Noise pseudorandom

Husimi Y, Wada A (1976) Time-domain measurement of dielectric dispersion as a response to pseudorandom noise. Rev Sci Instrum 47 213-19... 

Moller, A.R. 1983. Use of pseudorandom noise in studies of frequency selectivity the periphery of the auditory system. Biol. Cybern. 47 95. 

J. Borishand J.B. Angell, An Effident Algorithm for Measuring the Impulse Response Using Pseudorandom Noise, Journal of the Audio Engineering Society, vol. 31, no. 7/8, pp. 478 88, 1983. 

Pseudorandom noise A deterministic, but noisehke, waveform typically produced by a digital pseudorandom number generator. Two pseudorandom generators will generate identical waveforms if they are initiahzed with the same seed and clocked synchronously. 

Pseudorandom noise A noise signal that satisfies one or more of the standard tests for statistical randomness. Although it seems to lack any definite pattern, there is a sequence of pulses that repeats after a long time interval. 

The satellites broadcast on two carrier frequencies LI at 1575.42 MHz and L2 at 1227.6 MHz. Each satellite broadcasts a spread spectrum waveform, called a pseudorandom noise (PRN) code, on LI andL2, and each satellite is identified by the PRN code it transmits. There are two types of PRN codes. The first type is a coarse acquisition (C/A) code, with a chip rate of 1023 chips per millisecond. The second is a precision (P) code, with a chip rate of 10,230 chips per millisecond. The C7A code is broadcast on L1, and the P code is broadcast onbothL 1 and L2. GPS reception is line-of-sight, which means that the antenna must have a clear view of the slgr. 

In practice, it is desirable to have a special kind of excitation noise. Smith and his coworkers (34, 36) demonstrated that the best choice is an odd-harmonic, phase-varying pseudorandom white noise of the type displayed in Figure 10.8.3. This noise is the superposition of signals at several frequencies (15 in the example), all of which are odd harmonics of the lowest frequency. The choice of odd harmonics ensures that second-... 

Pseudorandom white noise (PRWN) Complex periodic signal with discrete components of identical amplitudes at frequencies = 10.07 N Hz, where N = 1,2,3,4...100. Phases are randomized (71 at distribution). 

Stochastic identification techniques, in principle, provide a more reliable method of determining the process transfer function. Most workers have used the Box and Jenkins [59] time-series analysis techniques to develop dynamic models. An introduction to these methods is given by Davies [60]. In stochastic identification, a low amplitude sequence (usually a pseudorandom binary sequence, PRBS) is used to perturb the setting of the manipulated variable. The sequence generally has an implementation period smaller than the process response time. By evaiuating the auto- and cross-correlations of the input series and the corresponding output data, a quantitative model can be constructed. The parameters of the model can be determined by using a least squares analysis on the input and output sequences. Because this identification technique can handle many more parameters than simple first-order plus dead-time models, the process and its related noise can be modeled more accurately. 

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