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In the modern electronic warfare signal environment, multiple radar signals of high density are mixed and received, and separating them into signals for each emitter is an essential step for emitter identification. Each radar has its own pulse repetition interval (PRI), which is a key parameter for deinterleaving pulse trains. The PRI is modulated in various forms depending on the purpose of the radar operation, and analyzing the mean PRI and the modulation type of PRI is the core of electronic warfare signal processing. Many existing papers have tried separate independent approaches for deinterleaving and for PRI modulation recognition. However, many distortions are unintentionally generated in the process of extracting the pulse train using the PRI estimated through deinterleaving for the PRI modulation recognition. This degrades the the modulation recognition performance. In this paper, we propose a unified method for the deinterleaving and PRI modulation recognition of radar pulses using deep learning-based multitasking learning. The simulation results demonstrate the good performance of the proposed method for deinterleaving and modulation recognition, compared to the conventional method, and prove that the proposed method is robust in noisy radar signal environments.

In a recent electronic warfare (EW), electronic support (ES) systems suffer from the ambiguity problem that multiple radar types are reported instead of picking out the specific one. Hence, a radar scan pattern has been utilised as an important feature to improve the accuracy of threat identification. However, since false identifications cause immediate danger to friendly forces, the probability of false identification should be reduced as well as the increase in identification accuracy. To cope with this necessity, a new decision category entitled 'unidentified' is introduced based on the variance of the difference in peak-to-peak intervals. Firstly, the successive received signal strength is modelled for a given scan pattern, and then the effects of the position and movement of an ES receiver onto the identification accuracy are examined to analyse the tendency of false identifications. Simulations are included to confirm the effect of the proposed new feature parameter. By introducing the new parameter, on average 82% of the false identifications are classified into the new decision category instead of incorrectly being classified as a radar scan, whereas only 4% of the correct identifications as a raster scan are classified into the new category.

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The emitter geolocation method using the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) has more accurate performance comparing to the single TDOA or FDOA based method. The estimation performance varies with the sensor paring strategies, the deployment and velocities of the sensors. Therefore, to establish effective strategy on the electronic warfare system, it is required to analyze the relation between the estimation accuracy and the operational condition of sensors. However, in the conventional non-iterative method, the restriction of the deployment of sensors and the reference sensor exists. Therefore, we derive the emitter geolocation method based on a Gauss-Newton method which is available to apply to any various sensor pairs and the deployment and velocities of the sensors. In addition, simulation results are included to compare the performance of geolocation method according to the used measurements: the combined TDOA/FDOA, TDOA, and FDOA. Also, we present that the combined TDOA/FDOA method outperforms over single TDOA or FDOA on the estimation accuracy with the CEP plane.

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