Case Study 19.1: Extending the Capability of an Electronic Warfare System[1] Electronic countermeasure (ECM) systems are multiplexed computer-controlled microwave (radar) transceivers that intercept an enemy's radar signal. They send back in real time a masking signal only slightly stronger than the expected reflection that tells the enemy radar operator the wrong signature, the wrong distance, and even the wrong number of potential targets. You cannot hide a carrier fleet group, but if you make all 28 destroyers appear to have the same signature as the carrier, the enemy cannot aim a missile accurately at the real carrier. The software for such systems must be continually upgraded to be able to detect and counter new enemy radars, new radar frequencies, and new targeting and scanning techniques. This is a natural application for Taguchi Methods, because the software extensions are additions to a tested library of proven countermeasure software algorithms. Also, tactical ECM systems must work properly in the presence of uncontrollable noise factors. Robustness is critical in such systems, and trustworthy software is essential, because the alternative is assured destruction! For the system under analysis in this case study, software upgrades are made on a weekly basis, but checking them against all the recognized emitter types takes 16 hours. The goal of the Taguchi analysis was to enhance robustness while reducing weekly testing time. The noise factors in the study were variation in emitter characteristics over the range of recognized emitter types; variation in emitter position, including amplitude (nearness) and azimuth (direction of received signal arrival); variation in emitter mode; and actual background (electrical atmospheric) noise. An L18 orthogonal array was selected to model one two-level factor and up to seven three-level factors. The factors in the array are as follows: Factor | Level 1 | Level 2 | Level 3 |
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Overall mean | | | | Frequency diversity | Single | Multiple | | Frequency | Low | Medium | High | Emission type | Continuous wave (CW) | Stable | Agile | Scan type | Steady | Circular | Raster | Peak power | Nominal | Medium | Low | Arrival angle | Bore sight | Offset 1 | Offset 2 | Illumination | Short | Medium | 100% | Background | None | Low density | High density |
Next, 18 emitters or Taguchi experiments were selected to cover the parameters represented in the factors. The major characteristics were single radio frequency (RF), high RF, stable emission mode, circular scan, and short illumination time. For example, the first 2 of the 18 experiments went as follows: Number | Mean | Diversity | Frequency | Emission | Scan | Power | Illumination |
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1 | 0 | Single | High | CW | Steady | Nominal | 100% | 2 | 1 | Single | High | Stable | Circular | Medium | Short |
The developer's choice of experimental parameters was informed by extensive experience with the system and a history of weekly upgrades to the software. We will not try to explain the case study at this level of technical detail, because we are merely focusing on the methodology. You're encouraged to read the referenced article.[1] The signal-to-noise (SN) ratio for this experiment was calculated as follows: SN = 20 log {(total defects) / (n + 1)} Because the problem type is "maximum is best," the SN for a perfect system is 0 dB. Any performance error will result in a negative SN ratio. The "defects plus 1" expression was used because log (0) is undefined and log (1) = 0. To compute the SN ratio for all 18 experiments in the array, the value of n would be 18. However, to compute the SN for RF diversity alone, n would be 9, because "multiple" appears in nine of the experiments and "single" in the other nine. The author calculates the SN ratio for multiple diversity as 10.6 dB and for single source as 7.8 dB, based on the errors detected in a particular testing session. This case study confirmed that each week's upgraded software can be tested in only four hours for the 18 emitter types modeled. This was considered significant, because sensitivity to test results did not show any additional system weakness when the more-extensive monthly test was carried out later. The use of a Taguchi analysis to reduce testing time for the weekly software upgrades by a factor of 4 is quite remarkable. However, the most amazing result of this application of the method was that the careful choice of 18 emitter types by the designer, together with the operational noise factors, surpassed the performance results of testing for each radar type individually! |