The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to sense whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 80.95, 72.50 and 86.05%, for data collected at S-band, C-band and W-band, respectively, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper.
In this paper we present a dual active and passive radar experimental setup that uses the UCL ARESTOR platform. This is a multi-role RF sensor based on a Xilinx Radio Frequency System on a Chip (RFSoC) device. The system is capable of operating as an active radar, passive radar and wideband electronic surveillance receiver. Experimental results are shown that leverage 2.4 GHz passive radar experiments along with a 5.8 GHz active radar mode that are operating simultaneously observing a target of interest. Details of a bespoke designed RF front-end to access higher frequency bands are included within the paper as well as information on processing pipelines developed within the Field Programmable Gate Array (FPGA). Comparison of the target signature and how both modes could be best utilised are analysed and discussed. The target of interest within this paper is a person walking while being sensing by both modes simultaneously.
Cognitive radar is a rapidly developing area of research with many opportunities for innovation. A significant obstacle to development in this discipline is the absence of a common understanding of what constitutes a cognitive radar. The proposition in this article is that radar systems should not classed as cognitive, or not cognitive, but should be graded by the degree of cognition exhibited. We introduce a new taxonomy framework for cognitive radar against which research, experimental and production systems can be benchmarked, enabling clear communication regarding the level of cognition being discussed.
In this article, we discuss a novel signal processing technique for adaptive radar that permits joint target-matched illumination and interference avoidance in dynamic spectral environments. This approach allows for spectral coexistence between a radar system and a primary user of the radio frequency space. Spectral coexistence is exploited to allow use of higher bandwidths than would otherwise be available to conventional radar systems. The technique proposed exploits the relative simplicity of the error reduction algorithm, and also provides a novel use of the masking procedure to allow for target-matched illumination. Practical constraints such as constant modulus are considered in the waveform design procedure, while providing an implied signal-to-interference-plus-noise ratio improvement via the error reduction algorithm. Results for full simulation and hardware-in-the-loop experiments are presented and analyzed. We are able to show a signal-to-interference-plus-noise ratio gain of 40 dB is achieved for the target-matched waveform as compared with a linear frequency modulated waveform. However, the signal-to-interference-plus-noise ratio gain comes at a cost of degraded autocorrelation characteristics of the targetmatched illumination waveform, despite only modest levels of primary user spectrum occupancy. Spectral notch depths achieved by the modified error reduction algorithm are approximately 25 dB.
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