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6. Whether the estimate and covariance propagation is done using a state transition matrix or (for nonlinear ltering) by numerical integration. 7. Whether the predicted measurement is computed using a measurement sensitivity matrix or a nonlinear measurement function. 8. The sparse structure (if any) of the matrix parameters in the Kalman lter equations. For example: (a) For Kalman lters integrating independent systems with no dynamic interactions (e.g., GPS and INS), dynamic coef cient matrices and state transition matrices will have blocks of zeros representing the dynamic uncoupling. (b) Because most sensors measure only a limited number of variables, the number of nonzero elements in the measurement senstivity matrix H usually tends to grow as the number of sensors (`) and not as the total number of possible elements (n`). (c) It is uncommon that the dynamic disturbance noise covariance matrix Q and=or sensor noise covariance matrix R are dense (i.e., no zeros), and it is not uncommon that they are diagonal matrices. 9. Details of the programming implementation, such as (a) whether the programming takes advantage of any offered matrix sparseness by skipping multiplications by zero, (b) whether the programming takes advantage of symmetry in the Riccati equation solution, and (c) multiplication order in evaluating matrix expressions, which can make a signi cant difference in the computation required. In Carlson Schmidt square-root ltering (Section 8.1.6.7), for example, it is not necessary to place the blocks of the matrix Fk CP;k 1 jGk CQ;k into a separate common array for triangularization. The additional array space can be saved by doing an implicit triangularization of the array (by modi ed indexing) without physically relocating the blocks.

a We will need 456 6144 rules for the conventional IRC b We will need 456 120 rules for the Combs URC Necessity is the degree to which the evidence supports a datum If there is no supporting evidence, then the datum has no support, and its necessity must be 0 If there is no refuting evidence, then there is nothing to refute the datum Since possibility is the extent to which the data fail to support a datum, the evidence fails completely to rfute the datum, and its possibility is 1 If we increase Pos(A) to 06, leaving Nec(A) unchanged, it will be impossible to correct an error or resolve a contradiction In a dual-truth-value system, this would not be necessary But in a necessitybased system, we have no way to store possibilities from one step to the next.

Local maximum. (From [12].)

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A calculated Pos(A) and Nec(NOT A) is lost when we go to the next rule ring step, and we could end up with in ated necessities in later steps unrestricted by the possibility calculated in an earlier step This is a disadvantage of necessity-based systems; fortunately, by reserving consideration of refuting evidence to the last steps, this disadvantage can be overcome In the simple rule IF A THEN B; A and B are indeed nested since if A is true, B must also be true and A ) B However, in more complex rules such as IF A AND B AND C THEN NOT D none of the four individual propositions A, B, C and D need be nested; the complex proposition A AND B AND C is nested with NOT D, but not with D.

The failure of max min logic to obey the laws of excluded middle (and non-contradiction) means that the use of min max logic to combine A and NOT A is invalid, since min max logic does not obey the law of the excluded middle (A AND NOT A 0) An IDE permits the programmer to access all necessary functions text editor to write the program, language help les, ability to run the program, and view any resulting error messages within a single program, the integrated program development environment.