本文選題：譜參數(shù)估計(jì) + 分段擬合��； 參考：《東南大學(xué)》2015年碩士論文

【摘要】：近年來,ADC不斷的朝著高速高精度的方向發(fā)展,這使得ADC標(biāo)準(zhǔn)測試算法的測試成本越來越高,而且采用標(biāo)準(zhǔn)算法測試時(shí)需要采集靜態(tài)和動態(tài)兩類數(shù)據(jù)并結(jié)合相應(yīng)的靜態(tài)和動態(tài)算法才能測出ADC所有的參數(shù),這更增加了ADC的測試成本。通過一次數(shù)據(jù)采集實(shí)現(xiàn)動態(tài)和靜態(tài)兩類參數(shù)的測試將能節(jié)約一次數(shù)據(jù)采集成本,而現(xiàn)有的該種算法有兩類,一類是基于靜態(tài)實(shí)現(xiàn)動態(tài)參數(shù)測試,但該類方法是基于直方圖測試實(shí)現(xiàn)的,需要采集大量的點(diǎn)數(shù),所減少的成本有限；另一類是基于動態(tài)實(shí)現(xiàn)靜態(tài)參數(shù)的測試,該類算法是基于擬合和頻譜實(shí)現(xiàn)的,所需的采樣點(diǎn)很少,可以降低大量成本,但該類算法的缺點(diǎn)是測試精度不高。因此,研究能夠大量降低測試成本的基于動態(tài)實(shí)現(xiàn)靜態(tài)參數(shù)測試的單次數(shù)據(jù)采集實(shí)現(xiàn)兩類參數(shù)測試的算法將具有極大的實(shí)用價(jià)值�；诖�,本文對基于動態(tài)實(shí)現(xiàn)靜態(tài)參數(shù)測試中的譜參數(shù)估計(jì)算法進(jìn)行了研究,并在研究基礎(chǔ)上獨(dú)創(chuàng)性的結(jié)合分段理論實(shí)現(xiàn)了譜參數(shù)估計(jì)算法的優(yōu)化。至此,本文在解決完優(yōu)化算法實(shí)現(xiàn)的關(guān)鍵問題即特定信號源的實(shí)現(xiàn)后,該優(yōu)化算法首先將ADC的傳輸函數(shù)采用傅里葉級數(shù)建模為時(shí)間與數(shù)字代碼的表達(dá)式,之后通過切比雪夫第一等式建立輸出代碼與模擬輸入之間的關(guān)系式,并根據(jù)ADC的滿幅量程將ADC的傳輸函數(shù)等分成幾段,在每段內(nèi)分別輸入正弦、采集數(shù)據(jù)以及計(jì)算傅里葉系數(shù),然后通過連續(xù)處理得到ADC完整的傳輸函數(shù),并與ADC的理想傳輸函數(shù)相減得到ADC的INL曲線,從而實(shí)現(xiàn)用動態(tài)數(shù)據(jù)計(jì)算ADC的靜態(tài)參數(shù)。此外,本文還重點(diǎn)研究了該優(yōu)化算法的最佳測試環(huán)境。最后,本文以14比特AD9648為基礎(chǔ)搭建AD9648的自動化測試系統(tǒng),通過實(shí)驗(yàn)驗(yàn)證本算法的性能。實(shí)驗(yàn)結(jié)果表明,以直方圖算法測試得到的最大INL即1.189LSB為該系統(tǒng)的真實(shí)INL,在最佳估算條件即采樣點(diǎn)為8000,傅里葉估算項(xiàng)數(shù)為80的情況下,原有的譜參數(shù)估計(jì)算法所得的INL的估算誤差達(dá)到了0.624LSB,而同樣在最佳估算條件即采樣點(diǎn)為8000,傅里葉估算項(xiàng)數(shù)為8,分段為8的情況下,本文所提算法得到的INL估算誤差為0.2953LSB。其估算精度相對原有譜參數(shù)估計(jì)算法提高了0.3303LSB,近30%。除此之外,本文所提算法比譜參數(shù)估計(jì)算法少做了近200萬次乘法,相比譜參數(shù)估計(jì)算法結(jié)合MATLAB軟件所得的計(jì)算時(shí)間2.605s而言,本文所提算法只需0.2832s,計(jì)算時(shí)間降低了近90%。
[Abstract]:In recent years, the ADC has been developing in the direction of high speed and high precision, which makes the test cost of ADC standard test algorithm more and more high. Moreover, it is necessary to collect static and dynamic data and combine the corresponding static and dynamic algorithms to measure all the parameters of ADC, which increases the testing cost of ADC. The cost of data acquisition can be saved by realizing the test of dynamic and static parameters through one data acquisition. There are two kinds of algorithms, one is to realize dynamic parameter testing based on static state, the other is to realize dynamic parameter testing based on static state. But this kind of method is based on histogram test, it needs to collect a lot of points, the cost is limited, the other is based on the dynamic implementation of static parameters testing, this kind of algorithm is based on fitting and spectrum implementation. There are few sampling points needed, which can reduce a lot of cost, but the shortcoming of this kind of algorithm is that the test accuracy is not high. Therefore, it is of great practical value to study a single data acquisition algorithm based on dynamic static parameter testing, which can greatly reduce the cost of testing. Based on this, this paper studies the spectral parameter estimation algorithm based on dynamic static parameter testing, and realizes the optimization of spectral parameter estimation algorithm based on the original piecewise theory. So far, after solving the key problem of the realization of the optimization algorithm, that is, the realization of a specific signal source, the optimization algorithm first uses Fourier series to model the transfer function of ADC as the expression of time and digital code. Then the relation between the output code and the analog input is established by Chebyshev's first equation. According to the full amplitude range of ADC, the transfer function of ADC is divided into several sections. In each section, sinusoidal input, data collection and Fourier coefficient are calculated. Then the complete transfer function of ADC is obtained by continuous processing, and the INL curve of ADC is obtained by subtracting it from the ideal transfer function of ADC, so that the static parameters of ADC can be calculated with dynamic data. In addition, this paper also focuses on the optimal test environment of the optimization algorithm. Finally, based on 14 bit AD9648, the automatic test system of AD9648 is built, and the performance of the algorithm is verified by experiments. The experimental results show that the maximum INL obtained by histogram algorithm, that is, 1.189LSB, is the real INL of the system. The best estimation condition is that the sampling point is 8 000 and the Fourier estimation term is 80. The estimation error of INL obtained by the original spectral parameter estimation algorithm is 0.624 LSB. but the estimation error of INL obtained by the proposed algorithm is 0.2953 LSB-0.2953LSB-0.2953LSB-0.2953LSB. when the sample point is 8000, Fourier estimation term is 8 and subsection is 8, the estimation error of INL is 0.2953LSB. Compared with the original spectral parameter estimation algorithm, its estimation accuracy is improved by 0.3303LSBand nearly 30303LSB. In addition, the proposed algorithm is less than the spectral parameter estimation algorithm by nearly 2 million times. Compared with the spectral parameter estimation algorithm combined with the MATLAB software, the computational time of the proposed algorithm is only 0.2832s, and the computational time is reduced by nearly 90 seconds.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN792

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