Research Article
Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix
Table 3
This table shows the time it took to complete the calculation of a dataset.
| | “Name” | “OWS” | “GWS” | Original SMT number of rotations | SMT after PCA number of rotations | Rotations number ratio |
| | “OP1_T1_S10” | 9 | 1 | 3845 | 1851 | 2.1 | | “OP1_T1_S33” | 9 | 1 | 3794 | 1831 | 2.1 | | “OP1_T1_S100” | 9 | 1 | 3820 | 1806 | 2.1 |
| | “OP1_T1_S10” | 7 | 1 | 2781 | 1508 | 1.8 | | “OP1_T1_S33” | 7 | 1 | 2769 | 1498 | 1.8 | | “OP1_T1_S100” | 7 | 1 | 2770 | 1457 | 1.9 |
| | “OP1_T1_S10” | 5 | 1 | 1965 | 1177 | 1.7 | | “OP1_T1_S33” | 5 | 1 | 1969 | 1203 | 1.6 | | “OP1_T1_S100” | 5 | 1 | 1965 | 1122 | 1.8 |
| | “OP1_T1_S10” | 3 | 1 | 1195 | 442 | 2.7 | | “OP1_T1_S33” | 3 | 1 | 1187 | 407 | 2.9 | | “OP1_T1_S100” | 3 | 1 | 1201 | 421 | 2.9 |
| | “OP2_T1_S10” | 9 | 1 | 2135 | 839 | 2.5 | | “OP2_T1_S33” | 9 | 1 | 2105 | 840 | 2.5 | | “OP2_T1_S100” | 9 | 1 | 2095 | 827 | 2.5 |
| | “OP2_T1_S10” | 7 | 1 | 1480 | 647 | 2.3 | | “OP2_T1_S33” | 7 | 1 | 1481 | 641 | 2.3 | | “OP2_T1_S100” | 7 | 1 | 1464 | 635 | 2.3 |
| | “OP2_T1_S10” | 5 | 1 | 961 | 440 | 2.2 | | “OP2_T1_S33” | 5 | 1 | 962 | 435 | 2.2 | | “OP2_T1_S100” | 5 | 1 | 960 | 431 | 2.2 |
| | “OP2_T1_S10” | 3 | 1 | 556 | 203 | 2.7 | | “OP2_T1_S33” | 3 | 1 | 555 | 205 | 2.7 | | “OP2_T1_S100” | 3 | 1 | 552 | 206 | 2.7 |
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The smaller the number of rotations, the less time needed for the calculation.
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