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Cyclostationary process

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3684: 2735: 3679:{\displaystyle {\begin{aligned}R_{x}^{n/T_{0}}(\tau )&={\frac {1}{T_{0}}}\int _{-T_{0}/2}^{T_{0}/2}R_{x}(t,\tau )e^{-j2\pi {\frac {n}{T_{0}}}t}\,\mathrm {d} t\\&={\frac {1}{T_{0}}}\int _{-T_{0}/2}^{T_{0}/2}\sigma _{a}^{2}\sum _{k=-\infty }^{\infty }p(t+\tau -kT_{0})p^{*}(t-kT_{0})e^{-j2\pi {\frac {n}{T_{0}}}t}\mathrm {d} t\\&={\frac {\sigma _{a}^{2}}{T_{0}}}\sum _{k=-\infty }^{\infty }\int _{-T_{0}/2-kT_{0}}^{T_{0}/2-kT_{0}}p(\lambda +\tau )p^{*}(\lambda )e^{-j2\pi {\frac {n}{T_{0}}}(\lambda +kT_{0})}\mathrm {d} \lambda \\&={\frac {\sigma _{a}^{2}}{T_{0}}}\int _{-\infty }^{\infty }p(\lambda +\tau )p^{*}(\lambda )e^{-j2\pi {\frac {n}{T_{0}}}\lambda }\mathrm {d} \lambda \\&={\frac {\sigma _{a}^{2}}{T_{0}}}p(\tau )*\left\{p^{*}(-\tau )e^{j2\pi {\frac {n}{T_{0}}}\tau }\right\}.\end{aligned}}} 45:
the more empirical Fraction Of Time (FOT) probability for the alternative model. The FOT probability of some event associated with the time series is defined to be the fraction of time that event occurs over the lifetime of the time series. In both approaches, the process or time series is said to be cyclostationary if and only if its associated probability distributions vary periodically with time. However, in the non-stochastic time-series approach, there is an alternative but equivalent definition: A time series that contains no finite-strength additive sine-wave components is said to exhibit cyclostationarity if and only if there exists some nonlinear time-invariant transformation of the time series that produces finite-strength (non-zero) additive sine-wave components.
28:. For example, the maximum daily temperature in New York City can be modeled as a cyclostationary process: the maximum temperature on July 21 is statistically different from the temperature on December 20; however, it is a reasonable approximation that the temperature on December 20 of different years has identical statistics. Thus, we can view the random process composed of daily maximum temperatures as 365 interleaved stationary processes, each of which takes on a new value once per year. 4545: 4672:. On the other hand, if the speed of rotation changes with time, then the signal is no longer cyclostationary (unless the speed varies periodically). Therefore, it is not a model for cyclostationary signals. It is not even a model for time-warped cyclostationarity, although it can be a useful approximation for sufficiently slow changes in speed of rotation. 2656: 1392: 4322: 4009:
periodic modulations). This happens to be the case for noise and vibration produced by gear mechanisms, bearings, internal combustion engines, turbofans, pumps, propellers, etc. The explicit modelling of mechanical signals as cyclostationary processes has been found useful in several applications, such as in
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In practice, signals exhibiting cyclicity with more than one incommensurate period arise and require a generalization of the theory of cyclostationarity. Such signals are called polycyclostationary if they exhibit a finite number of incommensurate periods and almost cyclostationary if they exhibit a
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of data--that which has actually been measured in practice and, for some parts of theory, conceptually extended from an observed finite time interval to an infinite interval. Both mathematical models lead to probabilistic theories: abstract stochastic probability for the stochastic process model and
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The wide sense theory of time series exhibiting cyclostationarity, polycyclostationarity and almost cyclostationarity originated and developed by Gardner was also generalized by Gardner to a theory of higher-order temporal and spectral moments and cumulants and a strict sense theory of cumulative
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countably infinite number. Such signals arise frequently in radio communications due to multiple transmissions with differing sine-wave carrier frequencies and digital symbol rates. The theory was introduced in for stochastic processes and further developed in for non-stochastic time series.
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Mechanical signals produced by rotating or reciprocating machines are remarkably well modelled as cyclostationary processes. The cyclostationary family accepts all signals with hidden periodicities, either of the additive type (presence of tonal components) or multiplicative type (presence of
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For time series, the reason the cyclic spectral density function is called the spectral correlation density function is that it equals the limit, as filter bandwidth approaches zero, of the average over all time of the product of the output of a one-sided bandpass filter with center frequency
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This same result can be obtained for the non-stochastic time series model of linearly modulated digital signals in which expectation is replaced with infinite time average, but this requires a somewhat modified mathematical method as originally observed and proved in.
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2018 W. A. Gardner. STATISTICALLY INFERRED TIME WARPING: EXTENDING THE CYCLOSTATIONARITY PARADIGM FROM REGULAR TO IRREGULAR STATISTICAL CYCLICITY IN SCIENTIFIC DATA. EURASIP Journal on Advances in Signal Processing volume 2018, Article number: 59. doi:
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W. A. Gardner. STATISTICALLY INFERRED TIME WARPING: EXTENDING THE CYCLOSTATIONARITY PARADIGM FROM REGULAR TO IRREGULAR STATISTICAL CYCLICITY IN SCIENTIFIC DATA. EURASIP Journal on Advances in Signal Processing volume 2018, Article number: 59. doi:
1179: 4540:{\displaystyle S_{x}^{\alpha }(f)=\lim _{S\rightarrow +\infty }{\frac {1}{S}}\int _{-S/2}^{S/2}\int _{-\infty }^{+\infty }R_{x}(\theta ,\tau )e^{-j2\pi f\tau }e^{-j2\pi \alpha {\frac {\theta }{\Theta }}}\,\mathrm {d} \tau \,\mathrm {d} \theta } 4152: 4028:
of rotation of a specific component – the “cycle” of the machine. At the same time, a temporal description must be preserved to reflect the nature of dynamical phenomena that are governed by differential equations of time. Therefore, the
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is called the spectral correlation density function is that it equals the limit, as filter bandwidth approaches zero, of the expected value of the product of the output of a one-sided bandpass filter with center frequency
2740: 614: 2651:{\displaystyle {\begin{aligned}R_{x}(t,\tau )&=\operatorname {E} \\&=\sum _{k,n}\operatorname {E} p(t+\tau -kT_{0})p^{*}(t-nT_{0})\\&=\sigma _{a}^{2}\sum _{k}p(t+\tau -kT_{0})p^{*}(t-kT_{0}).\end{aligned}}} 1627: 244: 507: 851: 3937:
in which the autoregression coefficients and residual variance are no longer constant but vary cyclically with time. His work follows a number of other studies of cyclostationary processes within the field of
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probability distributions. The encyclopedic book comprehensively teaches all of this and provides a scholarly treatment of the originating publications by Gardner and contributions thereafter by others.
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1983 R. A. Boyles and W. A. Gardner. CYCLOERGODIC PROPERTIES OF DISCRETE-PARAMETER NONSTATIONARY STOCHASTIC PROCESSES. IEEE Transactions on Information Theory, Vol. IT-29, No. 1, pp. 105-114.
1653: 1103: 2125: 1157: 837: 3723: 2122: 637: 4288: 2232: 1830: 321: 141: 4650: 1387:{\displaystyle {\widehat {R}}_{x}^{n/T_{0}}(\tau )=\lim _{T\rightarrow +\infty }{\frac {1}{T}}\int _{-T/2}^{T/2}x(t+\tau )x^{*}(t)e^{-j2\pi {\frac {n}{T_{0}}}t}\mathrm {d} t.} 363: 1971: 1937: 1899: 1865: 4206: 4041: 3914: 4929:
W. A. Gardner. SIGNAL INTERCEPTION: A UNIFYING THEORETICAL FRAMEWORK FOR FEATURE DETECTION. IEEE Transactions on Communications, Vol. COM-36, No. 8, pp. 897-906. 1988
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Cyclostationarity has extremely diverse applications in essentially all fields of engineering and science, as thoroughly documented in and. A few examples are:
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There are two differing approaches to the treatment of cyclostationary processes. The stochastic approach is to view measurements as an instance of an abstract
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A signal that is just a function of time and not a sample path of a stochastic process can exhibit cyclostationarity properties in the framework of the
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processes. The exact definition differs depending on whether the signal is treated as a stochastic process or as a deterministic time series.
1040:{\displaystyle R_{x}^{n/T_{0}}(\tau )={\frac {1}{T_{0}}}\int _{-T_{0}/2}^{T_{0}/2}R_{x}(t,\tau )e^{-j2\pi {\frac {n}{T_{0}}}t}\mathrm {d} t.} 4310:, are called (wide-sense) angle-time cyclostationary. The double Fourier transform of the angle-time autocorrelation function defines the 3930: 1993: 1522:. If the signal is further cycloergodic, all sample paths exhibit the same cyclic time-averages with probability equal to 1 and thus 1402: 53:
An important special case of cyclostationary signals is one that exhibits cyclostationarity in second-order statistics (e.g., the
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Kipnis, Alon; Goldsmith, Andrea; Eldar, Yonina (May 2018). "The Distortion Rate Function of Cyclostationary Gaussian Processes".
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W. A. Gardner. INTRODUCTION TO RANDOM PROCESSES WITH APPLICATIONS TO SIGNALS AND SYSTEMS. Macmillan, New York, 434 pages, 1985
4010: 1973:, with both filter outputs frequency shifted to a common center frequency, such as zero, as originally observed and proved in. 1901:, with both filter outputs frequency shifted to a common center frequency, such as zero, as originally observed and proved in. 4883:
W. A. Gardner. STATISTICAL SPECTRAL ANALYSIS: A NONPROBABILISTIC THEORY. Prentice-Hall, Englewood Cliffs, NJ, 565 pages, 1987.
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W. A. Gardner. STATISTICAL SPECTRAL ANALYSIS: A NONPROBABILISTIC THEORY. Prentice-Hall, Englewood Cliffs, NJ, 565 pages, 1987.
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W. A. Gardner. STATISTICAL SPECTRAL ANALYSIS: A NONPROBABILISTIC THEORY. Prentice-Hall, Englewood Cliffs, NJ, 565 pages, 1987.
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W. A. Gardner. STATISTICAL SPECTRAL ANALYSIS: A NONPROBABILISTIC THEORY. Prentice-Hall, Englewood Cliffs, NJ, 565 pages, 1987.
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having statistical properties that vary cyclically with time. A cyclostationary process can be viewed as multiple interleaved
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W. A. Gardner. STATIONARIZABLE RANDOM PROCESSES. IEEE Transactions on Information Theory, Vol. IT-24, No. 1, pp. 8-22. 1978
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A. Napolitano, Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations. Academic Press, 2020.
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A. Napolitano, Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations. Academic Press, 2020.
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for time delay. Processes whose angle-time autocorrelation function exhibit a component periodic in angle, i.e. such that
2237: 1768:{\displaystyle S_{x}^{\alpha }(f)=\int _{-\infty }^{+\infty }R_{x}^{\alpha }(\tau )e^{-j2\pi f\tau }\mathrm {d} \tau .} 21: 4920:
W. A. Gardner. CYCLOSTATIONARITY IN COMMUNICATIONS AND SIGNAL PROCESSING. Piscataway, NJ: IEEE Press. 504 pages.1984.
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Gardner, William A.; Antonio Napolitano; Luigi Paura (2006). "Cyclostationarity: Half a century of research".
3859:{\displaystyle S_{x}^{n/T_{0}}(f)={\frac {\sigma _{a}^{2}}{T_{0}}}P(f)P^{*}\left(f-{\frac {n}{T_{0}}}\right).} 771:{\displaystyle R_{x}(t,\tau )=\sum _{n=-\infty }^{\infty }R_{x}^{n/T_{0}}(\tau )e^{j2\pi {\frac {n}{T_{0}}}t}} 4723:
Gardner, William A. (1991). "Two alternative philosophies for estimation of the parameters of time-series".
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One peculiarity of rotating machine signals is that the period of the process is strictly linked to the
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Cyclostationarity is used to analyze mechanical signals produced by rotating and reciprocating machines.
1794: 285: 105: 62: 4626: 4014: 3939: 3934: 3872: 326: 4147:{\displaystyle R_{x}(\theta ,\tau )=\operatorname {E} \{x(t(\theta )+\tau )x^{*}(t(\theta ))\},\,} 4786: 4768: 3973: 2664: 1942: 1908: 1870: 1836: 37: 25: 4182: 3878: 252: 40:
model. As an alternative, the more empirical approach is to view the measurements as a single
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Pagano, M. (1978) "On periodic and multiple autoregressions." Ann. Stat., 6, 1310–1317.
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The Fourier transform of the cyclic autocorrelation function at cyclic frequency α is called
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Noise in mixers, oscillators, samplers, and logic: an introduction to cyclostationary noise
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and the conjugate of the output of another one-sided bandpass filter with center frequency
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and the conjugate of the output of another one-sided bandpass filter with center frequency
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Wide-sense stationary processes are a special case of cyclostationary processes with only
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point of view. This way, the cyclic autocorrelation function can be defined by:
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utilizes cyclostationary theory to analyze computer networks and car traffic;
609:{\displaystyle R_{x}(t,\tau )=R_{x}(t+T_{0};\tau ){\text{ for all }}t,\tau .} 4843:
Jones, R.H., Brelsford, W.M. (1967) "Time series with periodic structure."
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In signals intelligence, cyclostationarity is used for signal interception;
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to incorporate cyclostationary behaviour. For example, Troutman treated
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The cyclic spectrum at zero cyclic frequency is also called average
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If the time-series is a sample path of a stochastic process it is
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Troutman, B.M. (1979) "Some results in periodic autoregression."
2077:{\displaystyle x(t)=\sum _{k=-\infty }^{\infty }a_{k}p(t-kT_{0})} 4290:
has a non-zero Fourier-Bohr coefficient for some angular period
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Antoni, Jérôme (2009). "Cyclostationarity by examples".
255:, is said to be wide-sense cyclostationary with period 4658: 4629: 4609: 4586: 4558: 4325: 4296: 4254: 4234: 4214: 4185: 4165: 4044: 3881: 3726: 3697: 2738: 2706: 2677: 2319: 2240: 2195: 2163: 2134: 2095: 1996: 1945: 1911: 1873: 1839: 1797: 1656: 1528: 1405: 1182: 1118: 1058: 854: 789: 640: 522: 426: 391: 371: 329: 288: 261: 154: 108: 79: 1788:can be expressed in terms of its cyclic spectrum. 4664: 4644: 4615: 4592: 4564: 4539: 4302: 4282: 4240: 4220: 4200: 4171: 4146: 4004:Angle-time cyclostationarity of mechanical signals 3908: 3858: 3703: 3678: 2719: 2692: 2650: 2301:{\displaystyle \operatorname {E} =\sigma _{a}^{2}} 2300: 2226: 2178: 2149: 2116: 2076: 1965: 1931: 1893: 1859: 1824: 1767: 1621: 1514: 1386: 1151: 1097: 1039: 831: 770: 608: 501: 407: 377: 357: 315: 274: 238: 135: 94: 3875:adopted in digital communications have thus only 621:The autocorrelation function is thus periodic in 4354: 1235: 3955:Higher Order and Strict Sense Cyclostationarity 1784:. For a Gaussian cyclostationary process, its 2186:, is the supporting pulse of the modulation. 8: 4208:for the time instant corresponding to angle 4137: 4079: 1981:An example of cyclostationary signal is the 1098:{\displaystyle n/T_{0},\,n\in \mathbb {Z} ,} 229: 189: 4718: 4716: 4714: 3929:It is possible to generalise the class of 1977:Example: linearly modulated digital signal 4772: 4657: 4628: 4608: 4585: 4557: 4529: 4528: 4520: 4519: 4507: 4491: 4466: 4441: 4428: 4420: 4406: 4402: 4393: 4386: 4372: 4357: 4335: 4330: 4324: 4295: 4259: 4253: 4233: 4213: 4184: 4164: 4143: 4113: 4049: 4043: 3880: 3840: 3831: 3814: 3790: 3780: 3775: 3769: 3749: 3740: 3736: 3731: 3725: 3696: 3651: 3642: 3632: 3610: 3578: 3568: 3563: 3557: 3539: 3526: 3517: 3504: 3485: 3457: 3449: 3437: 3427: 3422: 3416: 3398: 3387: 3363: 3354: 3341: 3322: 3292: 3274: 3268: 3263: 3256: 3238: 3232: 3224: 3214: 3200: 3188: 3178: 3173: 3167: 3149: 3136: 3127: 3114: 3101: 3079: 3066: 3035: 3021: 3011: 3006: 2992: 2986: 2981: 2972: 2966: 2958: 2946: 2937: 2919: 2918: 2905: 2896: 2883: 2858: 2844: 2838: 2833: 2824: 2818: 2810: 2798: 2789: 2765: 2756: 2752: 2747: 2739: 2737: 2711: 2705: 2676: 2632: 2610: 2597: 2566: 2556: 2551: 2528: 2506: 2493: 2459: 2454: 2444: 2419: 2387: 2328: 2320: 2318: 2292: 2287: 2271: 2266: 2259: 2250: 2239: 2209: 2194: 2162: 2133: 2110: 2109: 2100: 2094: 2065: 2040: 2030: 2016: 1995: 1955: 1944: 1921: 1910: 1883: 1872: 1849: 1838: 1807: 1802: 1796: 1754: 1733: 1714: 1709: 1696: 1688: 1666: 1661: 1655: 1602: 1593: 1589: 1584: 1573: 1572: 1551: 1542: 1538: 1533: 1527: 1490: 1481: 1477: 1472: 1461: 1460: 1428: 1419: 1415: 1410: 1404: 1373: 1360: 1351: 1338: 1319: 1287: 1283: 1274: 1267: 1253: 1238: 1214: 1205: 1201: 1196: 1185: 1184: 1181: 1128: 1123: 1117: 1088: 1087: 1080: 1071: 1062: 1057: 1026: 1013: 1004: 991: 966: 952: 946: 941: 932: 926: 918: 906: 897: 877: 868: 864: 859: 853: 812: 803: 799: 794: 788: 755: 746: 736: 715: 706: 702: 697: 687: 673: 645: 639: 589: 574: 555: 527: 521: 491: 479: 425: 396: 390: 370: 334: 328: 287: 266: 260: 235: 214: 159: 153: 107: 78: 4941:Mechanical Systems and Signal Processing 2700:is a cyclostationary signal with period 4761:IEEE Transactions on Information Theory 4683: 1152:{\displaystyle R_{x}^{0}(\tau )\neq 0} 832:{\displaystyle R_{x}^{n/T_{0}}(\tau )} 2727:and cyclic autocorrelation function: 2117:{\displaystyle a_{k}\in \mathbb {C} } 1643:spectral correlation density function 7: 4312:order-frequency spectral correlation 4283:{\displaystyle R_{x}(\theta ;\tau )} 3931:autoregressive moving average models 2308:, the auto-correlation function is: 2227:{\displaystyle \operatorname {E} =0} 4031:angle-time autocorrelation function 4530: 4521: 4512: 4432: 4424: 4367: 4297: 4073: 3540: 3458: 3453: 3399: 3215: 3210: 3150: 3036: 3031: 2920: 2431: 2356: 2241: 2196: 2031: 2026: 1825:{\displaystyle S_{x}^{\alpha }(f)} 1755: 1700: 1692: 1448: 1374: 1248: 1027: 688: 683: 454: 427: 316:{\displaystyle \operatorname {E} } 289: 183: 136:{\displaystyle \operatorname {E} } 109: 69:Cyclostationary stochastic process 14: 4623:, angle is proportional to time, 1983:linearly modulated digital signal 4645:{\displaystyle \theta =\omega t} 4603:For constant speed of rotation, 4011:noise, vibration, and harshness 2128:random variables. The waveform 841:cyclic autocorrelation function 4459: 4447: 4361: 4347: 4341: 4277: 4265: 4195: 4189: 4134: 4131: 4125: 4119: 4106: 4097: 4091: 4085: 4067: 4055: 3807: 3801: 3763: 3757: 3625: 3616: 3595: 3589: 3497: 3491: 3478: 3466: 3393: 3371: 3334: 3328: 3315: 3303: 3107: 3085: 3072: 3044: 2876: 2864: 2779: 2773: 2687: 2681: 2638: 2616: 2603: 2575: 2534: 2512: 2499: 2471: 2465: 2437: 2402: 2399: 2393: 2380: 2368: 2362: 2346: 2334: 2277: 2267: 2251: 2247: 2215: 2202: 2173: 2167: 2144: 2138: 2071: 2049: 2006: 2000: 1819: 1813: 1726: 1720: 1678: 1672: 1616: 1610: 1565: 1559: 1504: 1498: 1442: 1436: 1331: 1325: 1312: 1300: 1242: 1228: 1222: 1140: 1134: 984: 972: 891: 885: 826: 820: 729: 723: 663: 651: 586: 561: 545: 533: 488: 485: 466: 460: 448: 445: 439: 433: 358:{\displaystyle R_{x}(t,\tau )} 352: 340: 310: 307: 301: 295: 226: 220: 207: 195: 177: 165: 143:and autocorrelation function: 130: 127: 121: 115: 89: 83: 61:signals, and are analogous to 1: 5001:Statistical signal processing 3972:Cyclostationarity is used in 3916:non-zero cyclic frequencies. 2667:, hence a signal periodic in 4705:10.1016/j.sigpro.2005.06.016 57:function). These are called 49:Wide-sense cyclostationarity 4953:10.1016/j.ymssp.2008.10.010 1966:{\displaystyle f-\alpha /2} 1932:{\displaystyle f+\alpha /2} 1894:{\displaystyle f-\alpha /2} 1860:{\displaystyle f+\alpha /2} 1163:Cyclostationary time series 5017: 4600:a frequency (unit in Hz). 4201:{\displaystyle t(\theta )} 3715:. The cyclic spectrum is: 59:wide-sense cyclostationary 4966:10.1186/s13634-018-0564-6 4947:(4). Elsevier: 987–1036. 4902:10.1186/s13634-018-0564-6 2157:, with Fourier transform 1633:Frequency domain behavior 4783:10.1109/TIT.2017.2741978 4699:(4). Elsevier: 639–697. 3909:{\displaystyle n=-1,0,1} 2663:The last summation is a 1786:rate distortion function 4834:, 66 (2), 219–228 4725:IEEE Trans. Inf. Theory 4665:{\displaystyle \omega } 4616:{\displaystyle \omega } 4565:{\displaystyle \alpha } 4303:{\displaystyle \Theta } 4221:{\displaystyle \theta } 4172:{\displaystyle \theta } 625:and can be expanded in 251:where the star denotes 18:cyclostationary process 4983:annotated presentation 4666: 4646: 4617: 4594: 4566: 4541: 4304: 4284: 4242: 4222: 4202: 4173: 4148: 3925:Cyclostationary models 3910: 3860: 3705: 3680: 3219: 3040: 2721: 2694: 2652: 2302: 2228: 2180: 2151: 2118: 2078: 2035: 1967: 1933: 1895: 1861: 1826: 1782:power spectral density 1769: 1623: 1516: 1388: 1153: 1099: 1041: 833: 772: 692: 610: 503: 409: 408:{\displaystyle T_{0},} 379: 359: 317: 276: 240: 137: 96: 4667: 4647: 4618: 4595: 4578:events per revolution 4567: 4542: 4305: 4285: 4243: 4241:{\displaystyle \tau } 4223: 4203: 4174: 4149: 3946:Polycyclostationarity 3911: 3861: 3706: 3681: 3196: 3017: 2722: 2720:{\displaystyle T_{0}} 2695: 2653: 2303: 2229: 2181: 2152: 2119: 2079: 2012: 1968: 1934: 1896: 1862: 1827: 1770: 1624: 1517: 1389: 1154: 1100: 1042: 834: 773: 669: 611: 504: 410: 380: 360: 318: 277: 275:{\displaystyle T_{0}} 241: 138: 97: 73:A stochastic process 63:wide-sense stationary 4656: 4627: 4607: 4584: 4556: 4323: 4294: 4252: 4232: 4212: 4183: 4163: 4042: 4015:condition monitoring 3940:time series analysis 3879: 3873:raised-cosine pulses 3724: 3695: 2736: 2704: 2693:{\displaystyle x(t)} 2675: 2317: 2238: 2193: 2179:{\displaystyle P(f)} 2161: 2150:{\displaystyle p(t)} 2132: 2093: 1994: 1943: 1909: 1871: 1837: 1795: 1654: 1629:with probability 1. 1526: 1403: 1180: 1116: 1056: 852: 787: 638: 520: 424: 389: 369: 327: 286: 259: 152: 106: 95:{\displaystyle x(t)} 77: 26:stationary processes 4847:, 54, 403–410 4436: 4415: 4340: 3785: 3756: 3573: 3462: 3432: 3299: 3183: 3016: 3001: 2853: 2772: 2561: 2464: 2297: 1812: 1719: 1704: 1671: 1609: 1558: 1497: 1435: 1296: 1221: 1133: 961: 884: 819: 722: 591: for all  493: for all  253:complex conjugation 4662: 4642: 4613: 4590: 4562: 4537: 4416: 4382: 4371: 4326: 4300: 4280: 4238: 4218: 4198: 4179:stands for angle, 4169: 4144: 3974:telecommunications 3906: 3856: 3771: 3727: 3701: 3676: 3674: 3559: 3445: 3418: 3220: 3169: 3002: 2954: 2806: 2743: 2717: 2690: 2665:periodic summation 2648: 2646: 2571: 2547: 2450: 2430: 2298: 2283: 2224: 2176: 2147: 2114: 2074: 1963: 1929: 1891: 1857: 1822: 1798: 1765: 1705: 1684: 1657: 1619: 1571: 1529: 1512: 1459: 1406: 1384: 1263: 1252: 1183: 1149: 1119: 1095: 1037: 914: 855: 829: 790: 768: 693: 606: 499: 405: 375: 355: 313: 272: 236: 133: 92: 38:stochastic process 4693:Signal Processing 4593:{\displaystyle f} 4515: 4380: 4353: 4019:envelope spectrum 3846: 3796: 3704:{\displaystyle *} 3657: 3584: 3532: 3443: 3369: 3194: 3142: 2952: 2911: 2804: 2562: 2415: 1645:and is equal to: 1581: 1469: 1366: 1261: 1234: 1193: 1107:cycle frequencies 1019: 912: 761: 592: 494: 378:{\displaystyle t} 5008: 4967: 4963: 4957: 4956: 4936: 4930: 4927: 4921: 4918: 4912: 4909: 4903: 4899: 4893: 4890: 4884: 4881: 4875: 4872: 4866: 4863: 4857: 4854: 4848: 4841: 4835: 4828: 4822: 4819: 4813: 4810: 4804: 4801: 4795: 4794: 4776: 4767:(5): 3810–3824. 4756: 4750: 4747: 4741: 4740: 4737:10.1109/18.61145 4720: 4709: 4708: 4688: 4671: 4669: 4668: 4663: 4651: 4649: 4648: 4643: 4622: 4620: 4619: 4614: 4599: 4597: 4596: 4591: 4571: 4569: 4568: 4563: 4546: 4544: 4543: 4538: 4533: 4524: 4518: 4517: 4516: 4508: 4486: 4485: 4446: 4445: 4435: 4427: 4414: 4410: 4401: 4397: 4381: 4373: 4370: 4339: 4334: 4309: 4307: 4306: 4301: 4289: 4287: 4286: 4281: 4264: 4263: 4247: 4245: 4244: 4239: 4227: 4225: 4224: 4219: 4207: 4205: 4204: 4199: 4178: 4176: 4175: 4170: 4153: 4151: 4150: 4145: 4118: 4117: 4054: 4053: 3915: 3913: 3912: 3907: 3865: 3863: 3862: 3857: 3852: 3848: 3847: 3845: 3844: 3832: 3819: 3818: 3797: 3795: 3794: 3784: 3779: 3770: 3755: 3754: 3753: 3744: 3735: 3710: 3708: 3707: 3702: 3685: 3683: 3682: 3677: 3675: 3668: 3664: 3663: 3662: 3658: 3656: 3655: 3643: 3615: 3614: 3585: 3583: 3582: 3572: 3567: 3558: 3550: 3543: 3538: 3537: 3533: 3531: 3530: 3518: 3490: 3489: 3461: 3456: 3444: 3442: 3441: 3431: 3426: 3417: 3409: 3402: 3397: 3396: 3392: 3391: 3370: 3368: 3367: 3355: 3327: 3326: 3298: 3297: 3296: 3278: 3273: 3272: 3262: 3261: 3260: 3242: 3237: 3236: 3218: 3213: 3195: 3193: 3192: 3182: 3177: 3168: 3160: 3153: 3148: 3147: 3143: 3141: 3140: 3128: 3106: 3105: 3084: 3083: 3071: 3070: 3039: 3034: 3015: 3010: 3000: 2996: 2991: 2990: 2980: 2976: 2971: 2970: 2953: 2951: 2950: 2938: 2930: 2923: 2917: 2916: 2912: 2910: 2909: 2897: 2863: 2862: 2852: 2848: 2843: 2842: 2832: 2828: 2823: 2822: 2805: 2803: 2802: 2790: 2771: 2770: 2769: 2760: 2751: 2726: 2724: 2723: 2718: 2716: 2715: 2699: 2697: 2696: 2691: 2657: 2655: 2654: 2649: 2647: 2637: 2636: 2615: 2614: 2602: 2601: 2570: 2560: 2555: 2540: 2533: 2532: 2511: 2510: 2498: 2497: 2463: 2458: 2449: 2448: 2429: 2408: 2392: 2391: 2333: 2332: 2307: 2305: 2304: 2299: 2296: 2291: 2276: 2275: 2270: 2264: 2263: 2254: 2233: 2231: 2230: 2225: 2214: 2213: 2185: 2183: 2182: 2177: 2156: 2154: 2153: 2148: 2123: 2121: 2120: 2115: 2113: 2105: 2104: 2083: 2081: 2080: 2075: 2070: 2069: 2045: 2044: 2034: 2029: 1972: 1970: 1969: 1964: 1959: 1938: 1936: 1935: 1930: 1925: 1900: 1898: 1897: 1892: 1887: 1866: 1864: 1863: 1858: 1853: 1831: 1829: 1828: 1823: 1811: 1806: 1774: 1772: 1771: 1766: 1758: 1753: 1752: 1718: 1713: 1703: 1695: 1670: 1665: 1628: 1626: 1625: 1620: 1608: 1607: 1606: 1597: 1588: 1583: 1582: 1574: 1557: 1556: 1555: 1546: 1537: 1521: 1519: 1518: 1513: 1511: 1507: 1496: 1495: 1494: 1485: 1476: 1471: 1470: 1462: 1434: 1433: 1432: 1423: 1414: 1393: 1391: 1390: 1385: 1377: 1372: 1371: 1367: 1365: 1364: 1352: 1324: 1323: 1295: 1291: 1282: 1278: 1262: 1254: 1251: 1220: 1219: 1218: 1209: 1200: 1195: 1194: 1186: 1169:fraction-of-time 1158: 1156: 1155: 1150: 1132: 1127: 1104: 1102: 1101: 1096: 1091: 1076: 1075: 1066: 1052:The frequencies 1046: 1044: 1043: 1038: 1030: 1025: 1024: 1020: 1018: 1017: 1005: 971: 970: 960: 956: 951: 950: 940: 936: 931: 930: 913: 911: 910: 898: 883: 882: 881: 872: 863: 838: 836: 835: 830: 818: 817: 816: 807: 798: 777: 775: 774: 769: 767: 766: 762: 760: 759: 747: 721: 720: 719: 710: 701: 691: 686: 650: 649: 615: 613: 612: 607: 593: 590: 579: 578: 560: 559: 532: 531: 508: 506: 505: 500: 495: 492: 484: 483: 414: 412: 411: 406: 401: 400: 384: 382: 381: 376: 364: 362: 361: 356: 339: 338: 322: 320: 319: 314: 281: 279: 278: 273: 271: 270: 245: 243: 242: 237: 219: 218: 164: 163: 142: 140: 139: 134: 101: 99: 98: 93: 5016: 5015: 5011: 5010: 5009: 5007: 5006: 5005: 4991: 4990: 4975: 4970: 4964: 4960: 4938: 4937: 4933: 4928: 4924: 4919: 4915: 4910: 4906: 4900: 4896: 4891: 4887: 4882: 4878: 4873: 4869: 4864: 4860: 4855: 4851: 4842: 4838: 4829: 4825: 4820: 4816: 4811: 4807: 4802: 4798: 4758: 4757: 4753: 4748: 4744: 4722: 4721: 4712: 4690: 4689: 4685: 4681: 4675: 4654: 4653: 4625: 4624: 4605: 4604: 4582: 4581: 4554: 4553: 4487: 4462: 4437: 4321: 4320: 4292: 4291: 4255: 4250: 4249: 4230: 4229: 4210: 4209: 4181: 4180: 4161: 4160: 4109: 4045: 4040: 4039: 4006: 3994:Queueing theory 3978:synchronization 3966: 3957: 3948: 3935:autoregressions 3927: 3919: 3877: 3876: 3836: 3824: 3820: 3810: 3786: 3745: 3722: 3721: 3693: 3692: 3673: 3672: 3647: 3628: 3606: 3605: 3601: 3574: 3548: 3547: 3522: 3500: 3481: 3433: 3407: 3406: 3383: 3359: 3337: 3318: 3288: 3264: 3252: 3228: 3184: 3158: 3157: 3132: 3110: 3097: 3075: 3062: 2982: 2962: 2942: 2928: 2927: 2901: 2879: 2854: 2834: 2814: 2794: 2782: 2761: 2734: 2733: 2707: 2702: 2701: 2673: 2672: 2645: 2644: 2628: 2606: 2593: 2538: 2537: 2524: 2502: 2489: 2440: 2406: 2405: 2383: 2349: 2324: 2315: 2314: 2265: 2255: 2236: 2235: 2205: 2191: 2190: 2159: 2158: 2130: 2129: 2096: 2091: 2090: 2061: 2036: 1992: 1991: 1979: 1941: 1940: 1907: 1906: 1869: 1868: 1835: 1834: 1793: 1792: 1729: 1652: 1651: 1639:cyclic spectrum 1635: 1598: 1547: 1524: 1523: 1486: 1458: 1454: 1424: 1401: 1400: 1356: 1334: 1315: 1210: 1178: 1177: 1165: 1114: 1113: 1067: 1054: 1053: 1009: 987: 962: 942: 922: 902: 873: 850: 849: 808: 785: 784: 751: 732: 711: 641: 636: 635: 570: 551: 523: 518: 517: 475: 422: 421: 392: 387: 386: 367: 366: 330: 325: 324: 284: 283: 262: 257: 256: 210: 155: 150: 149: 104: 103: 75: 74: 71: 55:autocorrelation 51: 34: 12: 11: 5: 5014: 5012: 5004: 5003: 4993: 4992: 4989: 4988: 4974: 4973:External links 4971: 4969: 4968: 4958: 4931: 4922: 4913: 4904: 4894: 4885: 4876: 4867: 4858: 4849: 4836: 4823: 4814: 4805: 4796: 4751: 4742: 4731:(1): 216–218. 4710: 4682: 4680: 4677: 4661: 4641: 4638: 4635: 4632: 4612: 4589: 4561: 4550: 4549: 4548: 4547: 4536: 4532: 4527: 4523: 4514: 4511: 4506: 4503: 4500: 4497: 4494: 4490: 4484: 4481: 4478: 4475: 4472: 4469: 4465: 4461: 4458: 4455: 4452: 4449: 4444: 4440: 4434: 4431: 4426: 4423: 4419: 4413: 4409: 4405: 4400: 4396: 4392: 4389: 4385: 4379: 4376: 4369: 4366: 4363: 4360: 4356: 4352: 4349: 4346: 4343: 4338: 4333: 4329: 4299: 4279: 4276: 4273: 4270: 4267: 4262: 4258: 4237: 4217: 4197: 4194: 4191: 4188: 4168: 4157: 4156: 4155: 4154: 4142: 4139: 4136: 4133: 4130: 4127: 4124: 4121: 4116: 4112: 4108: 4105: 4102: 4099: 4096: 4093: 4090: 4087: 4084: 4081: 4078: 4075: 4072: 4069: 4066: 4063: 4060: 4057: 4052: 4048: 4005: 4002: 4001: 4000: 3997: 3991: 3984: 3981: 3970: 3965: 3962: 3956: 3953: 3947: 3944: 3926: 3923: 3905: 3902: 3899: 3896: 3893: 3890: 3887: 3884: 3869: 3868: 3867: 3866: 3855: 3851: 3843: 3839: 3835: 3830: 3827: 3823: 3817: 3813: 3809: 3806: 3803: 3800: 3793: 3789: 3783: 3778: 3774: 3768: 3765: 3762: 3759: 3752: 3748: 3743: 3739: 3734: 3730: 3700: 3689: 3688: 3687: 3686: 3671: 3667: 3661: 3654: 3650: 3646: 3641: 3638: 3635: 3631: 3627: 3624: 3621: 3618: 3613: 3609: 3604: 3600: 3597: 3594: 3591: 3588: 3581: 3577: 3571: 3566: 3562: 3556: 3553: 3551: 3549: 3546: 3542: 3536: 3529: 3525: 3521: 3516: 3513: 3510: 3507: 3503: 3499: 3496: 3493: 3488: 3484: 3480: 3477: 3474: 3471: 3468: 3465: 3460: 3455: 3452: 3448: 3440: 3436: 3430: 3425: 3421: 3415: 3412: 3410: 3408: 3405: 3401: 3395: 3390: 3386: 3382: 3379: 3376: 3373: 3366: 3362: 3358: 3353: 3350: 3347: 3344: 3340: 3336: 3333: 3330: 3325: 3321: 3317: 3314: 3311: 3308: 3305: 3302: 3295: 3291: 3287: 3284: 3281: 3277: 3271: 3267: 3259: 3255: 3251: 3248: 3245: 3241: 3235: 3231: 3227: 3223: 3217: 3212: 3209: 3206: 3203: 3199: 3191: 3187: 3181: 3176: 3172: 3166: 3163: 3161: 3159: 3156: 3152: 3146: 3139: 3135: 3131: 3126: 3123: 3120: 3117: 3113: 3109: 3104: 3100: 3096: 3093: 3090: 3087: 3082: 3078: 3074: 3069: 3065: 3061: 3058: 3055: 3052: 3049: 3046: 3043: 3038: 3033: 3030: 3027: 3024: 3020: 3014: 3009: 3005: 2999: 2995: 2989: 2985: 2979: 2975: 2969: 2965: 2961: 2957: 2949: 2945: 2941: 2936: 2933: 2931: 2929: 2926: 2922: 2915: 2908: 2904: 2900: 2895: 2892: 2889: 2886: 2882: 2878: 2875: 2872: 2869: 2866: 2861: 2857: 2851: 2847: 2841: 2837: 2831: 2827: 2821: 2817: 2813: 2809: 2801: 2797: 2793: 2788: 2785: 2783: 2781: 2778: 2775: 2768: 2764: 2759: 2755: 2750: 2746: 2742: 2741: 2714: 2710: 2689: 2686: 2683: 2680: 2661: 2660: 2659: 2658: 2643: 2640: 2635: 2631: 2627: 2624: 2621: 2618: 2613: 2609: 2605: 2600: 2596: 2592: 2589: 2586: 2583: 2580: 2577: 2574: 2569: 2565: 2559: 2554: 2550: 2546: 2543: 2541: 2539: 2536: 2531: 2527: 2523: 2520: 2517: 2514: 2509: 2505: 2501: 2496: 2492: 2488: 2485: 2482: 2479: 2476: 2473: 2470: 2467: 2462: 2457: 2453: 2447: 2443: 2439: 2436: 2433: 2428: 2425: 2422: 2418: 2414: 2411: 2409: 2407: 2404: 2401: 2398: 2395: 2390: 2386: 2382: 2379: 2376: 2373: 2370: 2367: 2364: 2361: 2358: 2355: 2352: 2350: 2348: 2345: 2342: 2339: 2336: 2331: 2327: 2323: 2322: 2295: 2290: 2286: 2282: 2279: 2274: 2269: 2262: 2258: 2253: 2249: 2246: 2243: 2223: 2220: 2217: 2212: 2208: 2204: 2201: 2198: 2175: 2172: 2169: 2166: 2146: 2143: 2140: 2137: 2112: 2108: 2103: 2099: 2087: 2086: 2085: 2084: 2073: 2068: 2064: 2060: 2057: 2054: 2051: 2048: 2043: 2039: 2033: 2028: 2025: 2022: 2019: 2015: 2011: 2008: 2005: 2002: 1999: 1978: 1975: 1962: 1958: 1954: 1951: 1948: 1928: 1924: 1920: 1917: 1914: 1890: 1886: 1882: 1879: 1876: 1856: 1852: 1848: 1845: 1842: 1821: 1818: 1815: 1810: 1805: 1801: 1778: 1777: 1776: 1775: 1764: 1761: 1757: 1751: 1748: 1745: 1742: 1739: 1736: 1732: 1728: 1725: 1722: 1717: 1712: 1708: 1702: 1699: 1694: 1691: 1687: 1683: 1680: 1677: 1674: 1669: 1664: 1660: 1634: 1631: 1618: 1615: 1612: 1605: 1601: 1596: 1592: 1587: 1580: 1577: 1570: 1567: 1564: 1561: 1554: 1550: 1545: 1541: 1536: 1532: 1510: 1506: 1503: 1500: 1493: 1489: 1484: 1480: 1475: 1468: 1465: 1457: 1453: 1450: 1447: 1444: 1441: 1438: 1431: 1427: 1422: 1418: 1413: 1409: 1397: 1396: 1395: 1394: 1383: 1380: 1376: 1370: 1363: 1359: 1355: 1350: 1347: 1344: 1341: 1337: 1333: 1330: 1327: 1322: 1318: 1314: 1311: 1308: 1305: 1302: 1299: 1294: 1290: 1286: 1281: 1277: 1273: 1270: 1266: 1260: 1257: 1250: 1247: 1244: 1241: 1237: 1233: 1230: 1227: 1224: 1217: 1213: 1208: 1204: 1199: 1192: 1189: 1164: 1161: 1148: 1145: 1142: 1139: 1136: 1131: 1126: 1122: 1094: 1090: 1086: 1083: 1079: 1074: 1070: 1065: 1061: 1050: 1049: 1048: 1047: 1036: 1033: 1029: 1023: 1016: 1012: 1008: 1003: 1000: 997: 994: 990: 986: 983: 980: 977: 974: 969: 965: 959: 955: 949: 945: 939: 935: 929: 925: 921: 917: 909: 905: 901: 896: 893: 890: 887: 880: 876: 871: 867: 862: 858: 843:and equal to: 828: 825: 822: 815: 811: 806: 802: 797: 793: 781: 780: 779: 778: 765: 758: 754: 750: 745: 742: 739: 735: 731: 728: 725: 718: 714: 709: 705: 700: 696: 690: 685: 682: 679: 676: 672: 668: 665: 662: 659: 656: 653: 648: 644: 627:Fourier series 619: 618: 617: 616: 605: 602: 599: 596: 588: 585: 582: 577: 573: 569: 566: 563: 558: 554: 550: 547: 544: 541: 538: 535: 530: 526: 512: 511: 510: 509: 498: 490: 487: 482: 478: 474: 471: 468: 465: 462: 459: 456: 453: 450: 447: 444: 441: 438: 435: 432: 429: 404: 399: 395: 374: 365:are cyclic in 354: 351: 348: 345: 342: 337: 333: 312: 309: 306: 303: 300: 297: 294: 291: 269: 265: 249: 248: 247: 246: 234: 231: 228: 225: 222: 217: 213: 209: 206: 203: 200: 197: 194: 191: 188: 185: 182: 179: 176: 173: 170: 167: 162: 158: 132: 129: 126: 123: 120: 117: 114: 111: 91: 88: 85: 82: 70: 67: 50: 47: 33: 30: 13: 10: 9: 6: 4: 3: 2: 5013: 5002: 4999: 4998: 4996: 4987: 4984: 4981: 4977: 4976: 4972: 4962: 4959: 4954: 4950: 4946: 4942: 4935: 4932: 4926: 4923: 4917: 4914: 4908: 4905: 4898: 4895: 4889: 4886: 4880: 4877: 4871: 4868: 4862: 4859: 4853: 4850: 4846: 4840: 4837: 4833: 4827: 4824: 4818: 4815: 4809: 4806: 4800: 4797: 4792: 4788: 4784: 4780: 4775: 4770: 4766: 4762: 4755: 4752: 4746: 4743: 4738: 4734: 4730: 4726: 4719: 4717: 4715: 4711: 4706: 4702: 4698: 4694: 4687: 4684: 4678: 4676: 4673: 4659: 4639: 4636: 4633: 4630: 4610: 4601: 4587: 4579: 4575: 4559: 4534: 4525: 4509: 4504: 4501: 4498: 4495: 4492: 4488: 4482: 4479: 4476: 4473: 4470: 4467: 4463: 4456: 4453: 4450: 4442: 4438: 4429: 4421: 4417: 4411: 4407: 4403: 4398: 4394: 4390: 4387: 4383: 4377: 4374: 4364: 4358: 4350: 4344: 4336: 4331: 4327: 4319: 4318: 4317: 4316: 4315: 4313: 4274: 4271: 4268: 4260: 4256: 4235: 4215: 4192: 4186: 4166: 4140: 4128: 4122: 4114: 4110: 4103: 4100: 4094: 4088: 4082: 4076: 4070: 4064: 4061: 4058: 4050: 4046: 4038: 4037: 4036: 4035: 4034: 4032: 4027: 4022: 4020: 4016: 4013:(NVH) and in 4012: 4003: 3998: 3995: 3992: 3989: 3985: 3982: 3979: 3975: 3971: 3968: 3967: 3963: 3961: 3954: 3952: 3945: 3943: 3941: 3936: 3932: 3924: 3922: 3917: 3903: 3900: 3897: 3894: 3891: 3888: 3885: 3882: 3874: 3853: 3849: 3841: 3837: 3833: 3828: 3825: 3821: 3815: 3811: 3804: 3798: 3791: 3787: 3781: 3776: 3772: 3766: 3760: 3750: 3746: 3741: 3737: 3732: 3728: 3720: 3719: 3718: 3717: 3716: 3714: 3698: 3669: 3665: 3659: 3652: 3648: 3644: 3639: 3636: 3633: 3629: 3622: 3619: 3611: 3607: 3602: 3598: 3592: 3586: 3579: 3575: 3569: 3564: 3560: 3554: 3552: 3544: 3534: 3527: 3523: 3519: 3514: 3511: 3508: 3505: 3501: 3494: 3486: 3482: 3475: 3472: 3469: 3463: 3450: 3446: 3438: 3434: 3428: 3423: 3419: 3413: 3411: 3403: 3388: 3384: 3380: 3377: 3374: 3364: 3360: 3356: 3351: 3348: 3345: 3342: 3338: 3331: 3323: 3319: 3312: 3309: 3306: 3300: 3293: 3289: 3285: 3282: 3279: 3275: 3269: 3265: 3257: 3253: 3249: 3246: 3243: 3239: 3233: 3229: 3225: 3221: 3207: 3204: 3201: 3197: 3189: 3185: 3179: 3174: 3170: 3164: 3162: 3154: 3144: 3137: 3133: 3129: 3124: 3121: 3118: 3115: 3111: 3102: 3098: 3094: 3091: 3088: 3080: 3076: 3067: 3063: 3059: 3056: 3053: 3050: 3047: 3041: 3028: 3025: 3022: 3018: 3012: 3007: 3003: 2997: 2993: 2987: 2983: 2977: 2973: 2967: 2963: 2959: 2955: 2947: 2943: 2939: 2934: 2932: 2924: 2913: 2906: 2902: 2898: 2893: 2890: 2887: 2884: 2880: 2873: 2870: 2867: 2859: 2855: 2849: 2845: 2839: 2835: 2829: 2825: 2819: 2815: 2811: 2807: 2799: 2795: 2791: 2786: 2784: 2776: 2766: 2762: 2757: 2753: 2748: 2744: 2732: 2731: 2730: 2729: 2728: 2712: 2708: 2684: 2678: 2670: 2666: 2641: 2633: 2629: 2625: 2622: 2619: 2611: 2607: 2598: 2594: 2590: 2587: 2584: 2581: 2578: 2572: 2567: 2563: 2557: 2552: 2548: 2544: 2542: 2529: 2525: 2521: 2518: 2515: 2507: 2503: 2494: 2490: 2486: 2483: 2480: 2477: 2474: 2468: 2460: 2455: 2451: 2445: 2441: 2434: 2426: 2423: 2420: 2416: 2412: 2410: 2396: 2388: 2384: 2377: 2374: 2371: 2365: 2359: 2353: 2351: 2343: 2340: 2337: 2329: 2325: 2313: 2312: 2311: 2310: 2309: 2293: 2288: 2284: 2280: 2272: 2260: 2256: 2244: 2221: 2218: 2210: 2206: 2199: 2187: 2170: 2164: 2141: 2135: 2127: 2106: 2101: 2097: 2066: 2062: 2058: 2055: 2052: 2046: 2041: 2037: 2023: 2020: 2017: 2013: 2009: 2003: 1997: 1990: 1989: 1988: 1987: 1986: 1984: 1976: 1974: 1960: 1956: 1952: 1949: 1946: 1926: 1922: 1918: 1915: 1912: 1902: 1888: 1884: 1880: 1877: 1874: 1854: 1850: 1846: 1843: 1840: 1816: 1808: 1803: 1799: 1789: 1787: 1783: 1762: 1759: 1749: 1746: 1743: 1740: 1737: 1734: 1730: 1723: 1715: 1710: 1706: 1697: 1689: 1685: 1681: 1675: 1667: 1662: 1658: 1650: 1649: 1648: 1647: 1646: 1644: 1640: 1632: 1630: 1613: 1603: 1599: 1594: 1590: 1585: 1578: 1575: 1568: 1562: 1552: 1548: 1543: 1539: 1534: 1530: 1508: 1501: 1491: 1487: 1482: 1478: 1473: 1466: 1463: 1455: 1451: 1445: 1439: 1429: 1425: 1420: 1416: 1411: 1407: 1381: 1378: 1368: 1361: 1357: 1353: 1348: 1345: 1342: 1339: 1335: 1328: 1320: 1316: 1309: 1306: 1303: 1297: 1292: 1288: 1284: 1279: 1275: 1271: 1268: 1264: 1258: 1255: 1245: 1239: 1231: 1225: 1215: 1211: 1206: 1202: 1197: 1190: 1187: 1176: 1175: 1174: 1173: 1172: 1170: 1162: 1160: 1146: 1143: 1137: 1129: 1124: 1120: 1110: 1108: 1092: 1084: 1081: 1077: 1072: 1068: 1063: 1059: 1034: 1031: 1021: 1014: 1010: 1006: 1001: 998: 995: 992: 988: 981: 978: 975: 967: 963: 957: 953: 947: 943: 937: 933: 927: 923: 919: 915: 907: 903: 899: 894: 888: 878: 874: 869: 865: 860: 856: 848: 847: 846: 845: 844: 842: 823: 813: 809: 804: 800: 795: 791: 763: 756: 752: 748: 743: 740: 737: 733: 726: 716: 712: 707: 703: 698: 694: 680: 677: 674: 670: 666: 660: 657: 654: 646: 642: 634: 633: 632: 631: 630: 628: 624: 603: 600: 597: 594: 583: 580: 575: 571: 567: 564: 556: 552: 548: 542: 539: 536: 528: 524: 516: 515: 514: 513: 496: 480: 476: 472: 469: 463: 457: 451: 442: 436: 430: 420: 419: 418: 417: 416: 402: 397: 393: 372: 349: 346: 343: 335: 331: 304: 298: 292: 267: 263: 254: 232: 223: 215: 211: 204: 201: 198: 192: 186: 180: 174: 171: 168: 160: 156: 148: 147: 146: 145: 144: 124: 118: 112: 86: 80: 68: 66: 64: 60: 56: 48: 46: 43: 39: 31: 29: 27: 23: 19: 4986:presentation 4961: 4944: 4940: 4934: 4925: 4916: 4907: 4897: 4888: 4879: 4870: 4861: 4852: 4839: 4826: 4817: 4808: 4799: 4764: 4760: 4754: 4745: 4728: 4724: 4696: 4692: 4686: 4674: 4602: 4577: 4573: 4551: 4311: 4158: 4030: 4025: 4023: 4018: 4007: 3988:econometrics 3964:Applications 3958: 3949: 3928: 3918: 3870: 3690: 2671:. This way, 2668: 2662: 2189:By assuming 2188: 2088: 1980: 1903: 1790: 1779: 1642: 1638: 1636: 1398: 1168: 1166: 1111: 1106: 1051: 840: 782: 622: 620: 385:with period 250: 72: 58: 52: 35: 17: 15: 3976:for signal 3713:convolution 3711:indicating 1791:The reason 1105:are called 42:time series 4980:manuscript 4845:Biometrika 4832:Biometrika 4774:1505.05586 4679:References 839:is called 32:Definition 4660:ω 4637:ω 4631:θ 4611:ω 4576:(unit in 4560:α 4535:θ 4526:τ 4513:Θ 4510:θ 4505:α 4502:π 4493:− 4483:τ 4477:π 4468:− 4457:τ 4451:θ 4433:∞ 4425:∞ 4422:− 4418:∫ 4388:− 4384:∫ 4368:∞ 4362:→ 4337:α 4298:Θ 4275:τ 4269:θ 4236:τ 4216:θ 4193:θ 4167:θ 4129:θ 4115:∗ 4104:τ 4095:θ 4077:⁡ 4065:τ 4059:θ 4033:is used, 3889:− 3829:− 3816:∗ 3773:σ 3699:∗ 3660:τ 3640:π 3623:τ 3620:− 3612:∗ 3599:∗ 3593:τ 3561:σ 3545:λ 3535:λ 3515:π 3506:− 3495:λ 3487:∗ 3476:τ 3470:λ 3459:∞ 3454:∞ 3451:− 3447:∫ 3420:σ 3404:λ 3375:λ 3352:π 3343:− 3332:λ 3324:∗ 3313:τ 3307:λ 3283:− 3247:− 3226:− 3222:∫ 3216:∞ 3211:∞ 3208:− 3198:∑ 3171:σ 3125:π 3116:− 3092:− 3081:∗ 3057:− 3054:τ 3037:∞ 3032:∞ 3029:− 3019:∑ 3004:σ 2960:− 2956:∫ 2894:π 2885:− 2874:τ 2812:− 2808:∫ 2777:τ 2623:− 2612:∗ 2588:− 2585:τ 2564:∑ 2549:σ 2519:− 2508:∗ 2484:− 2481:τ 2461:∗ 2435:⁡ 2417:∑ 2389:∗ 2378:τ 2360:⁡ 2344:τ 2285:σ 2245:⁡ 2200:⁡ 2107:∈ 2056:− 2032:∞ 2027:∞ 2024:− 2014:∑ 1953:α 1950:− 1919:α 1881:α 1878:− 1847:α 1809:α 1760:τ 1750:τ 1744:π 1735:− 1724:τ 1716:α 1701:∞ 1693:∞ 1690:− 1686:∫ 1668:α 1614:τ 1579:^ 1563:τ 1502:τ 1467:^ 1452:⁡ 1440:τ 1349:π 1340:− 1321:∗ 1310:τ 1269:− 1265:∫ 1249:∞ 1243:→ 1226:τ 1191:^ 1144:≠ 1138:τ 1085:∈ 1002:π 993:− 982:τ 920:− 916:∫ 889:τ 824:τ 744:π 727:τ 689:∞ 684:∞ 681:− 671:∑ 661:τ 601:τ 584:τ 543:τ 458:⁡ 431:⁡ 350:τ 293:⁡ 216:∗ 205:τ 187:⁡ 175:τ 113:⁡ 4995:Category 3871:Typical 1985: : 282:if both 102:of mean 4791:5014143 4789:  4580:) and 4572:is an 4552:where 4159:where 2126:i.i.d. 2089:where 783:where 415:i.e.: 22:signal 4787:S2CID 4769:arXiv 4574:order 4026:angle 3691:with 20:is a 4228:and 2234:and 2124:are 323:and 4949:doi 4779:doi 4733:doi 4701:doi 4355:lim 3986:In 1641:or 1236:lim 4997:: 4945:23 4943:. 4785:. 4777:. 4765:65 4763:. 4729:37 4727:. 4713:^ 4697:86 4695:. 4314:, 3942:. 1159:. 1109:. 629:: 16:A 4955:. 4951:: 4793:. 4781:: 4771:: 4739:. 4735:: 4707:. 4703:: 4640:t 4634:= 4588:f 4531:d 4522:d 4499:2 4496:j 4489:e 4480:f 4474:2 4471:j 4464:e 4460:) 4454:, 4448:( 4443:x 4439:R 4430:+ 4412:2 4408:/ 4404:S 4399:2 4395:/ 4391:S 4378:S 4375:1 4365:+ 4359:S 4351:= 4348:) 4345:f 4342:( 4332:x 4328:S 4278:) 4272:; 4266:( 4261:x 4257:R 4196:) 4190:( 4187:t 4141:, 4138:} 4135:) 4132:) 4126:( 4123:t 4120:( 4111:x 4107:) 4101:+ 4098:) 4092:( 4089:t 4086:( 4083:x 4080:{ 4074:E 4071:= 4068:) 4062:, 4056:( 4051:x 4047:R 3904:1 3901:, 3898:0 3895:, 3892:1 3886:= 3883:n 3854:. 3850:) 3842:0 3838:T 3834:n 3826:f 3822:( 3812:P 3808:) 3805:f 3802:( 3799:P 3792:0 3788:T 3782:2 3777:a 3767:= 3764:) 3761:f 3758:( 3751:0 3747:T 3742:/ 3738:n 3733:x 3729:S 3670:. 3666:} 3653:0 3649:T 3645:n 3637:2 3634:j 3630:e 3626:) 3617:( 3608:p 3603:{ 3596:) 3590:( 3587:p 3580:0 3576:T 3570:2 3565:a 3555:= 3541:d 3528:0 3524:T 3520:n 3512:2 3509:j 3502:e 3498:) 3492:( 3483:p 3479:) 3473:+ 3467:( 3464:p 3439:0 3435:T 3429:2 3424:a 3414:= 3400:d 3394:) 3389:0 3385:T 3381:k 3378:+ 3372:( 3365:0 3361:T 3357:n 3349:2 3346:j 3339:e 3335:) 3329:( 3320:p 3316:) 3310:+ 3304:( 3301:p 3294:0 3290:T 3286:k 3280:2 3276:/ 3270:0 3266:T 3258:0 3254:T 3250:k 3244:2 3240:/ 3234:0 3230:T 3205:= 3202:k 3190:0 3186:T 3180:2 3175:a 3165:= 3155:t 3151:d 3145:t 3138:0 3134:T 3130:n 3122:2 3119:j 3112:e 3108:) 3103:0 3099:T 3095:k 3089:t 3086:( 3077:p 3073:) 3068:0 3064:T 3060:k 3051:+ 3048:t 3045:( 3042:p 3026:= 3023:k 3013:2 3008:a 2998:2 2994:/ 2988:0 2984:T 2978:2 2974:/ 2968:0 2964:T 2948:0 2944:T 2940:1 2935:= 2925:t 2921:d 2914:t 2907:0 2903:T 2899:n 2891:2 2888:j 2881:e 2877:) 2871:, 2868:t 2865:( 2860:x 2856:R 2850:2 2846:/ 2840:0 2836:T 2830:2 2826:/ 2820:0 2816:T 2800:0 2796:T 2792:1 2787:= 2780:) 2774:( 2767:0 2763:T 2758:/ 2754:n 2749:x 2745:R 2713:0 2709:T 2688:) 2685:t 2682:( 2679:x 2669:t 2642:. 2639:) 2634:0 2630:T 2626:k 2620:t 2617:( 2608:p 2604:) 2599:0 2595:T 2591:k 2582:+ 2579:t 2576:( 2573:p 2568:k 2558:2 2553:a 2545:= 2535:) 2530:0 2526:T 2522:n 2516:t 2513:( 2504:p 2500:) 2495:0 2491:T 2487:k 2478:+ 2475:t 2472:( 2469:p 2466:] 2456:n 2452:a 2446:k 2442:a 2438:[ 2432:E 2427:n 2424:, 2421:k 2413:= 2403:] 2400:) 2397:t 2394:( 2385:x 2381:) 2375:+ 2372:t 2369:( 2366:x 2363:[ 2357:E 2354:= 2347:) 2341:, 2338:t 2335:( 2330:x 2326:R 2294:2 2289:a 2281:= 2278:] 2273:2 2268:| 2261:k 2257:a 2252:| 2248:[ 2242:E 2222:0 2219:= 2216:] 2211:k 2207:a 2203:[ 2197:E 2174:) 2171:f 2168:( 2165:P 2145:) 2142:t 2139:( 2136:p 2111:C 2102:k 2098:a 2072:) 2067:0 2063:T 2059:k 2053:t 2050:( 2047:p 2042:k 2038:a 2021:= 2018:k 2010:= 2007:) 2004:t 2001:( 1998:x 1961:2 1957:/ 1947:f 1927:2 1923:/ 1916:+ 1913:f 1889:2 1885:/ 1875:f 1855:2 1851:/ 1844:+ 1841:f 1820:) 1817:f 1814:( 1804:x 1800:S 1763:. 1756:d 1747:f 1741:2 1738:j 1731:e 1727:) 1721:( 1711:x 1707:R 1698:+ 1682:= 1679:) 1676:f 1673:( 1663:x 1659:S 1617:) 1611:( 1604:0 1600:T 1595:/ 1591:n 1586:x 1576:R 1569:= 1566:) 1560:( 1553:0 1549:T 1544:/ 1540:n 1535:x 1531:R 1509:] 1505:) 1499:( 1492:0 1488:T 1483:/ 1479:n 1474:x 1464:R 1456:[ 1449:E 1446:= 1443:) 1437:( 1430:0 1426:T 1421:/ 1417:n 1412:x 1408:R 1382:. 1379:t 1375:d 1369:t 1362:0 1358:T 1354:n 1346:2 1343:j 1336:e 1332:) 1329:t 1326:( 1317:x 1313:) 1307:+ 1304:t 1301:( 1298:x 1293:2 1289:/ 1285:T 1280:2 1276:/ 1272:T 1259:T 1256:1 1246:+ 1240:T 1232:= 1229:) 1223:( 1216:0 1212:T 1207:/ 1203:n 1198:x 1188:R 1147:0 1141:) 1135:( 1130:0 1125:x 1121:R 1093:, 1089:Z 1082:n 1078:, 1073:0 1069:T 1064:/ 1060:n 1035:. 1032:t 1028:d 1022:t 1015:0 1011:T 1007:n 999:2 996:j 989:e 985:) 979:, 976:t 973:( 968:x 964:R 958:2 954:/ 948:0 944:T 938:2 934:/ 928:0 924:T 908:0 904:T 900:1 895:= 892:) 886:( 879:0 875:T 870:/ 866:n 861:x 857:R 827:) 821:( 814:0 810:T 805:/ 801:n 796:x 792:R 764:t 757:0 753:T 749:n 741:2 738:j 734:e 730:) 724:( 717:0 713:T 708:/ 704:n 699:x 695:R 678:= 675:n 667:= 664:) 658:, 655:t 652:( 647:x 643:R 623:t 604:. 598:, 595:t 587:) 581:; 576:0 572:T 568:+ 565:t 562:( 557:x 553:R 549:= 546:) 540:, 537:t 534:( 529:x 525:R 497:t 489:] 486:) 481:0 477:T 473:+ 470:t 467:( 464:x 461:[ 455:E 452:= 449:] 446:) 443:t 440:( 437:x 434:[ 428:E 403:, 398:0 394:T 373:t 353:) 347:, 344:t 341:( 336:x 332:R 311:] 308:) 305:t 302:( 299:x 296:[ 290:E 268:0 264:T 233:, 230:} 227:) 224:t 221:( 212:x 208:) 202:+ 199:t 196:( 193:x 190:{ 184:E 181:= 178:) 172:, 169:t 166:( 161:x 157:R 131:] 128:) 125:t 122:( 119:x 116:[ 110:E 90:) 87:t 84:( 81:x

Index

signal
stationary processes
stochastic process
time series
autocorrelation
wide-sense stationary
complex conjugation
Fourier series
power spectral density
rate distortion function
linearly modulated digital signal
i.i.d.
periodic summation
convolution
raised-cosine pulses
autoregressive moving average models
autoregressions
time series analysis
telecommunications
synchronization
econometrics
Queueing theory
noise, vibration, and harshness
condition monitoring
doi
10.1016/j.sigpro.2005.06.016



doi

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