

Mea

sure

men

t

Pred

icted

Stat

es

f(x)

HJacobian

AJacobian

Matrix Processor

Figure 2: Extended Kalman Filter Reference Design Block Diagram

TargetsSimulator

RadarSimulator

System-in-the-Loop

Radar Measurements

Target Estimated States

Matlab API (System Console)

Radar Tracker. Extended Kalman Filter

Visualization

System-in-the-Loop Target

ManagementExtendedKalman

Filter

MathworksMatlab

For additional information, please contact us at [email protected] or contact your local Altera sales representative.