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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
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