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where U is an orthonormal matrix (ie, U U = I) The UDU decomposition is possible for any symmetric matrix, see Section B11 If we choose z to be de ned as z = U y Then the measurement z satis es z = U y = U Hx + U = Hx + The equivalent additive noise has variance E = E U U = U UDU U = D

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Therefore, the scalar processing algorithm can be used with the measurement z having measurement noise with variance described by the diagonal matrix D, and related to the state by the observation matrix H

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In many applications, a measurement expected at the k-th sample time may occasionally be missing Such instances have no e ect on the Kalman lter algorithm, as long as the missing measurement is ignored correctly When a measurement is missing, the corresponding measurement update (both state and covariance) must not occur Alternative, the missing measurement can be processed using an arbitrary value for yk as long as the Kalman gain vector is set to zero, which corresponds to Rk being in nite In addition, the state estimate and error covariance must be propagated through time to the time of the next expected measurement Even when a measurement is available, it is sometimes desirable to reject certain measurements as faulty or erroneous The Kalman lter provides a mechanism for detecting candidate measurements for exclusion Let the state error covariance be P before the incorporation of the scalar measurement y = Hx + Then the measurement residual r = y y is a scalar Gaussian random variable with zero mean and variance E rr = HP H + R where R = var( ) The designer can select a threshold such that P rob r2 > HP H + R =

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Bullish % 3850 3980 3930 4380 4720 5600 5400 5330

where (0, 1) is usually quite small This probability can be evaluated using the probability density for chi-squared random variables which is de ned on p 164 When the condition r2 > (HP H + R) (581)

Bearish % 3190 2840 2920 2470 2470 1870 1800 1740

is satis ed, the measurement is declared invalid and the corresponding measurement update (state and covariance) is skipped Repeated violations of the test condition can be used to declare the sensor invalid Use of such conditions to remove measurements deemed to be invalid can be problematic If the state error x = x x is for some reason larger than that predicted by P , then the above logic may remove all measurements In realistic applications, the conditions for discarding measurements is usually considerably more descriptive that indicated by eqn (581) In addition, when the condition r2 > (HP H + R) occurs, it is often useful to log an assortment of descriptive data (eg, yk , yk , x, Rk , Hk , and Hk P Hk ) The data logged at this condition may allow detection of k modeling errors or debugging of software errors

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