Report

Technion Israel Institute of Technology Spring 2013 Mid Presentation Supervisors: Rolf Hilgendorf, Debby Cohen Consultant: Eli Shoshan Students: Etgar Israeli, Shahar Tsiper Theory Project Definition and Goals Project Main Stages: ◦ Matlab Reconstruction ◦ AWR Activities – Part A ◦ AWR Activities – Part B ◦ A-Matrix Calibration ◦ MWC Development Support Systems Epilogue ~ ~ Multiband model: • N – max number of transmissions • B – max bandwidth of each transmission Goal: Blind detection + Recovery Minimal achievable rate: 2NB << fNYQ nTs 1 2Ts m sequences Expander q m sequences pm/ q (t ) nTs fp 1 2Ts ym[n] Δ q fs fp • Support S recovery • Signal reconstruction zS f ~ y f ~ AS z f zS f AS† y f ~ m sequences Reconstructor ~ Support Recovery ym[n] ~ zS f AS† y f AS ~ A z f zS f z f In theory there is a solid algorithm for building the A-Matrix. We use the fourier coefficients of the mixing series: We’re interested in finding the coeff. cil Therefore we’ll use: We can further simplify if the mixing series are step functions: We can now define an all constant A-Matrix: We can now use the same A matrix in time domain. Due to the invariance for iDTFT. After the Support Recovery process: A AS m L , mr r 2 N Using Moore-Penrose psuedo-inverse process for the matrix: Solving the problem: Matlab reconstruction algorithm AWR Activities A-matrix Calibration MWC development support systems (Labview programming Rolf/Idan) Understanding and fixing the Matlab code Learning AWR tool and Modeling MWC Deeper understanding of the main issues the system suffers from Developing calibration solutions for the system Implementing the solutions on the actual system ◦ Matlab Reconstruction ◦ AWR Activities – Part A ◦ AWR Activities – Part B ◦ A-Matrix Calibration ◦ MWC Development Support Systems We’ve developed signal comparison algorithm using cross-correlation. Main Issues: ◦ Support recovery is successful at approx. 80% of the runs (better % for qpsk than sinc) ◦ If ⊂ the recovery adds redundant harmonics ◦ If = time reconstruction still isn’t perfect Understand schematics of analog part of new MWC Get understanding of AWR tool Define method for input and output files ◦ Matlab , CSV etc. Enter first draft of MWC schematic Refine MWC design ◦ Get final spice models for all components ◦ Get model of card ◦ Enter final schematic ◦ Ensure synchronization between patterns ◦ Ensure synchronization with trigger ◦ How to create the input scenarios (AWR or matlab) ◦ Sampling rate for AWR simulation and for output Basic Verification of output data using matlab ◦ Is input mapped to output as expected ◦ Limits for input signal (saturation, undetectable due to noise) ◦ Anti-aliasing filter response Understanding the Physical Issues Using the AWR model output define AMatrix ◦ Perform developed procedure using model and matlab only ◦ Perform procedure using MWC development systems described below Phase Shifts inside the system: ◦ Signals enter with unknown phase into the analog card. We should make sure we know how to recover the signals with their original phases. ◦ Analog Low-Pass Filter causes unknown phase shifts between the different channels. ◦ Fixed phase shift between the mixer channels and the Expander Unit. Noise Sources: ◦ Impedance mismatches in the input cable end – attenuator is used, and acts as a noise source. ◦ Analog splitter before entering the different mixers provide as a noise source. ◦ Analog Low-Pass Filter causes noise. Modeling each part of the system independently, according to schematic Trying to develop specific solutions to each of the micro-problems Unknown ? Attenuator Noise nTs 1 2Ts m sequences Expander q ATT m sequences pm/ q (t ) nTs fp 1 2Ts ym[n] Δ Unknown phase Splitter Noise LPF – Noise & phase shift Phase shift Multiplying by a correction matrix before applying the original A-Matrix - 1 . ◦ In order to get 1 we planned to drive an impulse function into the system, and determine the impulse response for each Hardware Channel Applying a filter after multiplying the signal with the AMatrix - 2 ◦ We’ll use multiple known fixed carriers inputs (modulated sincs or simple sine waves) in order to devise the required 2 Thinking on new calibration methods after examining a full analog model or real MWC System - Still work in progress Synchronizing the A matrix’s via cyclic shifts to the mixer series - Might be necessary Data acquisition using NI converter with external sampling clock Immediate system based on Tabor AWG ◦ Load data from AWR simulation Final development system using NI AWG ◦ NI sync card and external clocking Matlab: ◦ Used for full modeling of the MWC system – Already given – need to be fixed ◦ Calibration Methods AWR: ◦ Implementing an analog model of the entire MWC system. ◦ Linking the analog AWR frontend and the digital Matlab backend Labview: ◦ Implementing calibration procedure Main missions Fix Matlab reconstruction algorithm Understanding the existing Matlab code and Sub-Nyquist Radar AWR Becoming proficient in AWR environment Understand schematics of analog part of new MWC Define method for the input and output betweem AWR amd Matlab Enter first draft of MWC schematic Entering second stage of project: Refine MWC design week1 2/6 week2 9/6 week3 16/6 week4 23/6 week5 30/6 week6 7/7 week7 14/7 Technion Israel Institute of Technology Spring 2013 Mid Presentation Supervisors: Rolf Hilgendorf, Debby Cohen Students: Etgar Israeli, Shahar Tsiper