JPH0430550B2 - - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention is a digital camera that supplies a test signal to a measurement target, performs fast Fourier transform on the input test signal and output test signal of the measurement target, and measures the transfer function of the measurement target from the converted output. Regarding spectrum analyzers.

<Background of the Invention> As shown in FIG. 1, a digital spectrum analyzer generates a test signal from a test signal generator 11 and applies it to a measurement object 12, and performs fast Fourier transform analysis on the output test signal from the measurement object 12 from a terminal 13. Input to the device (hereinafter referred to as FFT analysis device) 14,
In addition, the test signal on the input side of the measurement object 12 is connected to the terminal 15.
is input to the FFT analysis device 14, and the analysis device 14
After converting each input test signal into a digital signal, fast Fourier transform is performed to obtain a transfer function of the measurement object 12.

In the FFT analyzer 14, as shown in FIG. 2, the test signal from the input terminal 13 (or 15) is supplied to the low frequency converter 17 through the variable gain amplifier 16, and the output of the low frequency converter 17 is sent to the AD converter. 18
The signal is sampled at a constant period and converted into a digital signal, and the digital signal is stored in the buffer memory 19. When a fixed number of samples, for example 1024 samples, are stored in the buffer memory 19, fast Fourier transform is performed in the FFT converter 21.

At that time, in order to effectively perform the conversion in the AD converter 18, in other words, the sampled data is
When converting to a 10-bit digital signal from an AD converter, the input range is set to use the entire 10-bit range as much as possible to increase analysis sensitivity. That is, a sample value is read from the buffer memory 19, and if the sample value is too small, the gain of the amplifier 16 is increased, and conversely, if the sample value is too large, the gain of the amplifier 16 is lowered to optimize the conversion by the AD converter 18. do.

In the FFT analyzer 21, the steps shown in Fig.
Set the input signal sensitivity as shown in _S1 . That is, as described above, an operation is performed to set the input level of the AD converter 18 so that the conversion is performed optimally. Thereafter, in step _S2 , a fast Fourier transform is performed on, for example, 1024 sample values taken into the buffer memory 19 in the setting state, and in step _S3 , the input test signal from the terminal 15 and the output test signal from the terminal 13 are processed. Power spectrum Gaa, Gbb, and mutual spectrum
Calculate Gab. That is, the sum Gaa of the square of the real part and the square of the imaginary part is calculated for each frequency component subjected to FFT analysis of the test signal input from the terminal 15, and the FFT is similarly performed for the output test signal input from the terminal 13. Each power spectrum is calculated by calculating the sum Gbb of the square of the real part and the square of the imaginary part of each analyzed frequency component, and then calculates the conjugate of the fast Fourier transform of the input test signal and the output test signal of the corresponding frequency component. The mutual spectrum Gab is calculated by calculating the product with the Fourier transform.

Repeat each test signal in the same way, obtain the required number of power spectra and mutual spectra, and average these in step _S4 to obtain the average power spectra <Gaa> and <
Gbb>, find the average mutual spectrum <Gab>. A transfer function is determined from these average values in step _S5 . That is, the average mutual spectrum <Gab> is divided by the average power spectrum <Gaa>. This is done for each frequency. The results analyzed by the FFT analyzer 21 are displayed on the display 22.

By the way, in the conventional test signal generator 11, for example, a single sine wave is generated, fast Fourier transform is performed on each sine wave, and then the frequency is changed and fast Fourier transform is performed repeatedly. In some cases, random noise is generated and given to the measurement target as a test signal.In the former case, using a single sine wave, the measurement accuracy is high, but the measurement time is long. On the other hand, when random noise is generated, it can be measured in one go, but the measurement accuracy is poor. However, for example, in a case where the change in level with respect to the frequency axis is relatively slow in measurement of a transfer function, measurement accuracy is not so bad even with measurement using random noise, and relatively high precision measurement can be obtained.
Depending on the object to be measured, resonance points or anti-resonance points may occur, and the accuracy of measurements using random noise deteriorates in areas where there is a sudden change in the frequency axis. That is, in the transfer function as shown in FIG. 4, there is a resonance point 23a and an anti-resonance point 23b in the middle part on the frequency axis, and the characteristic curve changes rapidly. In such places, measurement using random noise has a particularly poor accuracy, and has the disadvantage that the sharp part of the resonance point becomes blunt. However, it has been found that in the region 23c, which does not change rapidly but changes relatively slowly, measurement using random noise can be performed with relatively high accuracy.

<Summary of the Invention> From this point of view, the purpose of the present invention is to achieve high accuracy as a whole and a relatively short time in cases where the change in characteristics includes parts that are rapid with respect to the frequency axis and parts that do not change much. The purpose is to provide a digital spectrum analyzer that can perform measurements at

According to this invention, the test signal generator is configured to generate a plurality of types of test signals, and the measurement frequency range is divided into a plurality of regions, and the test signal to be generated for each divided region is generated. For each set frequency range, the test is performed using a set test signal suitable for that range, in which case at least
To optimize the conversion characteristics of the AD converter
Adjust the input level of the AD converter.

For this purpose, the test signal generator 11 is configured as shown in FIG. 5, for example. In other words, bus 25 has a CPU
26. ROM2 that stores the operating program
7. Readable/writable RAM28, and FFT shown in Figure 1
An input/output unit 29 connected to the analysis device 14 is connected. The FFT analyzer 14 outputs data necessary for generating a test signal through the input/output unit 29, that is, data indicating a signal mode such as a noise signal, multiple sine wave signal, single sine wave signal, frequency swept sine wave signal, etc. The maximum and minimum values of the frequency range to be analyzed, as well as the amplitude level and, if necessary, data indicating the frequency change width Δ are sent, and these data are synchronized with the sampling clock of the AD converter 18 in the FFT analysis device 14. Generally, a clock with a higher frequency than this is input. This clock is input from the input/output circuit 29 to a frequency divider 31, which is connected to the bus 25, and a necessary frequency division ratio is set by the CPU 26 in accordance with the input data.

On the other hand, the waveform memory 32 stores digital values obtained by sampling each point of a multiple sine wave waveform,
Various signal waveforms are stored, such as a frequency sweep signal, such as a swept sine wave signal whose frequency changes from F ₀ to F ₁ , and which waveform signal to read out is determined by the CPU 26 via the bus 25 .
That is, the readout area of the waveform memory 32 is selected and determined according to the signal mode given from the FFT analysis device 14. Waveform memory 32 is frequency divider 3
1 output, which is generally read out with a clock of the same frequency synchronized with the AD converter 18. Therefore, the output of the frequency divider 31 is supplied to the address counter in the waveform memory 32 through the switch 33, and this address counter is incremented. It is also possible to read out the noise signal by switching the switch 33 and supplying random pulses from the noise generator 40 to the waveform memory 32.

The signal read from the waveform memory 32 is converted into an analog signal, and is supplied to a level adjuster 36 through a multiplier 34 and a switch 35. The level adjuster 36 is set to bus 2 by the CPU 26.
5, the level is set according to the magnitude given by the FFT analyzer 14. A test signal having the set level is output from the terminal 37, and is applied to the measurement object 12 in FIG.
In the case of generating a single sine wave signal, the sine wave memory 38 is read out by the output clock of the frequency divider 31. The frequency of the sine wave signal generated from the sine wave memory 38 is set by the CPU 26 via the bus 25. This read sine wave signal is supplied to a level setter 36 through a switch 39 and further through a switch 35. Further, this sine wave signal can be multiplied by a signal read out from the waveform memory 32 in a multiplier 34. This test signal generator is capable of generating a test signal of a specified mode within a specified frequency range according to data given from the FFT analysis device 14.

As described above, the input level of the AD converter 18 is controlled so that the conversion operation of the AD converter is optimized, that is, the most effective conversion data is obtained. Therefore, it is possible to detect when the signal level input to the FFT analysis device 14 is too large. For example, as shown in FIG.
1 and 42 to a common input terminal of an amplifier 43, and the output of the amplifier 43 is compared with a reference voltage of a reference power supply 45 in a comparator 44. If this common mode noise component is larger than a certain level, the output of the amplifier 43 is applied to a common input terminal of an amplifier 43.
is configured so that the output is at a high level.
Furthermore, the output of the variable gain amplifier 16 is branched and supplied to a comparator 46, and this amplified output is compared with the reference voltage of a reference power supply 47. If the amplified output is at a level higher than the maximum conversion level in the AD converter 18, the comparison is made. The output of the device 46 is set to a high level. The conversion output of the AD converter 18 is branched to a digital comparator 48 and compared with a threshold value in a register 49. If the output of the AD converter 18 is overflowing data, the comparator 48
Levels higher than 8 occur.

The conversion output of the AD converter 18 is, for example, 12 bits, and the most significant 12 bits of the parallel bit output line 52 are used for the AD conversion output. On the other hand, comparator 4
Each output of 4, 46, 48 is passed through an OR circuit 51.
It is input to the least significant bit of the output line 52 of the AD converter. Therefore, if the input signal level or noise is too large, the least significant bit in the data input to the buffer memory 19 will be a logic 1, and when the FFT analyzer 21 reads out the buffer memory 19 and the least significant bit is 1, It is determined that the input signal is too large and the gain of the variable gain amplifier 16 is set to a constant value.
For example, lower it by 10db.

This range setting operation is performed, for example, as shown in FIG. When a predetermined number of data, for example 1024, is loaded into the buffer memory 19, this FFT
The analyzer 21 extracts a predetermined number, for example 1024, from the buffer memory 19 in step _S1 , and checks in step _S2 whether or not the gain of the variable gain amplifier 16 has been both increased and decreased. If such an operation is not performed, it is checked in step _S3 whether each least significant bit of the read data is 1, that is, whether there is any overflow, and if there is overflow, it is checked. In step _S4 , the gain of the variable gain amplifier 16 is set to a fixed amount, e.g.
Decreased by 10db. On the other hand, in step _S3 , it is checked whether the input is smaller than the read data, and if the upper bits of the absolute value of the data are always 0, it is determined that the input level data is too small. In this case, step
At _S6 , the gain of the variable gain amplifier 16 is set to a certain amount,
For example, it increases by 10db. This process is repeated, that is, new data is generated each time.
After checking whether there is overflow or too small input data for 1024 items and repeating it several times, or if all of the input data is neither overflow nor underinput in one operation, go to step S. ₇ is determined to be an appropriate range, and the gain of the variable gain amplifier 16 is set and held in this range. Note that in some cases, the gain is too large in a certain input range, so if the gain is lowered, the gain becomes too small for the next captured data, making it impossible to proceed to step _S7 . In other words, when the gain is increased and decreased at the same time, this is the step _S2 .
In step _S8 , the gain is set to the gain of the side determined to be in the under-input range at that time, that is, the gain determined to be under-input in step _S6 and the gain increased.

Next, the operation of the apparatus according to the present invention will be explained. As shown in Figure 8, first, in step _S1 , the entire measurement band is measured.
F ₀ to F _o are measured by applying a noise signal or a multiple sine wave signal to the measurement object 12 . As a result, a rough transfer function characteristic as shown in Fig. 4 is obtained, and the level change with respect to frequency is divided into a _steep part and _{a gentle} part. For example _{, as shown} in _{FIG .}
F ₂ to F _o are noise signals, F ₁ to F ₂ are single sine wave signals, and the amplitude of each generated signal is V ₁ ,
V ₂ and V ₃ , and for a sine wave signal, the frequency change width Δ of the sine wave is, for example, F _o /400.

Here, 400 is the number of analysis line spectra of the FFT analyzer. Such test data is set in the FFT analysis device 14. Also, at this time, r is set to 1, and from this point, in step _S3 , a _test measurement is performed for the frequency range _F0 to _F1 , which is the designated region r1,
After the measurement, r is incremented by 1 in step S ₄ , and in step S ₅ it is determined whether or not r is greater than the number of regions R, that is, in this example, since it is divided into three regions, R = 3. If so, the process returns to step _S3 and measurements are performed on the next specified area, that is, the second specified area _F1 to _F2 in this example.

When measuring using a noise signal such as in the designated area r=1, the FFT analysis device 1 is used as shown in Figure 10.
The test data required from 4, i.e. the frequency domain is F ₀
to F ₁ , and the generated test signal is a noise signal,
Furthermore, data indicating that the amplitude is V ₁ is supplied to the test signal generator 11 . Based on this, the test signal generator 11 switches the switch 33 to the noise signal source 40 side in _FIG . The signal is supplied to a level setter 36 through a switch 35 through a switch 34, and is set to the level of _V1 by the level setter 36 to output a test signal. Switch 39 is turned off.

Upon receiving this test signal, the FFT analyzer 14 changes the level of the input side of the AD converter 18 so that the conversion characteristics of the AD converter 18 are the best, as described above with reference to FIGS. 6 and 7. Controls the gain of gain amplifier 16. Thereafter, in step _S4 , FFT analysis is performed at the set level. This measurement is performed k times, for example, and the average is taken as described above. When the measurement for one set area is performed in this way, the next designated area, in this example, the area from F ₁ to F ₂ , is measured.
Measurement in this case is performed, for example, as shown in FIG.

That is, in step _S1 , test data indicating that the region is _F1 to _F2 , the test signal is a single sine wave, the amplitude is _V2 , and the frequency change is F _o /400 is transferred to the test signal generator 11. do. In step _S2 , the lowest frequency _F1 in this designated area is set to _F1 , and in step _S3 , a signal is generated from the test signal generator 11. In this case, the switch 39 is turned on in FIG. 5, and the switch 35 is switched to the switch 39 side. The sine wave memory 38 is set to generate the frequency _F1 . Therefore, initially, a sine wave of frequency _F1 is generated and output, and based on this sine wave signal, level setting is performed in step _S4 to obtain the optimum sensitivity. Then perform FFT on the sine wave of frequency F ₁ in step S ₅ .
The analysis is performed, and in step _S6 , Δ is added to the frequency F _i to obtain F i.In step _S7 , it is checked whether this _{F i is} greater than the highest frequency _F2 in the specified area, and if it is smaller, the process is continued in step _S3. , and from this a sine wave signal with a frequency of F ₁ +Δ is generated. In this way, a sine wave signal with a higher frequency is sequentially generated, and the sensitivity setting is set to be optimal each time.
Perform FFT analysis, and during the analysis, take data multiple times at the optimal setting level and average the FFT conversion output. Frequency of single sine wave generated
When F _i becomes greater than the highest frequency F ₂ in the specified area, the test with a single sine wave in the specified area is terminated.

In this example, next we move on to measurement in the frequency range F ₂ to F _o , in which case the test is performed in the same manner as in FIG. 10. For example, in the example of Fig. ₄ , the measurement frequency range is divided and a test signal is generated for _each region.
A frequency swept sine wave signal may be generated for the region from F ₂ to F _o . Alternatively, all of these regions may be configured to generate frequency-swept sinusoidal signals, for example, as shown in FIGS. 12 and 13, in the frequency F ₀ to F ₁ region, the frequency is swept between Δ ₁ =40 Hz. Generate a signal, then generate a sinusoidal signal that sweeps the frequency within the range of F ₀ + Δ ₁ to F ₀ + 2Δ ₁ , which is an addition of Δ ₁ , and then sweep the frequency range F ₁
For F ₂ to F 2 _, the sweep signal width Δ ₂ is made small, for example, about 4 _Hz , and the sweep signal is slowed down _.
The measurement may be performed with a large sweep width such as =100Hz. In addition, the measurement range is measured using a noise signal or multiple sine waves, and then a single sine wave or a slow frequency swept sine wave is used only within the range of F ₁ to F ₂ in the frequency domain where the frequency range changes rapidly. The measurement data of that part may be used in place of the previous data using multiple sine waves or noise signals.

In this way, the frequency domain is divided and measured, and the sensitivity is optimized each time, so it is necessary to perform level correction in order to view the analysis results as the entire frequency range F ₀ to _{F o} . . In other words, the first
The FFT analysis processing in step _S4 in FIG. 0 and step _S5 in FIG. 11 is performed as shown in FIG. That is, FFT conversion is performed in step _S1 , and size correction is performed on the analysis result in step _S2 . This correction can be done, for example, in the frequency domain that was first acquired.
FFT in the frequency domain when the gain is made larger than the gain of the variable gain amplifier 16 at that time, based on the level range setting made for F ₀ to F ₁
Regarding the conversion output, for example, if the gain is increased by 10db, the magnitude of each measured frequency component is
If the gain is lowered by 10 db, and conversely the gain is lowered by 10 db, the magnitude of each frequency component of the FFT conversion is corrected by increasing it by 10 db. Thereafter, in step _S3 , power spectra Gaa and Gbb mutual spectrum Gab are calculated for each of the corrected frequency components. Furthermore, new data is imported in the same manner, that is, the processing shown in _FIG . Compute the spectrum <Gaa><Gbb> and the average mutual spectrum <Gab>. Furthermore, a transfer function is calculated from this average spectrum in step _S5 .

<Effects> As described above, according to the present invention, first, rough measurements are performed over the entire measurement frequency band, and then, by looking at the results, it is possible to use a single sine wave signal or a signal in the vicinity where the measurement results are changing rapidly. Highly accurate measurements can be made by measuring with the generated frequency sweep signal, and in areas where the characteristics change relatively slowly and smoothly, noise signals, multiple sine wave signals, or high speed signals can be used. By using the sweep signal, highly accurate measurements can be made in a short time. Therefore, the overall measurement time is short and high precision measurement can be performed.

[Brief explanation of drawings]

Figure 1 is a block diagram showing the measurement system of the digital spectrum analyzer, Figure 2 is a block diagram specifically showing a part of the FFT analysis device, and Figure 3 is a block diagram showing the measurement system of the digital spectrum analyzer.
A flowchart showing an example of FFT analysis operation, Figure 4 is a diagram showing an example of the characteristics of the measured transfer function, Figure 5 is a block diagram showing an example of a test signal generator, and Figure 6 is a block diagram showing an example of overflow detection. 7 is a flowchart showing an example of operation for setting the optimum level range, FIG. 8 is a flowchart showing an example of operation of the spectrum analyzer according to the present invention, and FIG. 9 is a flowchart showing an example of setting the test signal and frequency domain. , FIG. 10 and FIG. 11 are flowcharts showing operation examples for each designated area, FIG. 12 is a diagram showing assignment data of other test signals, and FIG. 13 is a diagram showing frequency characteristic assignment corresponding to FIG. 12. Figure 14 shows the first
Step _S4 in Figure 0, Step _S5 in Figure 11
2 is a flowchart showing an example of operation of FFT analysis. 11...Test signal generator, 12...Measurement object, 14
...FFT analysis device, 16...Variable gain amplifier, 17
...Low frequency converter, 18...AD converter, 19...Buffer memory, 21...FFT analyzer, 22...Display device.

Claims (1)

[Claims] 1. In a digital spectrum analyzer that supplies a test signal from a test signal generator to a measurement target, converts the signal from the measurement target into a digital signal with an AD converter, and performs fast Fourier transform on the digital signal for analysis. The above test signal generator is configured to be able to selectively generate multiple types of test signals that are classified based on whether multiple frequency components at different frequency intervals are generated simultaneously or sequentially, and the frequency range for measurement is divided into multiple regions. a test signal setting means for setting the type of test signal to be generated in each frequency region; and means for causing the test signal generator to generate a set test signal set by the setting means and corresponding to each frequency region. and a level setting means for setting the input level range of the AD converter so that the AD converter can perform optimal conversion according to each test signal generated.