Analysis Of High Frequency Digital Signals Computer Science Essay

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This paper presents work carried out in developing a user-friendly system to observe and analyze High Frequency (HF) signals. The system consists of two main parts namely Offline HF Analysis and Real Time (Online) HF Analysis. With a frequency spectrum of 3MHz to 30MHz, HF communication is the only way of achieving global communication coverage without using expensive terrestrial and satellite infrastructure. However, HF technology is still limited and very much in its infancy. This research involved the development of a system that is capable of analyzing the characteristics of HF signals such as the frequency, power of the received signals, the signal modulation type and the bit rate of the signal using Matlab. The Graphic User Interface (GUI) of the system is also designed for ease of use.

High Frequency is widely used in long distance communication and very popular among amateur radio operators and military communication [1]. High Frequency is one of the radio spectrum with frequencies between 3 to 30MHz and combined with the use of the ionosphere (a layer of ionization gases that resides between 100 and 700km above the earth's surface). The ionosphere often reflects HF signal.

Previous research that has been done is the measurement of the total power from the HF receiving antenna. Wide-band RF systems and receivers must be capable of handling the total power generated by all signals, noise, and interference within their operating bandwidth. In addition, spectrum users must know the amplitude distribution of specific types of signals to assess the reliability of their reception in the presence of other signals, noise, and interference [2].

Previous research has also been done to improve and analyze the HF signals in offline mode. Signal information such as frequency, energy, the modulation type of the signal and the bit rate has also been determined [3]. Another research conducted by [5] gives a proposal, which is only one of many possible, for using the oblique sounding technique on HF radio circuits to obtain more reliable long distance communication.

In [6], it proposes an algorithm to increase the performance of high frequency (HF) wireless networks. The solution and results presented in the research contribute to achieve a reliable communication system to support real time video packet communications, encoded at about 8 kbps using the H.264 video coding standard. In addition to allowing civil/amateur communications, HF bands are also used for long distance wireless military communications.

Results obtained from this research shows that this system is capable to analyze the characteristics of the received HF radio signals. This paper presents a system for signal analysis in offline and real time mode. In offline mode, user can analyze the signal by using HF signal in '*.wav' file form. While in online mode, the system requires to be interfaced with other hardware whereas HF radio will be connected to the personal computer and an online signal will be observed, recorded and analyzed.

Scope of Work

In this system, HF digital signals will be analyzed. Digital communications include systems where relatively high frequency analog carriers are modulated by relatively low-frequency digital information signals and systems involving digital transmission. In digital modulation, the information signal is digital. Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), Phase Shift Keying (PSK) and Quadrature Amplitude Modulation (QAM) are all forms of digital modulation [4]. This system can be used to determine the modulation type and the bit rate of the signal by using the periodogram and spectrogram technique. Noise tends to have a great effect on the HF bands. Concerning to the issues, the received signal will be analyzed using Finite Impulse Response (FIR) filter.

Methodology

In this section, the process to develop this system is explained in details. The system is developed using Matlab software. This system is designed so that it can analyze the *.wave file for offline analysis. For real time analysis, user needs to connect the system (using personal computer) with HF radio. For this project, YAESU VR5000 is used.

Finite Impulse Response (FIR) digital filter is used in this system. Filter is a device or process that removes some unwanted component from signal. Most often, this means removing some frequencies and not others in order to suppress interfering signals and reduce noise. In practice, it can be classified into a number of different bandforms describing which frequencies the filter passes (the passband) and which it rejects (the stopband). The filters known as Low-pass filter (LPF), High-pass filter (HPF), Band-pass filter (BPF) and Band-stop filter [6].

Many digital filters are based on the Fast Fourier transform, a mathematical algorithm that quickly extracts the frequency spectrum of a signal, allowing the spectrum to be manipulated (such as to create band-pass filters) before converting the modified spectrum back into a time-series signal. Commonly, it has two categories of digital filter used in digital signal processing there are infinite impulse response (IIR) filters and finite impulse response (FIR) filters. For this project, FIR filter has been used, it preferable than infinite impulse response (IIR) filter [9]. Whereas, FIR filters has it useful properties that suitable for phase-sensitive applications, it correspond to equal delay at all frequencies so phase change proportional to frequency.

The window design method, considering as windowed versions of ideal finite filters for implementation because actual signals are finite in time. By truncating or windowing a FIR filter, the frequency response of the FIR filter is convolved with the frequency response of the window. Examples of the window function are Boxcar, Hamming, Triangular and Blackman.

In this system the technique to analyze the frequency spectrum is periodogram (Power Spectral Estimations). The periodogram, is from Fast Fourier Transform (FFT) that is obtained by squared the magnitude of FFT divided by the length of series, N [10].

[1]

The periodogram is often computed from a finite-length digital sequence using the fast Fourier transform (FFT). The raw periodogram is not a good spectral estimate because of spectral bias and the fact that the variance at a given frequency does not decrease as the number of samples used in the computation increases. The spectral bias problem arises from a sharp truncation of the sequence, and can be reduced by first multiplying the finite sequence by a window function which truncates the sequence gracefully rather than abruptly [11]. The periodogram technique only shows the frequencies at the overall time and not at the particular time. This is one of the limitations of periodogram technique. Hence, the spectrogram technique will be used in order to make this system more efficient [12]. Time- frequency is a technique that will show a signal in terms of power versus time-frequency. This technique is used to determine the frequency at discrete time. It will measure how the signal frequency will vary with time. The spectrogram is used in time-frequency domain. It will clearly show what happen at the particular time. Spectrogram also can determine the modulation type of signal whether it is ASK, FSK, multi FSK, multi-channel ASK or Morse code [12]. In the spectrogram, y-axis displays the spectra frequency that is illustrated by Fourier transform. The x-axis displays the time for the signal. Red color represents the highest energy in a spectrogram followed by orange, yellow, green, cyan and blue. For this project, Matlab GUI has been designed for ease of use. The flowchart of the overall procedure is shown below:

Figure 1. The flowchart of the system

Result And Discussion

The performance of the proposed method is tested on a personal computer with Matlab Version 7.7 (R2008b) Software. In order to use the system, user has to type 'real-time' at Matlab command window. The GUI of the system will appear on the screen as shown in Figure 2.

Figure 2. Home menu of the system

This system provides two modes of analysis 'Offline System Analysis' and 'Online System Analysis'. The 'EXIT PROGRAM' is to close the system after the system has been used.

Offline System Analysis

In offline analysis, user has to click 'Offline System Analysis' button as shown in Figure 2 and the offline analysis program menu will appear as shown in Figure 3. For this system, user has to follow the procedure in the arrangement from step 1 to step 6. For step 1; user needs to initialize the parameter to do the analysis of the HF digital signal. User must fill in the text box that requires sampling frequency and the name of the file that will be analysed. Then, click 'OK' button. Then the original waveform of the signal will appear and this indicates that the signal has been loaded into the system. Example of the original waveform is shown in Figure 4.

Figure 3. Offline System Analysis menu

Figure 4. Original HF signal

From Figure 3, after the file has been loaded, user can playback the signal whereas user can hear the signal sound. User has to fill in the lower time and upper time limit of the signal.

User can proceed to step Filter Signal in Figure 3. For this part, there are 4 types of Finite Impluse Response (FIR) filter that can be used including bandpass, lowpass, highpass and bandstop filter. User is required to fill in the required values. To run the filtering process, click 'OK' button. The filtered waveform will appear as shown in Figure 5.

Figure 5. The wave of signal after filtered

The next part is to perform spectrum analysis. In this section, user can determine the window function either to use Boxcar or Hamming window function. Then by clicking 'OK', the periodogram will be displayed as shown in Figure 6. From Figure 6, it can be seen that the signal contains frequency at 2456 Hz.

Figure 6. The periodogram of sampled signal analysis

The spectrogram technique can also be used to ensure the type of signal modulation. User needs to go to 'Perform Time-Frequency Analysis' section. In this section, user can choose either Contour or Waterfall plot. Then by clicking 'OK' button the spectrogram will be displayed as shown in Figure 7. From Figure 7, there is only one frequency exists at a constant time. Hence, it can be concluded that it is the Amplitude Shift Keying (ASK) signal. The bit-rate of the signal can be calculated by this equation;

Bit rate of the signal ≈ 1/(bit duration) (4)

Bit rate ≈ 1/ 50ms = 20bps

Figure 7. The spectrogram of the signal with contour plot choice

For the last analysis user can estimate the value of frequency contained in the signal. User has to enter the reference level. By clicking 'OK' button, the results will appear as shown in Figure 8 and Figure 9. Figure 8 is the estimation frequency of the signal. From figure 9, shows the signal frequency at the highest normalized energy.

Figure 8. The estimate value of the spectrum analysis

Figure 9. The estimate value of time-frequency analysis

Result for Frequency Shift Keying (FSK)

Figure 10 shows that the signal has two frequencies. From the periodogram in Figure 10, it illustrates that the signal is FSK since the two frequencies exists at different time.

Bit rate ≈ 1/ 50ms = 20 bps

Figure10. Periodogram of FSK

Figure 11. Spectrogram of FSK

Result for Amplitude Shift Keying (ASK)

Figure 12 shows that the signal frequency exists at 3147 Hz and the amplitude of the signal varies. Since only one frequency exists in the signal, the signal might be ASK or Morse code. Result for time frequency analysis is shown in Figure 13. It consists of single frequency with the amplitude varies at a constant time. Therefore, the signal is ASK.

Bit rate ≈ 1/ 100ms = 10 bps

Figure 12. Periodogram of ASK

Figure 13. Spectrogram of ASK

Result for Phase Shift Keying (PSK)

Figure 14 illustrates that the signal frequency exists at 984 Hz and 2100 Hz. Since the phase of frequency varies in the signal, the signal might be PSK. The spectrogram is shown in Figure 15. From the figure, it shows that the signal is PSK. It consists of two frequencies changing at a different phase.

Bit rate ≈ 1/ 50ms = 20 bps

Figure 14. Periodogram of PSK

Figure 15. Spectrogram of PSK

Online System Analysis

User has to click 'Online System Analysis' button as shown in Figure 2 and the online analysis program menu will appear as shown in Figure 16. For online analysis, this system must be interfaced with hardware whereas user has to connect HF radio with the PC as shown in Figure 16.

Figure 16. The Online System Analysis Menu

Figure 17. Connection between HF radio and PC

For this system, user has to follow the procedures.

Step 1: It requires user to initialize the parameters for further analysis. User must fill in the sampling frequency of the signal. Hence, the real time signal will appear as shown in Figure 18. Use has the save the desired signals using sound recorder in *.wav file.

Step 2: User has to fill in the parameters required to filter the signals.

Step 3: User can choose whether to analyze the signal using Real Time Spectrum Analysis.

Step 4: User can choose whether to analyze the signal using Real Time Frequency Analysis

Step 5: User can determine the frequency of the signal by providing the signals' parameters.

Figure 18. Online signal input system

Figure 19. Sound recorder

For Filter Signal procedure, there are 4 types of FIR filter that can be used such as bandpass, lowpass, highpass and bandstop filter. Example of filtered waveform in real time mode is as shown in Figure 20.

Figure 20. The filtered signal wave

The next section is to display the periodogram of the real time signal as shown in Figure 21. From the figure, it shows that the signal contains many frequencies but the highest is at 1050 Hz.

Figure 21. The periodogram of the real time spectrum

From the time-frequency analysis user can choose either 'Contour' plot or 'Waterfall' plot. The contour plot spectrogram is as shown in Figure 22. The spectrogram shows the spectral energy of the signal. Red colour represents the highest energy followed by orange, yellow, green, cyan and blue. The spectrogram in Figure 22 shows multiple frequencies and it proves that the modulation type is Dual Tone Multiple Frequency (DTMF). The bit rate of the signal is;

Bit rate ≈ 1/ 650ms = 1.54bps

Figure 22. The spectrogram of time-frequency analysis

The signal frequency can also be estimated. Figure 22 shows the signal frequency at the highest normalized energy.

Figure 23. The estimate signal frequency of the signal

Conclusion

A user friendly system has been successfully developed to analyze HF signals in real time and offline mode for the utilization in communication technology. Results from the research revealed that the proposed system is capable to be used to analyze the HF signals.

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