Company LOGO Project Characterization Spring 2008/9 Performed by: Alexander PavlovDavid Domb Supervisor: Mony Orbach GPS/INS Computing System

Agenda 1. General overview 2. Our Project 3. Working environment 5. Timeline GPS/INS Computing System 4. Design Solution

GPS/INS Computing System General overview “Even Noah got no salary for the first six months partly on account of the weather and partly because he was learning navigation.” Mark Twain

GPS/INS navigation INS Characteristics Self-contained Provides accurate position and velocity over short time periods but slowly drifts over time More expensive and heavier than GPS GPS Characteristics Relies on GPS satellites: susceptible to jamming, RF interference, multipath and integrity problems Provides accurate position and velocity over longer time periods but has high frequency noise By combining the outputs of a GPS and an INS, the performance issues of both systems can be remedied. Both Global Positioning System (GPS) and Inertial Navigation System (INS) has its advantages and disadvantages. GPS/INS Computing System

Tightly Coupled INS/GPS GPS/INS Computing System IMU Strap down Inertial Navigation Algorithm State Update Filter Innovation Calculation GPS ∆V m ∆ θm∆ θm PIPI VIVI qIqI P INS/GPS V INS/GPS q INS/GPS P SAT  meas  PP VV qq b   SDINS.

Kalman & Particle filter Standard filter used in navigation systems is extended Kalman filter (EKF) Disadvantages of the extended Kalman filter: –EKF is not an optimal estimator for non-linear systems –Optimized for statistical noise only Particle filter can be used as an alternative to EKF, these to improve estimation's accuracy. Particle filters is a sophisticated model estimation technique based on simulation using sufficient number of samples GPS/INS Computing System

Theoretical Solution 0 Initialization 1 Particle Propagation 2 Particle Update & Normalization 3 State Estimation 4 Effective N calculation 5 D computation 6 Re-sampling 7 Regularization 8 Weight Re-computation GPS/INS Computing System  Implementing the tightly coupled INS/GPS navigation unit with the particle filter, according to algorithm developed in Technion.  The theoretical algorithm stages:

Project Goals Establishing the efficiency of the particle filter based, tightly coupled INS/GPS navigation unit realization. Designing an efficient real- time particle filter based, tightly coupled INS/GPS navigation unit. GPS/INS Computing System

GPS Computing System

General Project will be performed in 2 stages. First part in this semester. Project will be performed by several work groups Our group will implement Particle Propagation and State Estimation stages in this first part. Both stages need to be performed each 0.01 sec, regardless of other stages performance. GPS Computing System

Group Project Goals – PART 1 Learning GPS/INS navigation using Particle Filter algorithm Learning VHDL language Learning FPGA environment Implementation of Particle Propagation and State Estimation stages of algorithm GPS/INS Computing System

Working Environment

Gidel PROCStar II GPS/INS Computing System Up to 4 ALTERA Stratix II 60 to 180 FPGAs Five level memory structure (over 2.5GB) Typical system frequencies: Mhz. Flexible clocking system. Up to 695 available I/Os. Up to 5 PSDBs (ProcStar II Daughter Boards): Camera Links, machine I/Os and other interfaces. Expandable system: up to 96 DDR II I/Os between PROCStar II boards. Up to 660 Gbits per second connectivity between FPGAs.

Altera Stratix II GPS/INS Computing System 15,600 to 179,400 equivalent Logic elements Adaptive logic module (ALM), maximizes performance and resource usage efficiency Up to 9,383,040 RAM bits High-speed DSP blocks provide dedicated implementation of multiply-accumulate functions. Up to 12 PLLs (four enhanced PLLs and eight fast PLLs) per device. Support for high-speed external memory Megafunctions support

Altera Quartus II Provides a multiplatform design environment for all phases of FPGA design. GPS/INS Computing System

State Vector - X[1..18] Position X[1..3] Velocity X[4..6] Quaternion X[7..10] Accelerometers offset X[11..13] Gyroscope drift X[14..16] GPS clock error X[17..18] GPS/INS Computing System

Design guidelines Constrains:  large amount of calculations  Limited hardware  real-time results Possible solutions:  Pipelining  Large amount of parallel calculation units Selected solution:  Max. Parallel processing  What can’t be parallel – will be Pipelined. GPS/INS Computing System

Solution – Top design GPS/INS Computing System Weight vector Particles propagation unit State estimation unit Estimated State Vector [1..18] Estimated State Vector [1..18] xN Extended State Vector [1..18] Extended State Vector [1..18] Extended State Vector [1..18] Controller

Parallel VS. Pipeline GPS/INS Computing System Considerations:  Max. parallel processes will result in Min. calculation time.  Number of parallel processes is limited by the hardware.  Not all calculations have to be parallel in order to comply with 100 Hz.

Parallel VS. Pipeline GPS/INS Computing System Conclusions:  The number of L.E.’s (available on the FPGA) will determine the number of parallel processes in the “Particle propagation unit” and the “State estimation unit”.  To complete this number to N, we will pipeline the processes in those units.

Particles Propagation block GPS/NS Computing System

1 Particle Propagation block GPS/INS Computing System

State estimation block GPS/INS Computing System

q estimation block – N units GPS/INS Computing System

Not q components estimation block GPS/INS Computing System

Growth capability GPS/INS Computing System  Each design unit, deals with a number of particles.  The basic calculations in each unit, are designed for one particle and is then multiplied.  The same design can be implemented with “bigger” FPGAs, by increasing the number of multiplications and parallel processes.  This can result in lesser pipelines which means faster realization.  It can also implement bigger N.

GPS/INS Computing System Timeline

GPS/INS Computing System GANTT – PART A