Idea Transcript
Spectral Analyzer Design Using LabVIEW Fine Dwinita Aprilyanti (13304080) Supervisor: Prof. Harijono A. Tjokronegoro Study Program of Engineering Physics, Faculty of Industrial Technology Bandung Institute of Technology Labtek VI Building, Jl. Ganesha 10, Bandung 40132, Indonesia Phone: +62 22 250 4424
Keyword: power spectral density, classic spectral estimation method, sampling frequency, dual channel signal input, windowing, Fast Fourier Transform. I. Abstract Spectral analyzer is a tool used for analyzing signals that store the information from measurement process. This is done by achieving the spectral density of those signals. Signal analyzing technique using classic method can be divided into 2 types; the first is periodogram technique or also known as direct method, and correlogram technique or also known as indirect method. In this project I will simulate a periodogram and correlogram spectral analyzer using software LabVIEW. To obtain spectrum of the signal, some steps is needed. The first step is signal conditioning process. A filter is used to reduce the noise that comes along with the information needed. Type and order of filter are the important of this step. The next step is converting the analog signal into digital form, by noticing the sampling frequency, and uses the filter to remove any alias. The next step is windowing, or multiplies the signal by window function. After this step, the signal is transformed using Fast Fourier Transform to obtain the signal components in frequency domain. Spectral density of the signal is the square form of frequency domain signal. If there are two input signals, the cross spectral density is needed to give additional information. This is obtained by cross correlating the signals. It is interesting to establish the joint-cooperation between ITB and university in Germany to improve practical study and students experiment activities. I am looking forward to continue my study in acoustics signal processing in Gemany. II. Objective The objective of this project is to simulate a dual channel digital spectral analyzer using LabVIEW software. III. Methodology A. Fast Fourier Analysis
Transform-based
Signal
The signals are functions of one or more independent variables and typically contain information about the behavior or nature of some phenomenon. When we measure something, the result signal usually represented as time-domain. Sometimes it is difficult to get the information from this signal form, thus we need to transform it into frequency-domain signal. The tool for this task is called Fourier Transform. For discrete signal, the transformation is called Discrete Fourier Transform or DFT.
IV. Progress In practice, DFT needs several time to complete its algorithm. To reduce the time, we can use the Fast Fourier Transform, one of approximation technique to solve the Fourier transform found by J.W Cooley and J.W.Tukey. This technique use the simetric and periodicity characteristic of the signal. Thus, to run this algorithm, the signal must have 2N points of data, where N is integer. If there are no sufficient data, we can resample the signal or add some zero to the signal. This process is called zero padding, and can cause the spectral to have higher resolution.
The spectral analyzer in this project will be designed to meet these specification:
1.
2. 3.
4. For non-stationary signals, we must do the FFT algorithm in every range where the data can be assumed as stationary signal (usualy at range 25ms). This method is also called Short Time Fourier Transform (STFT). B. Periodogram Technique In periodogram technique or also known as direct method, the signal is directly multiplied with window function. Then, the windowed signal is transformed into frequency-domain using FFT. To obtain the power spectral density, the signal is multiplied with its conjugate. If we want to see the cross power spectral density, we must multiply one of the signal with the other’s conjugate. Since the phase information is lost when the signal is being multiplied with its conjugate, the phase information can be achieved from the FFT result. C.
Correlogram Technique
In correlogram technique, the signal is correlated with itself before multiplied with window function. If we want to achieve the cross power spectral density, we must crosscorrelate both of the signal we have. After that, the signal is transformed using FFT. The spectral density obtained from this transform has already included phase information.
5. 6.
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8.
It has dual channel input that can be used to analyze non-stationary digital signal. It has input range until 20KHz, which is a range for audio signal. It uses periodogram and correlogram techniques as spectral estimation methods. It gives output Power Spectral Density from each input signal and Cross Power Spectral Density from crosscorrelation of both input signal, which are displayed as a 3D spectogram, and also the phase information of both signals. It has an optional input range from 1x - 100x. It has automatic and manual zero padding and resampling features. The operator can choose the sampling frequency by himself. It has the following window functions option: Hanning, Hamming, Rectangle, Blackman, BlackmanHarris, Gaussian, and Triangle. It uses LPF with programmable cutoff frequency.
V. References [1] Alan V. Oppenheim and Alan S. Wilsky, Signals and Systems. 1995. India: Prentice-Hall. [2]
Harijono A. Tjokronegoro. Pengolahan Sinyal. 2001. Bandung: Penerbit ITB.
[3] Harijono A. Tjokronegoro. Analisis Spektral Digital. 2004. Bandung: Penerbit ITB [4] National Instruments Tutorial : The Fundamentals of FFT-Based Signal Analysis and Measurement in LabVIEW and LabWindows/CVI
Spectral Analyzer Design Using LabVIEW Fine Dwinita Aprilyanti (13304080) Supervisor: Prof. Harijono A. Tjokronegoro Study Program of Engineering Physics, Faculty of Industrial Technology Bandung Institute of Technology Labtek VI Building, Jl. Ganesha 10, Bandung 40132, Indonesia Phone: +62 22 250 4424
Keyword: power spectral density, classic spectral estimation method, sampling frequency, dual channel signal input, windowing, Fast Fourier Transform. I. Abstract Spectral analyzer is a tool used for analyzing signals that store the information from measurement process. This is done by achieving the spectral density of those signals. Signal analyzing technique using classic method can be divided into 2 types; the first is periodogram technique or also known as direct method, and correlogram technique or also known as indirect method. In this project I will simulate a periodogram and correlogram spectral analyzer using software LabVIEW. To obtain spectrum of the signal, some steps is needed. The first step is signal conditioning process. A filter is used to reduce the noise that comes along with the information needed. Type and order of filter are the important of this step. The next step is converting the analog signal into digital form, by noticing the sampling frequency, and uses the filter to remove any alias. The next step is windowing, or multiplies the signal by window function. After this step, the signal is transformed using Fast Fourier Transform to obtain the signal components in frequency domain. Spectral density of the signal is the square form of frequency domain signal. If there are two input signals, the cross spectral density is needed to give additional information. This is obtained by cross correlating the signals. It is interesting to establish the joint-cooperation between ITB and university in Germany to improve practical study and students experiment activities. I am looking forward to continue my study in acoustics signal processing in Gemany. II. Objective The objective of this project is to simulate a dual channel digital spectral analyzer using LabVIEW software. III. Methodology A. Fast Fourier Analysis
Transform-based
Signal
The signals are functions of one or more independent variables and typically contain information about the behavior or nature of some phenomenon. When we measure something, the result signal usually represented as time-domain. Sometimes it is difficult to get the information from this signal form, thus we need to transform it into frequency-domain signal. The tool for this task is called Fourier Transform. For discrete signal, the transformation is called Discrete Fourier Transform or DFT.
IV. Progress In practice, DFT needs several time to complete its algorithm. To reduce the time, we can use the Fast Fourier Transform, one of approximation technique to solve the Fourier transform found by J.W Cooley and J.W.Tukey. This technique use the simetric and periodicity characteristic of the signal. Thus, to run this algorithm, the signal must have 2N points of data, where N is integer. If there are no sufficient data, we can resample the signal or add some zero to the signal. This process is called zero padding, and can cause the spectral to have higher resolution.
The spectral analyzer in this project will be designed to meet these specification:
1.
2. 3.
4. For non-stationary signals, we must do the FFT algorithm in every range where the data can be assumed as stationary signal (usualy at range 25ms). This method is also called Short Time Fourier Transform (STFT). B. Periodogram Technique In periodogram technique or also known as direct method, the signal is directly multiplied with window function. Then, the windowed signal is transformed into frequency-domain using FFT. To obtain the power spectral density, the signal is multiplied with its conjugate. If we want to see the cross power spectral density, we must multiply one of the signal with the other’s conjugate. Since the phase information is lost when the signal is being multiplied with its conjugate, the phase information can be achieved from the FFT result. C.
Correlogram Technique
In correlogram technique, the signal is correlated with itself before multiplied with window function. If we want to achieve the cross power spectral density, we must crosscorrelate both of the signal we have. After that, the signal is transformed using FFT. The spectral density obtained from this transform has already included phase information.
5. 6.
7.
8.
It has dual channel input that can be used to analyze non-stationary digital signal. It has input range until 20KHz, which is a range for audio signal. It uses periodogram and correlogram techniques as spectral estimation methods. It gives output Power Spectral Density from each input signal and Cross Power Spectral Density from crosscorrelation of both input signal, which are displayed as a 3D spectogram, and also the phase information of both signals. It has an optional input range from 1x - 100x. It has automatic and manual zero padding and resampling features. The operator can choose the sampling frequency by himself. It has the following window functions option: Hanning, Hamming, Rectangle, Blackman, BlackmanHarris, Gaussian, and Triangle. It uses LPF with programmable cutoff frequency.
V. References [1] Alan V. Oppenheim and Alan S. Wilsky, Signals and Systems. 1995. India: Prentice-Hall. [2]
Harijono A. Tjokronegoro. Pengolahan Sinyal. 2001. Bandung: Penerbit ITB.
[3] Harijono A. Tjokronegoro. Analisis Spektral Digital. 2004. Bandung: Penerbit ITB [4] National Instruments Tutorial : The Fundamentals of FFT-Based Signal Analysis and Measurement in LabVIEW and LabWindows/CVI
Spectral Analyzer Design Using LabVIEW Fine Dwinita Aprilyanti (13304080) Supervisor: Prof. Harijono A. Tjokronegoro Study Program of Engineering Physics, Faculty of Industrial Technology Bandung Institute of Technology Labtek VI Building, Jl. Ganesha 10, Bandung 40132, Indonesia Phone: +62 22 250 4424
Keyword: power spectral density, classic spectral estimation method, sampling frequency, dual channel signal input, windowing, Fast Fourier Transform. I. Abstract Spectral analyzer is a tool used for analyzing signals that store the information from measurement process. This is done by achieving the spectral density of those signals. Signal analyzing technique using classic method can be divided into 2 types; the first is periodogram technique or also known as direct method, and correlogram technique or also known as indirect method. In this project I will simulate a periodogram and correlogram spectral analyzer using software LabVIEW. To obtain spectrum of the signal, some steps is needed. The first step is signal conditioning process. A filter is used to reduce the noise that comes along with the information needed. Type and order of filter are the important of this step. The next step is converting the analog signal into digital form, by noticing the sampling frequency, and uses the filter to remove any alias. The next step is windowing, or multiplies the signal by window function. After this step, the signal is transformed using Fast Fourier Transform to obtain the signal components in frequency domain. Spectral density of the signal is the square form of frequency domain signal. If there are two input signals, the cross spectral density is needed to give additional information. This is obtained by cross correlating the signals. It is interesting to establish the joint-cooperation between ITB and university in Germany to improve practical study and students experiment activities. I am looking forward to continue my study in acoustics signal processing in Gemany. II. Objective The objective of this project is to simulate a dual channel digital spectral analyzer using LabVIEW software. III. Methodology A. Fast Fourier Analysis
Transform-based
Signal
The signals are functions of one or more independent variables and typically contain information about the behavior or nature of some phenomenon. When we measure something, the result signal usually represented as time-domain. Sometimes it is difficult to get the information from this signal form, thus we need to transform it into frequency-domain signal. The tool for this task is called Fourier Transform. For discrete signal, the transformation is called Discrete Fourier Transform or DFT.
IV. Progress In practice, DFT needs several time to complete its algorithm. To reduce the time, we can use the Fast Fourier Transform, one of approximation technique to solve the Fourier transform found by J.W Cooley and J.W.Tukey. This technique use the simetric and periodicity characteristic of the signal. Thus, to run this algorithm, the signal must have 2N points of data, where N is integer. If there are no sufficient data, we can resample the signal or add some zero to the signal. This process is called zero padding, and can cause the spectral to have higher resolution.
The spectral analyzer in this project will be designed to meet these specification:
1.
2. 3.
4. For non-stationary signals, we must do the FFT algorithm in every range where the data can be assumed as stationary signal (usualy at range 25ms). This method is also called Short Time Fourier Transform (STFT). B. Periodogram Technique In periodogram technique or also known as direct method, the signal is directly multiplied with window function. Then, the windowed signal is transformed into frequency-domain using FFT. To obtain the power spectral density, the signal is multiplied with its conjugate. If we want to see the cross power spectral density, we must multiply one of the signal with the other’s conjugate. Since the phase information is lost when the signal is being multiplied with its conjugate, the phase information can be achieved from the FFT result. C.
Correlogram Technique
In correlogram technique, the signal is correlated with itself before multiplied with window function. If we want to achieve the cross power spectral density, we must crosscorrelate both of the signal we have. After that, the signal is transformed using FFT. The spectral density obtained from this transform has already included phase information.
5. 6.
7.
8.
It has dual channel input that can be used to analyze non-stationary digital signal. It has input range until 20KHz, which is a range for audio signal. It uses periodogram and correlogram techniques as spectral estimation methods. It gives output Power Spectral Density from each input signal and Cross Power Spectral Density from crosscorrelation of both input signal, which are displayed as a 3D spectogram, and also the phase information of both signals. It has an optional input range from 1x - 100x. It has automatic and manual zero padding and resampling features. The operator can choose the sampling frequency by himself. It has the following window functions option: Hanning, Hamming, Rectangle, Blackman, BlackmanHarris, Gaussian, and Triangle. It uses LPF with programmable cutoff frequency.
V. References [1] Alan V. Oppenheim and Alan S. Wilsky, Signals and Systems. 1995. India: Prentice-Hall. [2]
Harijono A. Tjokronegoro. Pengolahan Sinyal. 2001. Bandung: Penerbit ITB.
[3] Harijono A. Tjokronegoro. Analisis Spektral Digital. 2004. Bandung: Penerbit ITB [4] National Instruments Tutorial : The Fundamentals of FFT-Based Signal Analysis and Measurement in LabVIEW and LabWindows/CVI