10.6 Application of the SSKF to the DIFAR Ship Tracking Case Study
Since the dynamic equation is linear for the DIFAR tracking problem, the SSKF process used for this problem is identical to that shown in Table 9.2, except for the upper limit of the summation.
Table 10.1 Multidimensional Spherical Simplex Kalman Filter Process.
| Step 1. | Filter initialization: | Set  |
Initialize  and  |
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| Step 2. | State vectorprediction: |  |
 |
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 |
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 |
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| Step 3. | Observation- related prediction: |  |
 |
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 |
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