S. Haykin, Y. Xue, and P. Setoodeh, “Cognitive radar: Step

Deep Learning for Classification of Mini-UAVs Using Micro-Doppler Spectrograms in Cognitive Radar

Albert Huizing, Matijs Heiligers, Bastiaan Dekker, Jacco de Wit, Radar Technology Department, TNO, The Hague, The Netherlands Lorenzo Cifola, Ronny Harmanny, Advanced Development Thales Nederland B.V., Delft, The Netherlands

INTRODUCTION

Military radar can not only provide kinematic information (position, speed, and course) of land, sea, and air targets during day and night in all weather conditions, but also provides information about the type of target using microDoppler properties. The micro-Doppler properties of a target are determined by the moving parts on the target body. The number, location, and type of motion of these parts are specific for a given target type and consequently the related micro-Doppler signature can be exploited for classification. Analysis of the micro-Doppler signature may provide detailed properties of rotating parts, such as the rotation rate, number of blades, and blade length. However, for operators and/or image analysts, the interpretation and understanding of radar micro-Doppler spectrograms is much more difficult and time-consuming than the analysis of optical images because of the different nature of the radar imaging principle and target scattering mechanisms. Consequently, there is a need for automatic target recognition (ATR) in radar using micro-Doppler spectrograms.

An ability to automatically recognize complex patterns in radar signals is one of the key elements of the perception–action cycle in cognitive radar as identified by Haykin et al. [1]. A cognitive radar uses the feedback from the receiver to adjust the transmitted waveforms to

Authors’ current addresses: Albert Huizing, Matijs Heiligers, Bastiaan Dekker, Jacco de Wit, Radar Technology Department, TNO, 2594 AK, The Hague, The Netherlands, (E-mail: albert.huizing@tno.nl). Lorenzo Cifola, Ronny Harmanny, Advanced Development, Thales Nederland B.V. 2628 XH, Delft, The Netherlands. Manuscript received March 27, 2019, revised July 12, 2019, and ready for publication August 6, 2019. Review handled by P. Willett. 0885-8985/19/$26.00
2019 IEEE operate effectively and robustly in a dynamic environment. In addition to the feedback from the receiver, a cognitive radar can also use prior knowledge about the environment and targets of interest to optimize its performance [2]. This prior knowledge can be provided by human experts in the form of heuristics, terrain databases, or computer models of the interaction between the radar and the environment, including target signatures, such as micro-Doppler spectrograms. Alternatively, prior knowledge can also be derived from previous radar measurements in the same or similar environments [3]. Examples of this are a land clutter map and micro-Doppler spectrograms of targets of interest.

Figure 1 shows a high-level block diagram that incorporates two of the key elements of a cognitive radar: a perception–action cycle and priori knowledge stored in longterm memory, i.e., a knowledge base (KB). This diagram has been adapted from the cognitive radar architecture proposed by Guerci [2]. A knowledge-aided processor (KAP) uses prior knowledge in the KB to control the radar scheduler, the waveforms transmitted by the adaptive transmitter, the radio frequency filter parameters and the sampling rate in the adaptive receiver, and the parameters and configuration of the radar signal processing. The signal processing typically includes functions, such as pulse compression, Doppler filtering, detection, tracking, and classification. The KAP may decide to update the KB based on information that is extracted from the radar signal. For example, if the number of false alarms in an area is too large due to a change in the clutter distribution, the KAP may decide to update the clutter map.

In conventional radars, the recognition of targets, such as mini-UAVs typically relies on the extraction of predefined target features from the received radar signal. These target features are defined and implemented by a human expert in the radar signal processing, often after a long and tedious analysis of large amounts of radar data. Instead of this hand-crafted feature engineering approach, a cognitive radar can use deep learning techniques to