Beta Filter in electrical engineering from the University of Southern California.
He has coach outlet store in atlanta 17 years experience in electronic circuit and system design and simulation. He has been programming in C for five years and working with alpha beta filters for four years. Bob can be reached on GEnie at RPENOYER. or on Compuserve at 71603,1335. Data collected or received in the real world is nearly always corrupted coach outlet purses 449 by errors, or noise. With relatively consistent data information that varies more slowly than the noise you can look at more than one measurement at a time and more accurately separate the data from the noise. To minimize the effects of noise, the measured data can be filtered, or smoothed. In an ideal world you could remove all the noise and be left with an exact duplicate of the original information. (But in an ideal world, the information would not have been corrupted in the first place.) The best you can hope to accomplish in the real world is to remove as much noise as possible while retaining as much of the original information as possible. The a b filter is a classic option for smoothing a corrupted signal and tracking discrete data. Child of the Kalman Filter The a b filter is a derivative of the Kalman filter. The Kalman filter, a so called optimal filter, performs better than any other linear filter, under specific conditions (Gelb 1974, Meditch 1969, Mendel 1974). You pay for such excellent performance with extreme complexity and computationally intensive software. The a b filter resembles the Kalman coach outlet handbags younkers filter in its nature, structure, and performance, but without as much complexity. The Kalman filter automatically varies its coefficients to optimize its performance as a function of the statistics of the incoming corrupted data. The a b filter, on the other hand, uses either fixed coefficients or coefficients that vary as the programmer requires them to vary. Thus, the a b filter avoids entirely, or almost entirely, the computational overhead necessary for the Kalman filter to perform properly. The a b filter is called suboptimal. But don't interpret that to mean it performs poorly. On the contrary, the a b filter can be a highly effective smoother. By varying the coefficients of this filter, you can control the length of coach outlet sale xmas the data history used to estimate the data's true value from the most recent measurement. The Classic a b Filter The a b filter has about as many forms as there are sources that describe it (Benedict and Bordner 1962, Lewis 1984, Morris 1986). The following equation set is based on one presented by Morris. The equations form a recursive relationship and are used formally as follows: Given a new position measurement, an estimate is made of the true position and velocity for the current time increment using Equation 1 and Equation 2.
The estimates from Equation 1 and Equation 2 are used to predict the position and velocity for the next time increment using Equation 3 and Equation 4. The a and b terms give the filter its name. These fixed coefficients replace the optimized coefficients of the Kalman filter.
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