The GPS Toolkit
Explosive is perhaps the best term to describe the growth of the Global Positioning System (GPS) market in recent years. Contributing factors are numerous, and perhaps the most dramatic are economic: access to GPS is absolutely free and the cost of hardware continues to plummet. As a result, the GPS user can choose from a variety of devices that provide a position estimate. GPS has long been used, however, to explore topics beyond positioning; space weather, precise timing and continental drift are but three examples.
In order to use GPS for advanced topics or simply for improved positioning, the raw observations collected by the GPS receiver must be processed. In the past, the nuts and bolts of such processing have been left up to proprietary software. Now, a project called the GPS Toolkit, or GPSTk, is available under the LGPL to the Open Source and research communities. GPSTk is the by-product of GPS research conducted at the Applied Research Laboratories of the University of Texas at Austin (ARL:UT) since before the first satellite launched in 1978. It is the combined effort of many software engineers and scientists. Recently, the research staff at ARL:UT has decided to open source much of their basic GPS processing software as the GPSTk.
The Global Positioning System actually is a US government satellite navigation system that provides a civilian signal. As of this writing, the signal is broadcast simultaneously by a constellation of 29 satellites, each with a 12-hour orbit. From any given position on the Earth, 8–12 satellites usually are visible at a time.
Each satellite broadcasts spread spectrum signals at 1,575.42 and 1,227.6MHz, also known as L1 and L2, respectively. Currently, the civil signal is broadcast only on L1. The signal contains two components, a time code and a navigation message. By differencing the received time code with an internal time code, the receiver can determine the distance, or range, that the signal has traveled. This range observation is offset by errors in the (imperfect) receiver clock; therefore, it is called a pseudorange. The navigation message contains the satellite ephemeris, which is a numerical model of the satellite's orbit.
GPS receivers record, besides the pseudorange, a measurement called the carrier phase, or phase. The phase also is a range observation like the pseudorange is, except it has an unknown constant added to it, the phase ambiguity. It also is much smoother, having about 100 times less measurement noise than the pseudorange, which makes it useful for precise positioning. Because of the way it is measured, the phase is subject to random, sudden jumps. These discrete changes always come in multiples of the wavelength of the GPS signal and are called cycle slips.
The standard solution for the user location requires a pseudorange measurement and an ephemeris for each satellite in view. At least four measurements are required, as there are four unknowns: three coordinates of position plus the receiver clock offset. The basic algorithm for the solution is described in the official GPS Interface Control Document, ICD-GPS-200. The position solution is corrupted due to two sources of error, errors in the observations and errors in the ephemeris.
The GPS signal travels through every layer of the Earth's atmosphere. Each layer affects the signal differently. The ionosphere, which is the high-altitude, electrically charged part of the atmosphere, introduces a delay, and therefore a range error, into the signal. The delay is frequency-dependent, so it can be computed directly if you have data on both the GPS frequencies. There also is a delay due to the troposphere, the lower part of the atmosphere. This delay also can be modeled and removed. Many other errors are associated with the GPS signal. Multipath reflections and relativistic effects are two examples.
More precise applications reduce the effect of error sources by a technique referred to as differential GPS (DGPS). By differencing measurements simultaneously collected by the user and a nearby reference receiver, the errors common to both receivers (most of them) are removed. The result of DGPS positioning is a position relative to the reference receiver; adding the reference position to the DGPS solution results in the absolute user position.
The alternative to DGPS is to model and remove errors explicitly. Creating new and robust models of phenomena that affect the GPS signal is an area of active research at ARL:UT and other laboratories. The positioning algorithm can be used to explore such models. Essentially, the basic approach is to turn the positioning algorithm inside out to look at the corrections themselves. For example, observations from a network of receivers can create a global map or model of the ionosphere.
Practical Task Scheduling Deployment
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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- Stunnel Security for Oracle
- The Firebird Project's Firebird Relational Database
- My +1 Sword of Productivity
- Managing Linux Using Puppet
- SUSE LLC's SUSE Manager
- Murat Yener and Onur Dundar's Expert Android Studio (Wrox)
- Non-Linux FOSS: Caffeine!
- Doing for User Space What We Did for Kernel Space
- Google's SwiftShader Released
- Parsing an RSS News Feed with a Bash Script
With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide