Principles of Modern Radar - Volume 2 1891121537

14.2 Unified Framework for ATR 633PDPEMSROCSARSVDSVMUHFprobability of detectionpredict, extract, match, and searchreceiver operating chacteristicsynthetic aperture radarsingular value decompositionsupport vector machineultra high frequencies14.2 UNIFIED FRAMEWORK FOR ATREven though the applications of ATR are diverse, the basic framework for ATR is remarkablysimilar and can be decomposed into four main steps, referred to here as the unifiedframework for ATR. The following sections describe these steps, as well as commonchallenges.14.2.1 Step 1: Identify the Target SetThe first step in development of an ATR algorithm is to identify the target set, whichincludes the targets of interest, as well as targets that are not of interest but are likely tobe observed. Implicit in this decision is the desired level of precision. For example, thegoal of some ATR systems is to distinguish between broad classes of targets, while thegoal of others is to distinguish specific models within a class. In the former case, the targetset may include broad classes such as aircraft, satellites, missiles, and unknown targets,whereas in the latter case it might include more precise classes of aircraft models such asF-15, F-16, T-38, and unknown targets. Subsequent decisions are heavily influenced bythis first step.14.2.2 Step 2: Select the Feature SetHaving identified the target set, the next step is to select a feature set that maximizes “thesimilarity of objects in the same class while maximizing the dissimilarity of objects indifferent classes” [118, pp. 367]. Sensitivity is a key component in this definition. Somefeatures (e.g., radar cross section, when the wavelength is much smaller than the target)are extremely sensitive to small changes in target parameters like aspect or pose; hence,they do not maximize the similarity of objects in the same class, or even of the same objectover time. The goal is to find a set of features sufficiently sensitive to reliably separatetargets into classes but not so sensitive that targets in the same class are appropriated intodisparate classes.14.2.3 Step 3: Observe the Feature SetOnce the feature set has been selected, the next challenge is to observe it as accuratelyas possible. Depending on the application and the choice of features, this may imply thatspecialized signal processing or conditioning (e.g., background removal) is necessary toextract observations of the feature set from the collected data. Accurately observing thefeature set may also require the sensor to operate in a non-standard mode (i.e., to usedifferent waveforms than are normally applied), which could drive fielding decisions.