wiki:Other/Summer/2015/aSDR1

Version 103 (modified by krajput96, 9 years ago) ( diff )

—

Indoor Localization

Table of Contents

  1. 2015 Winlab Summer Internship
    1. Projects
    1. Indoor Localization
    2. Introduction
      1. Motivation
      2. What is ORBIT Lab?
      3. Overall Approach
      4. Resources
      5. Procedure
      6. Weekly Presentations
      7. Team
    1. SDR in ORBIT: Spectrum Sensing
      1. Introduction
      2. Team
      3. Objectives
      4. Weekly Progress
      5. Experiments
    1. LTE Unlicensed (LTE-U)
      1. Introduction
      2. Objectives
      3. Theory
      4. Analyzing Tools
      5. Experiment 1: Transmit and Receive LTE Signal
      6. Experiment 2: The Waterfall Plot
      7. Experiment 3: eNB and UE GUI
      8. Experiment 4: Varying Bandwidths
      9. Experiment 5: Working with TDD or FDD
      10. Experiment 6: TDD with Varying Bandwidths
      11. Experiment 7: TDD Waterfall Plot
      12. Poster
      13. Members
      14. Materials
      15. Resources
    1. Distributed Simulation of Power Grid
      1. Introduction
      2. Objectives
      3. People
      4. Resources
    1. Context-Aware IoT Application on MobilityFirst
      1. Introduction
      2. Objectives
      3. System Architecture
      4. Network Diagram
      5. Experiment Tools
      6. Results
      7. Future Work
      8. Team member
    1. Real-Time Cyber Physical Systems Application on MobilityFirst
      1. Github Repo
      2. Introduction
      3. Preliminary Goal
      4. Outline of the Project
      5. Tasks
      6. Image Processing
      7. Weekly Summary
      8. Team
      9. Presentation Slides
    1. GNRS Assited Inter Domain Routing
      1. Introduction
    1. GNRS Management
      1. Introduction
      2. Work Milestones
    1. Effective Password Cracking Using GPU
      1. Introduction
      2. Objectives
      3. GPU
      4. Experiment
      5. Tools and Resources
  11. Body Sensor Networks
    1. Introduction
    2. Project Overview
    3. Data Collection
      1. Initial BCI data
    4. Data Analysis
    5. Tools/ Resources
    1. Unity Traffic Simulation
      1. Introduction
      2. Objectives
      3. People
    1. Mobile Security
      1. Introduction
      2. Motivation
    2. Resources
  14. Dynamic Video Encoding
    1. Introduction
    2. Goals
    3. Background Information
      1. Anatomy of a Video File
      2. What is a CODEC?
      3. H.264 Compression Algorithm
      4. Scalable Video Coding
      5. Network Emulator Test Results
      6. DASH Multi-Bitrate Encoding
      7. DASH Content Generation
      8. Bitrate Profiles
      9. Video Encoding Algorithms
      10. GPAC
    4. Presentations
    5. People

Introduction

The use of GPS services is growing just as fast as the development and accessibility of mobile devices. A GPS device, which used to be a significant investment, is now included in every smartphone that emerges on the market. These services have assisted many as they navigate themselves from place to place outdoors.

Although GPS is well-defined outdoors, localization indoors is still an active research problem. GPS signals indoors tend to be weaker; even if they are usable, the accuracy associated with GPS signals is not up to par. Large errors (on the order of meters) associated with GPS generally do not affect the user's ability to navigate to buildings, parks, landmarks, etc. Errors on the order of meters indoors, however, could mean that somebody is in a different room or different building altogether. A fine-grained service, down to the centimeter, is needed to localize indoors.

Motivation

An effective, low-cost, easy-to-implement solution to the indoor localization problem will have immediate impacts on everyday life, especially commercial retail. Based on movements of people in a store, retailers could determine where to place their best-selling items. They could place products effectively to accommodate shoppers and increase profits. In addition to commercial applications, indoor localization could help emergency responders efficiently respond to calls indoors, or help the elderly navigate inside a large building. Once the technology is fully developed, there are plenty of applications.

What is ORBIT Lab?

The ORBIT facility consists of a 20 x 20 grid of programmable radio nodes used to test wireless protocols and applications. Certain nodes in the facility, as well as certain sandboxes (part of the lab but not the grid), contain Universal Software Radio Peripherals (USRP), which are software defined radios that transmit and receive signals.

Founded in 2003 and launched in 2005, this lab provides the world's largest academic testbed for wireless communications. As of 2014, there are over 1000 registered users who have logged ~200,000 experimentation hours since the lab's founding.

Overall Approach

Trilateration is the method of determining location of an object through relative distances of points and geometry of spheres. It is a method that is used in Global Positioning Systems but we intend to use the same principle in indoor localization. As illustrated below, knowing an object's relative distance from Boise, Minneapolis and Tucson, one can derive that that object is in Denver. Similarly, knowing a person's location from three USRPs enables us to roughly estimate his position in an indoor place.

Resources

USRP:

ESG Signal Regenerator:

Wiserd:

Spectrum Analyzer:

Procedure

Using the USRPs as both transmitters and receivers, we measure the received signal power of a certain transmitted signal and plot this measurement against the distance between the node and transmitter. We hope to obtain many distance-power measurements, which would allow us to accurately predict the the distance (but not direction) between a transmitter and receiver based on the signal power.

Trilateration
The location of the transmitter and the various receivers
Experiment one: Detecting noise and signals using the nodes

The photos below show what occurs before the signal is transmitted and after the signal is transmitted. The ASCII art below gives us a general idea of the signal amplitude, which is then measure directly with OMF (ORBIT Management Framework) commands.



Experiment two: I/Q Samples
The noise in the ORBIT lab
the signal received by a node
Analysis: The left image shows signal reception when there is no signal transmitted. This is the noise that is present while we conduct experiments in the ORBIT room. The peak that is visible in the right image is the frequency that the transmitted signal is received at the receiver nodes.
IQ samples are often used in RF applications. In signal processing, I/Q samples are the real and imaginary components of a transmitted signal. The in-phase component is the "i" and the out of phase component, shifted by 90 degrees, is "q". These samples were used to calculate the power of the signal and figure out the relationship between signal power and distance.
Real and Imaginary Signals
Verifying Reception using FFT

Analysis: We used USRPs to collect the IQ samples that are portrayed in Figure 1. The graph has time on the x axis and amplitude on the y axis for both the real and imaginary components of the signal. Figure 2 verifies the reception of this signal using Fast Fourier Transform in Matlab. IQ samples are often used for modulation and demodulation of the signal that is being analyzed. We used these samples to calculate the power of the signal at known distances.

Experiment Three: Calculating power

Calculating signal amplitude and power is the next step in the process. The IQ samples, as mentioned above, gives the real and imaginary amplitude (y-axis) as it is related to time. Therefore, using the real amplitude on the x axis and the imaginary amplitude on the y-axis would result in the amplitude of the signal at a given time (hypotenuse). We can also calculate power from the similar calculation.
Amplitude using IQ samples
Power Program
Analysis: The figure on the left is the main principle of this power program. Using the phase, frequency and the phase angle of the sine wave, we can figure out the power of a signal by graphing the real amplitude on the x axis and the imaginary amplitude on the y axis. The hypotenuse is the amplitude of the signal. In this manner, we can figure out the power of the signal at each of the times in the domain using the program on the right. Using the average power, thereby cancelling out interference and propagation errors, we can figure out each of the powers at each distance between the receiver and the transmitter. The next step is to figure out the relationship between the two.

Experiment four: Relating SNR and power to distance

Signal to noise ratio is an important part in this localization process. To maintain a high SNR at higher distances means a greater chance of better localization data since there is more signal to collect and analyze. Even in the real world, people look for gadgets and devices with a higher SNR since that provides them with greater aural experience. Due to these reasons, it is very important to analyze SNR to validate the results and conclusions of the experiments.


Signal-to-Noise ratio versus distance and the fitted curve(red)
Power vs. Distance


Using the measured signal power, along with the distance between the transmitted and receiver, we obtained a signal amplitude-distance pair. We many of these pairs using different transmitters and receivers. We then plotted these items on a graph and found the exponential fit for the graph, as shown below.


Analysis: As shown in this experiment, the line of best fit follows a generally negative and exponential curve but some of the points are no where close to the curve. The scattering of the data points signify either an error in signal processing or simply not enough data points in the power graph. We believed the latter might have had a hand in this error.

Weekly Presentations

Presentations are done on a weekly basis before other research interns or professors. Presentations include the group's accomplishments over the past week as well as goals for the following week

​Week 2
​Week 3
​Week 4
​Week 5
​Week 6
​Week 7
​Week 8
​Week 9
​Final Week

Team
Rahul Hingorani
University of Michigan
Industrial/Electrical Engineering		 Vineet Shenoy
Rutgers University
Electrical and Computer Engineering 		Karan Rajput
Rutgers University
Electrical and Computer Engineering

Attachments (30)

Note: See TracWiki for help on using the wiki.