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The model for a nite-dimensional linear continuous-time system with stochastic inputs can be represented as x(t) = F(t)x(t) + G(t)w(t) y(t) = H(t)x(t) + v(t) (457)

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where w(t) and v(t) are random variables The random variable w is called the process noise The random variable v is called the measurement noise At each time, x(t) and y(t) are also random variables The designation random variable implies that although the value of the random variable at any time is not completely predictable, the statistics of the random variable may be known Assuming that the model is constructed so that v(t) and w(t) are white, the mean and covariance of the random variables w(t) and v(t) will be denoted w (t) = E w(t) = 0 and cov (w(t), w( )) = Q(t) (t ), v (t) = E v(t) = 0 and cov (v(t), v( )) = R(t) (t ) (458) (459) (460) (461)

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In the analysis that follows, it will often be accurate (and convenient) to assume that the process and measurement noise are independent of the current and previous state cov (w(t), x( )) = 0 for t cov (v(t), x( )) = 0 for t and independent of each other cov (w(t), v( )) = 0 for all t, 0 (464) (462) (463)

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In most applications, the random processes w(t) and v(t) will be assumed to be Gaussian The linear discrete-time model is xk+1 = k xk + wk yk = Hk xk + vk (465)

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Alpha hydroxy acids are the most frequently used super cial peel agents, and glycolic and lactic acid are the most commonly commercially synthesized144 The common structure is a linear carboxylic acid with an attached hydroxyl group at the second (alpha) carbon and a variable length carbon chain145 This group is hydrophilic and derived primarily from fruit and food acids Glycolic acid is derived from sugarcane, lactic acid from sour milk, malic acid from apples, and citric acid from citrus fruits At the epidermal level, alpha hydroxy acids act by disrupting corneocyte cohesion at the stratum corneum stratum granulosum junction146 This reduced adhesion

The mean and covariance of the random variables wk and vk will be denoted wk cov (wk , wl ) vk cov (vk , vl ) = E wk = 0 and = Qdk (k l), = E vk = 0 and = Rk (k l) (466) (467) (468) (469)

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Assumptions corresponding to eqns (462 464) also apply to the discretetime model In the analysis to follow, the time argument of the signals will typically be dropped, to simplify the notation

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Navigation system error analysis will often result in equations of the form xn y = Fn (t)xn + Gn (t) = Hn (t)xn + (470) (471)

where and represent instrumentation error signals and xn represents the error in the nominal navigation state This and subsequent sections of this chapter will present a set of techniques which will allow the error signals and to be modeled as the outputs of linear dynamic systems x = F x +G = H x + (472) (473)

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