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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.

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Tharp says that being able to see what the market is doing right now, and to feel the pulse of the market, will make you a better trader To feel the pulse of the market, he relies on macroeconomic indicators such as Gross Domestic Product (GDP), job growth, and debt levels, besides just studying charts For the next several years, he foresees several deep economic crises, but it s still possible to find plenty of ways to make money, although not with buy-and-hold strategies It s important to observe what s going on at the time It s important to have a good idea about the big picture so that you don t get confused when market conditions change suddenly Prediction, however, has very little to do with making money

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Figure 51: Continuous-time system with input u = 0 The topic of covariance analysis and its use in error budgeting is critical to making informed system level decisions at the design stage

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This section summarizes the linear stochastic state estimation results from the previous chapters For a linear continuous-time system such as that shown in Figure 51, to enable state estimation in discrete-time, we nd the discrete-time equivalent state-space model of the actual process: xk yk = = xk 1 + Guk 1 + k 1 Hxk + k (51) (52)

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where xk Rn , yk Rp , k Rp , and k Rn are the stochastic signals representing the state, output, measurement noise and driving noise, respectively; u Rm is a known input signal Assuming that the state is observable from the output signal, our objective is to provide an optimal estimate of the state vector The resulting algorithm is referred to as the Kalman lter The basic state estimation block diagram is shown in Figure 52 For simplicity of notation, the system is assumed to be time-invariant and the subscript k s on the system parameter matrices have been dropped (eg, k = ) The assumption of a time-invariant system is not a restriction on the approach The derivation does go through for and the Kalman lter is often applied to time-varying systems

In this model, k and k are signals determined by the environment The designer does not know the actual values of these signals, but formulates the problem so that the following assumptions hold: E k = 0 E k = 0 var ( k , l ) = Rk kl var ( k , l ) = Qdk kl

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