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1 Repeat analysis similar to that of Section 493 to characterize the rate of growth of the error in the estimate of 2 Assume that a measurement of is available at a 1 Hz rate and that the measurement is corrupted by Gaussian white noise with standard deviation of 10 deg Design a stable error state estimator and perform analysis similar to that of Section 494 to characterize the performance of the aided system

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You will sometimes hear scientists talk rather loosely about orders of magnitude They mean to say that the absolute value of one quantity is some power of 10 times bigger or smaller than the absolute value of the other quantity Here are a few examples: 45,300 is one order of magnitude larger than 4,530 056 is two orders of magnitude smaller, in absolute terms, than 56 0565 is three orders of magnitude smaller, in absolute terms, than 565

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If one or both quantities is negative, you should be sure to include the phrase in absolute terms so there s no confusion about the meanings of smaller or larger When you want to portray a decimal number greater than or equal to 0 but smaller than 1, it is customary to write a single numeral 0 to the left of the decimal point If the number is greater than 1 but smaller than 0, you should write the minus sign first, then a single 0, and then the decimal point

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Here s a challenge!

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The main topic of this chapter is the derivation and study of the topic of optimal state estimation Sections 36 and 48 have already introduced the concept of state estimation In those sections the state estimation gain was represented by L The gain L could, for example, be selected by pole placement methods Given a stochastic model and a stabilizing gain L, the system performance could be evaluated by the methods discussed in Section 49 This chapter presents the state estimation problem from a di erent perspective: given a stochastic model, can the state estimation gain be selected as a time-varying sequence that optimizes some measure of the performance of the system The state estimation algorithm that uses this optimal timevarying gain sequence will be referred to as the Kalman lter The state estimation problem is reviewed in Section 51 Using the mean-squarederror of the state covariance sequence as the optimization criteria, we derive the Kalman lter by two distinct methods Section 52 sets up an optimization problem and selects the optimal state estimation gain Kk at each time step For an alternative perspective, Section 53 starts with the problem of weighted least squares, progresses through recursive weighted least squares, and nally uses time propagation of the state and covariance matrix to derive the Kalman lter gain sequence Kk Section 54 summarizes the derivations of the Kalman lter algorithm and presents di erent approaches to computing the variance of the state estimation error after a measurement correction Section 55 discusses useful properties of the Kalman lter Section 56 discusses various issues that should be understood for e cient and successful Kalman lter implementations Section 59 discusses two numeric issues important for Kalman lter implementations Section 510 discusses various motivations for and approaches to suboptimal ltering 6 will revisit the topic of state estimation performance analysis 169.

Imagine two sets of single-digit numerals, called set A and set B Suppose A has m elements and B has n elements, named as follows: A = {a1, a2, a3, , am} and B = {b1, b2, b3, , bn} Now imagine these single-digit numerals arranged around a decimal point like this: am a3 a2 a1 b1 b2 b3 bn The string of numerals to the left of the point represents the sum SA = (am 10m 1) + + (a3 102) + (a2 101) + (a1 100)

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