# Radiometric Identification of Signals by Matched Whitening Transform
## 1 Introduction
In [15], DL is implemented for RF device fingerprinting in the cognitive Zigbee networks using the time-domain complex baseband error signal as training and test data.The results show good accuracy (\(\approx\)90%) but at high SNR (\(\geq\)20 dB). In [19], the input data are preprocessed as Hilbert spectrum gray-scale images and achieves acceptable accuracy under moderate SNR levels (Avg 70% accuracy rate for SNR of 15 dB). A comprehensive performance comparison is shown for various DL algorithms in [13], reporting an average accuracy of 98% measured for 12 transmitters. The fact that ML operates on much smaller data sets and requires much less training time compared to DL (hours of training [15]), provides more versatility to signal characteristics changes that occurs under different environmental circumstances (overheating, excess current, etc. ), which can strongly affect the classification selected feature. This property of ML (data-driven) allows for fast feature update and consequently results in higher accurate classification on the long term. In addition, the reduced complexity compared to DL allows to easier hardware implementation and fast on-the-fly classification. Specific Emitter Identification(SEI) is another paradigm for radiometric identification [20; 21; 22]. The SEI approach attempts to identify the unique transmitter of a signal using only external feature measurements [22]. SEI is implemented in two stages, (1) transient signal state and (2) steady state signal state. The transient approach applies to the particular signatures embedded in the signal as the transmitter powers up or down [23; 24]. Transient approaches are more difficult to implement due to the unavailability or transient nature of the data that is often not accessible or saved. Steady state approach refers to the period where transients have stabilized. The available features include modulation and preamble [25; 26], among others. In modulation-based techniques, the received and the target constellations are compared where the difference creates an RF fingerprint [27]. A fast decision identification algorithm appears in [28]. Identification is based on the similarity of a signal vector and its comparison to patterns available in a database. The approach is classified as an example of SEI applied to radar identification. The algorithm was applied hundreds of radar signals records which came from several different types of radars. In some cases, copies of the same type of radar of were investigated. Weighing all features equally, 85% correct recognition rate is reported for radar types. A mixed method of radar identification based on electromagnetic emission and intrapulse analysis appears in [29]. The premise is that electronic devices impart electrical features on the transmitted pulse. The signal model is \(N\) non-overlapping pushes form \(K\) transmitters. Linear Discriminant Analysis is used. Four distance metrics are used to classify the unknown pulse. It is reported that three copies of the same type of radar are successfully recognized. Radiometric identification of communication protocols are also of interest. Identification of sources that use the LTE protocol is reported in [30; 31]. The identification is based on unique modulation characteristics exhibited by the transmitters, resulting from minute imperfections introduced during radio hardware manufacturing. Device imperfections have been used as a signature for radiometric identification including clock jitter [32], the digital-to-analog converters (DAC) errors [33], local frequency synthesizer [34], the power amplifier non-linearity [35; 36; 37]. Power amplifier imperfections are also used for source identification [38]. Real radar signals are used for emitter identification [39]. An entirely different application for radiometric identification is radar. Even though the transmitters may belong to the same type of radar, they may exhibit subtle differences in their transmitted pulses. In [33], 18 features are used to identify three class of radars.