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This implementation for one dimension has many features common to implementations for three dimensions: Accelerometers cannot measure gravitational acceleration An accelerometer effectively measures the force acting on its proof mass to make it follow its mounting base, which includes only nongravitational accelerations applied through physical forces acting on the INS through its host vehicle Satellites, which are effectively in free fall, experience no sensible accelerations Accelerometers have scale factors, which are the ratios of input acceleration units to output signal magnitude units (eg, meters per second squared per volt) The signal must be rescaled in the navigation computer by multiplying by this scale factor Accelerometers have output errors, including 1 unknown constant offsets, also called biases; 2 unknown constant scale factor errors; 3 unknown sensor input axis misalignments; 4 unknown nonconstant variations in bias and scale factor; and 5.

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Proof We claim that L = PL Indeed, it is clear that PL L Conversely, suppose that z L but z PL Since {z} H and there is no K PL such that z K, it follows that {z} PL , which contradicts the assumption that z PL This means that L = PL Let K0 , K1 PL be two distinct sets These sets are disjoint since otherwise we would have either K0 K1 , or K1 K0 , and this would contradict the de nition of PL Theorem 6 Let H be a hierarchy on a set S The graph of the relation on H is a tree whose root is S; its leaves are the singletons {x} for every x S Proof Since is an antisymmetric relation on H it is clear that the graph (H, ) is acyclic.

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For example, category C 1 contains the cases with feature F 1 to feature Fm 1 and an exemplar of C 1 (eg, e11 is represented with two features, F 1 and F 2) Given a new case nc, the rst layer of the network is used as a category index to rank [using equation (219)] the location of the probable categories (in descending order) to which the new case may belong (see Fig 217) The second layer is then used as an exemplar index to determine from the aforesaid categories in descending order the location of a group of cases (represented by an exemplar ei) that are most similar to the new case To measure such similarity between ei and nc, equation (219) is used and then it is checked if the corresponding P(ei|nc) exceeds a prede ned threshold.

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unknown zero-mean additive noise on the sensor outputs, including quantization noise and electronic noise The noise itself is not predictable, but its statistical properties are used in Kalman ltering to estimate drifting scale factor and biases Gravitational accelerations must be modeled and calculated in the navigational computer, then added to the sensed acceleration (after error and scale  compensation) to obtain the net acceleration x of the INS The navigation computer must integrate acceleration to obtain velocity This is _ a de nite integral and it requires an initial value, x t0 That is, the INS implementation in the navigation computer must start with a known initial velocity The navigation computer must also integrate velocity (_ ) to obtain position (x) x This is also a de nite integral and it also requires an initial value, x t0 .

The algorithm stops as soon as an exemplar satisfying the threshold is found The category index thus narrows down the searching space for the exemplar index, thereby improving the ef ciency of the indexing structure (Note that it may sometimes happen that the best-match exemplar is found in a less probable.

Moreover, for each set K H there is a unique path that joins K to S, so the graph is indeed a rooted tree De nition 7 Let H be a hierarchy on a set S A grading function for H is a function h : H R that satis es the following conditions: (i) h({x}) = 0 for every x S, and (ii) if H, K H and H K, then h(H) < h(K) If h is a grading function for a hierarchy H, the pair (H, h) is a graded hierarchy.

The INS implementation in the navigation computer must start with a known initial location, too 6412 Inertial Navigation in Three Dimensions Inertial navigation in three dimensions requires more sensors and more signal processing than in one dimension, and it also introduces more possibilities for implementation The earliest successful INSs used gimbals to isolate the sensors from rotations of the host vehicle Gimbaled INS A stable platform, inertial platform, or ``stable table'' is a mechanically rigid unit isolated from the rotations of the host vehicle by a set of three or (preferably) four gimbals, as illustrated in Figs 612a,b Each gimbal is effectively a ring with orthogonal inside and outside pivot axes These are nested.

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