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computed from these variables will also be stochastic variables. Therefore, accurate stochastic system modeling and analysis is critical to navigation system design. Accurate characterization of model and variable properties and analysis of their a ect on the system de nes the analysis of the overall system performance. When purchasing or designing sensors, the designer naturally searches for the best sensors that t within the project budget. However, any use of an adjective such as best requires further consideration of its de nition in this context. For example, for the acceleration sensor, the best sensor would have no noise (i.e., 1 (t) = 0) and no bias (i.e., b(t) = 0); however, this is typically not possible under application conditions. Instead, thermal uctuations and component variation will cause both quantities to be nonzero. To surpass the competition, each sensor manufacturer has the incentive to remove, to the extent possible, all the predictable components of sensor error (e.g., nonlinearities, temperature dependent characteristics). Therefore, the remaining sensor error is non-deterministic and will be accommodated within the navigation system design by stochastic process modeling and state estimation. Knowing this, the sensor manufacturer designs the sensor so that the characteristics that de ne the random nature of the sensor are time-invariant. Also, the sensor manufacturer will typically supply the parameters that quantify the stochastic nature of the sensor. A brief discussion of sensor speci cations is presented in Section 4.9.2. 4 discusses the various topics from the theory of random variables and stochastic processes that must be understood for the design and analysis of navigation systems. This discussion includes de nitions of basic stochastic process concepts, discussion of dynamic systems with stochastic inputs, de nition of various stochastic process models that regularly appear in navigation systems, and discussion of the time variation of the mean and variance of the state of a linear stochastic state space system. 4 concludes with a discussion of state estimation as it relates to linear stochastic state space systems and the characterization of their state estimation accuracy through covariance analysis. The example of Section 1.1.1 is considered again in Section 4.9.3-4.9.4 using the quantitative methods of 4.
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