Thursday, 20 October 2016

Drone as an application for Data Acquisition system (DAQ)

Drone as an application for Data Acquisition system (DAQ)
Introduction
Just recently a Pakistan Taliban leader Mullah Mansoor was killed in a drone attack in the Southern Pakistani province of Balochistan (Aljazeera). This is an example of the use of drones which have gone to revolutionized warfare in the 20th century. Apart from this drones have also become a common trend used by businesses for photography purposes, filming and boosting surveillance. The photography and filming industry has embraced the use of drones without following current laws governing the use of drones in the nation airspace (Rule). This was the core reason that enabled the coming up with an efficient DAQ system to locate such drones at all times (Patil, Kumar and Mhatre).
With the increase in the use of drones and its complexity, there is a need to develop and have a Data Acquisition system (DAQ) capable of ensuring data can be acquired efficiently. What then is a DAQ system? This is a system that measures electric and physical phenomenon and converts this into digitized data that can be accessed through the use of a computer (Goswani). Most DAQ consist of a computer that has the programmable software to access the data, sensors and measurement hardware (Judd). The digital signal collected can be stored in either temporary memory (RAM) or the magnetic memory meaning it can be accessed later on or used for future references. It is critical here to note that the DAQ system consists primarily of three elements which are also called embedded DAQ systems: an input and output subsystem, the controlling software and lastly the host computer through which the data will be accessed.
The Raspberry Pi belongs to a group of boards called the ARM boards which cost less in development of the embedded systems, has a high speed and flexibility (Goradiya and Pandya). The paper is going to look into how the Raspberry Pi can be used and integrated into a DAQ system so as to accurately pinpoint location of a source. There has been an increase in drones mainly domestic violating the rules of flying, therefore, creating a need to locate the drones accurately at any given time.
The Raspberry Pi as a DAQ system
Raspberry Pi contains an inbuilt ARM11 board which as mentioned before is highly effective. A USB port and it operates primarily in Linux (Priyanga and V.). Drones are usually developed with embedded sensors in their systems that enable them to perform tasks set out to be done such as surveying and collection of data. Examples of sensors that can be found in drones include accelerometers, inertial measurements unit, tilt sensors, current sensors and magnetic sensors (Winkler). The use of Raspberry pi as a DAQ system has been controversial in the drone industry, but its application has traversed and grown despite how lightweight it is. In our model, the Raspberry pi uses microphones as the superior form of sensors.  The microphones will be used to detect signals from a given range of frequencies. Microphones can be used to measure both the amplitudes and frequency characteristics.  The two are represented by the following measurements: Pascals (Pa) for sound pressure which can then be converted to decibels and Hertz (Hz) for frequency. When it comes to the selection of the microphone the phase characteristics should be excellent so that when integrated with the relevant software it can be used in the detection of source of sound (PCB PIEZOTRONICS). Other factors should also be considered before the selection of microphones and they include frequency response, polarization type, temperature range and type of response field.
The microphones which will be three in total will be fitted with a Global Positioning System (GPS) to assist in the triangulation of the source of sound that is the point at which the data is received. The location where the sensors will be placed is also helpful as they should be located in an area that is free from any interference such as winds and air pressure. Since drones are constantly on the move when they are up in the air, it is therefore imperative to devise a way of pinpointing the actual source or location of the drone at any given time. Drones are equipped with an algorithm that dictates its motions in the air. These paths can be linear or in circular orbits (Sujit, Saripalli and Sousa). Since each sensor will be detecting sound independently there is need for nonlinear equations gathered from each sensor to precisely determine the location. These equations are necessary to ensure that the location of the drones can be reached at accurately. Raspberry pi integrates the equation into its model to make sure that time is not wasted on calculations. Valuable methods of solving non linear circle equations include the python programs SciPy and NumPy.
These two are open sourced to the python program. The SciPy (Scientific Python) expands on the functions of the NumPy with other mathematical techniques and algorithms which include regression and the Fourier transformation (Shell). The NumPy (Numeric Python) offers ways of manipulating large matrices of data that is numeric in nature (Shell). Arrays are the core foundations for the NumPy. The two work together and are useful in analyzing and calculating non linear circle equations. In line with the collected data from the three sensors SciPy and NumPy can be used to find the source of the location through calculations. The raspberry pi can therefore use these final data to import to a host computer from which the data can be digitized and saved for future reference.
The use of Raspberry pi is cost efficient and as shown from the procedures followed until the end, it is highly useful as a DAQ system. As mentioned before the choice of the position of the sensors is important in determining the accuracy of the data collected. The following are some of the factors to consider: the sensors should be placed in an open space with little or no obstacles and barriers, a position that cannot be affected by harsh weather conditions such as winds and heavy rains, the sensors should be placed on a high ground to avoid instances of easy access which makes them vulnerable to attacks and lastly the sensors should be able to work daily without interruptions such as the recharging of the batteries.
Conclusion
Just a recap of how the Raspberry pi works: the microscope sensors are placed at a strategic position so as to detect frequencies and sound of the drones, the drones data are decoded through the use of SciPy and NumPy and the numerical data is digitized and saved on either a permanent or temporary memory location for importation to a computer with relevant software to open it. When the location of the drone has been established the data is saved with the exact time plus the longitudinal and latitudinal position at the exact time. The use of Raspberry Pi is an efficient method of DAQ system.







Works Cited
Aljazeera. Pakistan says US drone strike violated its sovereignty. 22 May 2016. 1 June 2016 .
Goradiya, Bhargav and H. N. Pandya. "Real time Monitoring & Data logging System using ARM architecture of Raspberry pi & Ardiuno UNO." July 2013. ijves.com. 1 June 2016 .
Goswani, Priyanka. "Data Acquisition System." www.slideshare.net. 1 June 2016 .
Judd, Bob. "Everything You Ever Wanted to Know about Data Acquisition." March 2016. www.ueidaq.com. 2 June 2016 .
Patil, Akshay, et al. "Data Acquisition System for an Unmanned Aerial Vehicle." May 2015. www.ijste.org. 2 June 2016 .
PCB PIEZOTRONICS. "Microphone Handbook." www.pcb.com. 1 June 2016 .
Priyanga, M. and Raja Ramanan V. "Unmanned Aerial Vehicle for Video Surveillance Using Raspberry Pi." 21 March 2014. www.rroij.com. 1 June 2016 .
Rule, Troy A. "Airspace in an age of drones." 2015. www.bu.edu. 2 June 2016 .
Shell, M. Scott. "An introduction to Numpy and Scipy." 17 June 2014. www.engr.ucsb.edu. 2 June 2016 .
Sujit, P. B., Srikanth Saripalli and J. B. Sousa. "An Evaluation of UAV Path Following Algorithms." 17 July 2013. www.nt.ntnu.no. 1 June 2016 .
Winkler, Chris. "Sensor solutions play critical roles in enabling innovation in drones." www.memsic.com. 1 June 2016.

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