JPH05223860A - Digital spectrum analyzer - Google Patents

Digital spectrum analyzer

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Publication number
JPH05223860A
JPH05223860A JP2819592A JP2819592A JPH05223860A JP H05223860 A JPH05223860 A JP H05223860A JP 2819592 A JP2819592 A JP 2819592A JP 2819592 A JP2819592 A JP 2819592A JP H05223860 A JPH05223860 A JP H05223860A
Authority
JP
Japan
Prior art keywords
period
data string
fourier transform
weighting
averaging
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
JP2819592A
Other languages
Japanese (ja)
Inventor
Eiichi Eguchi
栄一 江口
Masayuki Ogawa
政行 小川
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Advantest Corp
Original Assignee
Advantest Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Advantest Corp filed Critical Advantest Corp
Priority to JP2819592A priority Critical patent/JPH05223860A/en
Publication of JPH05223860A publication Critical patent/JPH05223860A/en
Withdrawn legal-status Critical Current

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Abstract

PURPOSE:To provide possibility of analyzing with high precision even after averaging by performing Fourier's transform upon weighing with the window function even if the input signal varies. CONSTITUTION:The input sampling data train is taken out step by step at intervals of period Tf while the period Td is overlapped and subjected to weighing process with a flat pass function by a weighing part 21, The weighed data train is FFT converted by a Fourier transform part 15, and the same frequency component of each data train having corresponding FFT transform result is averaged by an averaging part 16, and the resultant is given on a display 17.

Description

【発明の詳細な説明】Detailed Description of the Invention

【0001】[0001]

【産業上の利用分野】この発明は入力サンプリングデー
タ列を一定期間ごとに窓関数で重み付け、その重み付け
されたデータ列を離散的フーリエ変換し、そのような離
散的フーリエ変換の結果の各周波数成分を、入力サンプ
リングデータ列中の上記一定期間の複数に対し、同一周
波数について平均化してSN比を向上するようにしたデ
ジタルスペクトラムアナライザに関する。
BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention weights an input sampling data sequence with a window function at regular intervals, performs a discrete Fourier transform on the weighted data sequence, and outputs each frequency component as a result of such a discrete Fourier transform. To a digital spectrum analyzer for improving the SN ratio by averaging the same frequency for a plurality of fixed periods in the input sampling data sequence.

【0002】[0002]

【従来の技術】図3Aに示すようにデジタルスペクトラ
ムアナライザは一般に、入力端子11から入力された入
力信号はAD変換器12で周期的にサンプリングされ、
その各サンプル値がデジタルデータに変換されて入力サ
ンプリングデータ列に変換される。この入力サンプリン
グデータ列はメモリ13に一旦取込まれ、その後、図3
Baに示すように最初の一定周期Tf のデータ列が取
出され、重み付け部14で窓関数、通常ハニング関数で
重み付けされ、その重み付けされたデータ列が離散的フ
ーリエ変換部15で離散的フーリエ変換、通常高速フー
リエ変換(FFT)され、各周波数成分V(fi ) (i
=1,2,3,…)が得られる。
2. Description of the Related Art Generally, as shown in FIG. 3A, in a digital spectrum analyzer, an input signal input from an input terminal 11 is periodically sampled by an AD converter 12,
Each sample value is converted into digital data and converted into an input sampling data string. This input sampling data string is once taken in the memory 13, and then, as shown in FIG.
As shown in Ba, the data sequence of the first fixed period T f is taken out, weighted by the weighting unit 14 with the window function, usually the Hanning function, and the weighted data sequence is subjected to the discrete Fourier transform by the discrete Fourier transform unit 15. , Usually Fast Fourier Transform (FFT) and each frequency component V (f i ) (i
= 1, 2, 3, ...) is obtained.

【0003】その後、入力サンプリングデータ列aの次
の一定期間Tf のデータ列をメモリ13から取出し、
このデータ列を同様に処理してその各周波数成分V
(fi) を得る。以下同様にして、入力サンプリングデ
ータ列aから一定期間のデータ列を次々と取出し、その
各データ列について重み付け、フーリエ変換してその周
波数成分V(fi ) を得る。このようにしてn個のデー
タ列の各フーリエ変換結果について、平均化部16で同
一周波数成分、つまり各V(f1 ) ,各V(f2) ,各
V(f3 ) ,…をそれぞれ平均化して、入力信号につい
てのSN比の高い周波数解析結果を得て表示器17に表
示する。
After that, a data string for the next fixed period T f of the input sampling data string a is fetched from the memory 13,
This data string is processed in the same manner and each frequency component V
Get (f i ). In the same manner, data strings of a fixed period are successively taken out from the input sampling data string a, weighted and Fourier-transformed for each data string to obtain its frequency component V (f i ). In this way, with respect to each Fourier transform result of n data strings, the averaging unit 16 outputs the same frequency component, that is, each V (f 1 ), each V (f 2 ), each V (f 3 ) ,. After averaging, a frequency analysis result having a high SN ratio for the input signal is obtained and displayed on the display unit 17.

【0004】[0004]

【発明が解決しようとする課題】入力信号が時間的に変
動していない時はよいが、入力信号が時間的に変動する
場合は、従来の装置においては平均化による統計処理の
確らしさが悪いものとなる。つまり重み付け部14にお
いて重み付けを行っているが、この重み付けは通常、フ
ーリエ変換を行うデータ列の期間Tf における中央部で
大、両端部で小とされている。このため入力信号に変動
があると、その変動が期間Tf の中央部で生じると、大
きく影響し、期間Tf の端部で生じるとわずかしか影響
しないことになり、入力信号の変動に対する各周波数成
分の変動の推定が悪くなる。
It is good when the input signal does not fluctuate with time, but when the input signal fluctuates with time, the accuracy of statistical processing by averaging is poor in the conventional apparatus. Will be things. That is, although the weighting unit 14 performs weighting, this weighting is generally set to be large at the central portion and small at both end portions in the period T f of the data string to be Fourier transformed. Therefore, if there is a change in the input signal, it will have a large effect if the change occurs at the center of the period T f , and will have a small effect if it occurs at the end of the period T f. Estimates of fluctuations in frequency components are poor.

【0005】[0005]

【課題を解決するための手段】この発明によれば、入力
サンプリングデータ列から一定期間ずつのデータ列の取
込みを、一定の重複期間重複させながら順次ずらして行
われ、また各データ列に対する重み付けはフラットパス
関数により行われる。
According to the present invention, a data string is fetched from an input sampling data string for a certain period of time while sequentially shifting while overlapping for a certain overlapping period, and weighting for each data column is performed. It is done by a flat path function.

【0006】[0006]

【実施例】図1Aにこの発明の実施例を示し、図3Aと
対応する部分に同一符号を付けてある。この発明ではメ
モリ13に取込まれた入力サンプリングデータ列は、最
初の一定期間Tf のデータ列が取出されて、重み付
け、フーリエ変換の処理がなされた後、次にはデータ列
と重複期間Td だけ重複した一定期間Tf のデータ列
が取出され、以下同様に、メモリ13の入力サンプリ
ングデータ列は、フーリエ変換した直前の一定期間Tf
のデータ列に対し、重複期間Td だけ重複して一定期間
f ずつデータ列として取出される。
FIG. 1A shows an embodiment of the present invention, in which parts corresponding to those in FIG. 3A are designated by the same reference numerals. According to the present invention, the input sampling data string taken into the memory 13 is first taken out of the data string of a constant period T f , subjected to weighting and Fourier transform processing, and then the data string and the overlapping period T. A data string of a fixed period T f that is duplicated by d is taken out, and similarly, the input sampling data string of the memory 13 is the fixed period T f immediately before the Fourier transform.
The data string is duplicated for the overlapping period T d and taken out as a data string for each fixed period T f .

【0007】重複期間Td は、入力サンプリングデータ
列の各サンプリング点における重み付け部により各重み
付け量を重複したものにつきそれぞれ加算した時、ほぼ
同一値となるようにすることが好ましい。つまり一定レ
ベルの入力信号の場合は重み付け後において期間Tf
各データ列の対応するサンプル点のデータを加算した
時、ほぼ同一となるように重複期間Td が決められる。
It is preferable that the overlapping period T d be approximately the same value when the weighting units at the sampling points of the input sampling data string add the respective weighting amounts for the overlapping. That is, in the case of an input signal of a constant level, the overlapping period T d is determined so as to be almost the same when the data of the corresponding sample points of each data string of the period T f is added after weighting.

【0008】このようにしてメモリ13から取出された
各期間Tf のデータ列は重み付け部21でフラットパス
関数で重み付けがなされる。フラットパス関数Y(t)
は、DOLPH−CHEBYSHEV関数D(f)とR
ECTANGULAR関数R(f)とを畳込みした結果
を逆フーリエ変換(IFFT)した時間関数である。即
ち Y(t)=IFF{D(f)*R(f)} *:畳込み関数の記号 D(f)= cos{n・arc-cos(z0・cos(πf)}/cosh(n・arc -cosh(z0)) z0= cosh(1/n・arc-cosh(para)) = cosh{1/n・ln(para+SQRT(para*2−1))} cosh(f)=(exp(f)+exp(−f))/2 n:サンプリング数 z0:変数 para:変数(=10**epara) R(f)=0.0 (0≦f<n1,n2<f≦n) =0.5 (f=n1,f=n2) =1.0 (n1<f<n2) このフラットパス関数は図1Cに示すような形状をして
いる。このフラットパス関数による重み付けにおいて、
前記重複期間Td は大きい程、得られた平均値の周波数
の各レベルの確らしさは向上するが、処理時間が長くな
る。この点から、Td はTf の99〜75%、好ましく
は84%程度がよい。
The data string of each period T f fetched from the memory 13 in this way is weighted by the weighting unit 21 by the flat path function. Flat path function Y (t)
Is a DOLPH-CHEBYSHEV function D (f) and R
It is a time function obtained by performing an inverse Fourier transform (IFFT) on the result of convolving the ECTANGULAR function R (f). That is, Y (t) = IFF {D (f) * R (f)} *: Symbol of convolution function D (f) = cos {n-arc-cos (z0.cos (πf)} / cosh (n ・arc-cosh (z0)) z0 = cosh (1 / n ・ arc-cosh (para)) = cosh {1 / n ・ ln (para + SQRT (para * 2-1))} cosh (f) = (exp (f ) + Exp (-f)) / 2 n: Number of samples z0: Variable para: Variable (= 10 ** epara) R (f) = 0.0 (0 ≦ f <n1, n2 <f ≦ n) = 0. 5 (f = n1, f = n2) = 1.0 (n1 <f <n2) This flat path function has a shape as shown in Fig. 1C.
The larger the overlap period T d, the more accurate each level of the obtained average frequency is, but the longer the processing time becomes. From this point, 99-75% of the T d is T f, preferably from about 84%.

【0009】重み付け部21でデータ列に重み付けし、
それをフーリエ変換部15で離散的フーリエ変換し、順
次重複しながら取込んだ各期間Tf のデータ列について
重み付け、フーリエ変換を行い、その後、各フーリエ変
換された結果の全体を平均化部16で各同一周波数成分
についてそれぞれ平均化して表示器17に表示すること
は従来と同様である。
The weighting unit 21 weights the data sequence,
The Fourier transform unit 15 performs a discrete Fourier transform on the data sequence of each period T f which is sequentially overlapped and weighted, and the Fourier transform is performed. After that, the whole result of each Fourier transform is averaged by the averaging unit 16. It is the same as in the prior art that the same frequency components are averaged and displayed on the display unit 17.

【0010】上述では入力サンプリングデータ列のすべ
てをメモリ13に取込んだ後に、期間Tf ずつフーリエ
変換したが、実時間で処理するようにしてもよい。即ち
図3に示すように、入力端子11より入力された入力信
号はAD変換器12で入力サンプリングデータ列に変換
され、その入力サンプリングデータ列は重複期間Td
複させながら、一定期間Tf ずつ順次ずらされたデータ
列,,,,…(図1B)として、バッファ2
2a,22b,22c,22dに各1データ列ずつ順次
取込むことが繰返される。
In the above description, all the input sampling data strings are fetched in the memory 13 and then Fourier-transformed for each period T f , but they may be processed in real time. That is, as shown in FIG. 3, the input signal input from the input terminal 11 is converted into an input sampling data string by the AD converter 12, and the input sampling data string is overlapped by the overlapping period T d, and by a certain period T f. As the data string sequentially shifted, ... (FIG. 1B), the buffer 2
It is repeated to sequentially fetch each one data string into 2a, 22b, 22c and 22d.

【0011】これら各バッファ22a〜22dにそれぞ
れ取込まれたデータ列はそれぞれ各データ列の取込みが
終ると次々と、重み付け部21a〜21dへそれぞれ供
給されてフラットパス関数で重み付けされ、それぞれフ
ーリエ変換部15a〜15dで離散的フーリエ変換さ
れ、これらフーリエ変換の結果は平均化部16へ供給さ
れて、同一周波数成分について常に最新のn個のデータ
列分についてそれぞれ平均化される、つまり移動平均さ
れて表示器17へ表示される。このnは操作員が設定す
る。
The data strings fetched in the buffers 22a to 22d are supplied to the weighting units 21a to 21d one after another after the data strings have been fetched, weighted by the flat path function, and Fourier transformed respectively. Discrete Fourier transforms are performed in the units 15a to 15d, and the results of these Fourier transforms are supplied to the averaging unit 16 and are always averaged, that is, moving averaged, for the latest n data strings of the same frequency component. Is displayed on the display unit 17. This n is set by the operator.

【0012】1組の重み付け部及びフーリエ変換部によ
る重み付け、フーリエ変換の処理時間が期間Tf より長
い場合はこれに応じてバッファの数よりも多くし、逆に
前記処理時間が期間Tf よりバッファの数よりも少なく
することもできる。またバッファの数はTf を、重複し
ていない期間(Tf −Td )で割った値(整数でなけれ
ば切上げた整数)と同数とすればよい。
When the processing time of the weighting and Fourier transform by one set of weighting section and Fourier transform section is longer than the period T f , the number of buffers is correspondingly increased, and conversely, the processing time is longer than the period T f . It can be less than the number of buffers. The number of buffers may be the same as the value obtained by dividing T f by the non-overlapping period (T f −T d ), which is a rounded up integer if not an integer.

【0013】[0013]

【発明の効果】以上述べたようにこの発明によれば入力
サンプリングデータ列を重複させながら一定周期ずつフ
ーリエ変換しているため、入力信号のレベルが変動して
も、窓関数で重み付けされているが、平均化した時に、
期間Tf のどの部分での変動もほぼ一様な影響となり、
確らしい結果が得られる。また入力信号とサンプリング
周期とが同期していないと、入力信号中の周波数成分
で、1/Tf の整数倍でないものはそれに近い1/Tf
の整数倍の前後の周波数成分として現われ、つまり、誤
差が生じるが、従来において窓関数としてハニング関数
が用いられ、その前記誤差は最大で1.42dBである。
しかしこの発明ではフラットパス関数を用いているた
め、この誤差は3.12×10-5dBと著しく小さくな
り、この点から平均化された周波数成分は高い確度のも
のとなる。
As described above, according to the present invention, since the input sampling data strings are Fourier-transformed by a constant period while overlapping, the window function is weighted even if the level of the input signal changes. But when averaged,
Fluctuations in any part of the period T f have almost uniform influence,
Definite results are obtained. Further, if the input signal and the sampling period are not synchronized, the frequency components in the input signal, 1 / T f for those that are not integer multiple 1 / T f close to it
It appears as a frequency component around an integral multiple of, that is, an error occurs, but the Hanning function is conventionally used as a window function, and the error is 1.42 dB at maximum.
However, since the present invention uses the flat path function, this error is remarkably small at 3.12 × 10 −5 dB, and from this point the averaged frequency component has high accuracy.

【図面の簡単な説明】[Brief description of drawings]

【図1】Aはこの発明の実施例を示すブロック図、Bは
入力サンプリングデータ列より取込むデータ列を示す
図、Cはフラットパス関数の波形を示す図である。
FIG. 1 is a block diagram showing an embodiment of the present invention, B is a diagram showing a data string taken from an input sampling data string, and C is a diagram showing a waveform of a flat path function.

【図2】この発明の他の実施例を示すブロック図。FIG. 2 is a block diagram showing another embodiment of the present invention.

【図3】Aは従来のデジタルスペクトラムアナライザを
示すブロック図、Bはその入力データ列の取込み処理を
示す図である。
3A is a block diagram showing a conventional digital spectrum analyzer, and FIG. 3B is a diagram showing a process of capturing an input data string thereof.

─────────────────────────────────────────────────────
─────────────────────────────────────────────────── ───

【手続補正書】[Procedure amendment]

【提出日】平成5年2月19日[Submission date] February 19, 1993

【手続補正1】[Procedure Amendment 1]

【補正対象書類名】明細書[Document name to be amended] Statement

【補正対象項目名】0008[Correction target item name] 0008

【補正方法】変更[Correction method] Change

【補正内容】[Correction content]

【0008】このようにしてメモリ13から取出された
各期間Tf のデータ列は重み付け部21でフラットパス
関数で重み付けがなされる。フラットパス関数y(t)
は、DOLPH−CHEBYSHEV関数D(f)とR
ECTANGULAR関数R(f)とを畳込みした結果
を逆フーリエ変換(IFFT)した時間関数である。即
ち y(t)=IFFT{D(f)*R(f)} *:畳込み関数の記号 D(f)= cos{n・cos -1(Z0 ・cos(πf))}/cosh(n・cosh-1(Z0)) Z0= cosh(1/n・cosh-1(para)) = cosh{1/n・ln(para+√((para)2-1) )} n:サンプリング数 Z0:変数 para:変数(=10epara ) epara :変数 R(f)=0.0 (0≦f<n1 ,n2 <f≦n) =0.5 (f=n1 ,f=n2 ) =1.0 (n1 <f<n2 ) このフラットパス関数は図1Cに示すような形状をして
いる。このフラットパス関数による重み付けにおいて、
前記重複期間Td は大きい程、得られた平均値の周波数
の各レベルの確らしさは向上するが、処理時間が長くな
る。この点から、Td はTf の99〜75%、好ましく
は84%程度がよい。
The data string of each period T f fetched from the memory 13 in this way is weighted by the weighting unit 21 by the flat path function. Flat path function y (t)
Is a DOLPH-CHEBYSHEV function D (f) and R
It is a time function obtained by performing an inverse Fourier transform (IFFT) on the result of convolving the ECTANGULAR function R (f). That is, y (t) = IFFT {D (f) * R (f)} *: Symbol of convolution function D (f) = cos {n · cos −1 (Z 0 · cos (πf))} / cos ( n · cosh −1 (Z 0 )) Z 0 = cosh (1 / n · cosh −1 (para)) = cosh {1 / n · ln (para + √ ((para) 2 −1))} n: sampling Number Z 0 : Variable para: Variable (= 10 epara ) epara: Variable R (f) = 0.0 (0 ≦ f <n 1 , n 2 <f ≦ n) = 0.5 (f = n 1 , f = N 2 ) = 1.0 (n 1 <f <n 2 ) This flat path function has a shape as shown in FIG. 1C. In this weighting by the flat path function,
The larger the overlap period T d, the more accurate each level of the obtained average frequency is, but the longer the processing time becomes. From this point, 99-75% of the T d is T f, preferably from about 84%.

Claims (1)

【特許請求の範囲】[Claims] 【請求項1】 入力サンプリングデータ列の一定期間に
ついてフラットパス関数で重み付けを行う重み付け手段
と、 その重み付けされた一定期間のデータ列を離散的フーリ
エ変換するフーリエ変換手段と、 上記重み付け及び離散的フーリエ変換を行うデータ列
を、上記入力サンプリングデータ列について一定の重複
期間だけ重複させるように順次ずらす手段と、 上記フーリエ変換された結果の各周波数成分の対応する
ものを平均化する平均化手段と、を具備するデジタルス
ペクトラムアナライザ。
1. A weighting means for weighting a fixed period of an input sampling data sequence with a flat path function, a Fourier transforming means for performing a discrete Fourier transform of the weighted data period of the constant period, and the weighting and the discrete Fourier transform. A means for sequentially shifting the data string to be transformed so as to be overlapped with respect to the input sampling data string for a certain overlap period, and an averaging means for averaging corresponding ones of the frequency components of the result of the Fourier transform, Digital spectrum analyzer equipped with.
JP2819592A 1992-02-14 1992-02-14 Digital spectrum analyzer Withdrawn JPH05223860A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2819592A JPH05223860A (en) 1992-02-14 1992-02-14 Digital spectrum analyzer

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP2819592A JPH05223860A (en) 1992-02-14 1992-02-14 Digital spectrum analyzer

Publications (1)

Publication Number Publication Date
JPH05223860A true JPH05223860A (en) 1993-09-03

Family

ID=12241898

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2819592A Withdrawn JPH05223860A (en) 1992-02-14 1992-02-14 Digital spectrum analyzer

Country Status (1)

Country Link
JP (1) JPH05223860A (en)

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JPH08201162A (en) * 1995-01-25 1996-08-09 Nippon Seiko Kk Periodic signal processor
JP2006064549A (en) * 2004-08-27 2006-03-09 Nippon Telegr & Teleph Corp <Ntt> Method, system and program for analyzing spectrum
JP2009244264A (en) * 2008-03-28 2009-10-22 Tektronix Inc Video bandwidth emulation method
JP4619402B2 (en) * 2005-02-01 2011-01-26 株式会社日立国際電気 Spectral analysis method, distortion detection apparatus, distortion compensation amplification apparatus
JP2011047839A (en) * 2009-08-28 2011-03-10 Hitachi Ltd Vector measuring device, vector measuring system, and vector measuring method
JP2015028461A (en) * 2013-06-28 2015-02-12 株式会社Jvcケンウッド Frequency setting apparatus and frequency setting method
JP2019109137A (en) * 2017-12-19 2019-07-04 Tdk株式会社 Radiated emissions measurement device and radiated emissions measuring method

Cited By (7)

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JPH08201162A (en) * 1995-01-25 1996-08-09 Nippon Seiko Kk Periodic signal processor
JP2006064549A (en) * 2004-08-27 2006-03-09 Nippon Telegr & Teleph Corp <Ntt> Method, system and program for analyzing spectrum
JP4619402B2 (en) * 2005-02-01 2011-01-26 株式会社日立国際電気 Spectral analysis method, distortion detection apparatus, distortion compensation amplification apparatus
JP2009244264A (en) * 2008-03-28 2009-10-22 Tektronix Inc Video bandwidth emulation method
JP2011047839A (en) * 2009-08-28 2011-03-10 Hitachi Ltd Vector measuring device, vector measuring system, and vector measuring method
JP2015028461A (en) * 2013-06-28 2015-02-12 株式会社Jvcケンウッド Frequency setting apparatus and frequency setting method
JP2019109137A (en) * 2017-12-19 2019-07-04 Tdk株式会社 Radiated emissions measurement device and radiated emissions measuring method

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