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This paper presents measurements of Radar Cross Section (RCS) of five Unmanned Aerial Vehicles (UAVs), comprising both consumer grade and professional small drones, collected in a semi-controlled environment as a function of azimuth aspect angle, polarization and frequency in the range 8.2-18 GHz. A first-order statistical analysis of the measured RCSs is firstly reported prior to assessing the radar detection performance on both measured and bespoke simulated data (leveraging the results of the developed statistical analysis), including, as benchmark terms, the curves for non-fluctuating and Rayleigh fluctuating targets.

Echolocating bats have evolved an impressive ability to detect and discriminate targets in highly challenging environments. It is believed that over 50 million years of evolution have contributed to optimize their echolocation system so that highlevel performance could be achieved within their operating environment. The way bats interrogate the surroundings present differences, as well as similarities, with respect to typical signal processing techniques used in synthetic sensors. In identifying and investigating these differences, useful lessons can be made available to engineers that can potentially be used to improve radar and sonar systems. In this chapter, we review some of the strategies that bats are believed to employ to detect and classify moving and static targets and present a comparison with the radar and sonar counterparts. We introduce a baseband receiver based on an existing model of the bat auditory system and apply it to baseband synthetic ultrasound signals to investigate target detection and resolution performance.

In this chapter, we take inspiration from the bat and develop an algorithm that guides an airborne radar interceptor towards a target by jointly developing an optimal guidance and automatically adapting and optimising the transmitted waveform on a pulse-to-pulse basis. The algorithm uses a Kalman filter to predict the relative position and speed of the interceptor with respect to the target.

In this chapter, we have presented a baseband receiver that allows an analytical treatment of the output of a SCAT-like processing and that can also be applied to RF signals. The output of the Baseband SCAT (BSCT) has been compared analytically with that of a conventional matched filter for two closely spaced scatterers. Results have shown that a bat-inspired spectrogram transformation can provide better range resolution performance than that of a conventional matched filter. The results have been derived analytically and then verified with a set of laboratory experiments at ultrasound, using phantom echoes and real targets, to assess the robustness of the algorithm with respect to operational hardware limitations and propagation effects.

In this paper, we analyse a real set of data containing high range resolution profiles (HRRP) of unpollinated corollas of Cobaea scandens and of an inflorescence of Rhytidophyllum auriculatum, which are plants typically pollinated by bats. These were collected by transmitting a synthetic wideband linear chirp with an acoustic radar capable of a very high range resolution. Two C. scandens flowers taken at different stage of maturity were measured to investigate if and how the information contained in the flowers' echo acoustic signatures changes as the flower wilts and hence stops producing nectar and loses attractiveness. These were then modified, by manually removing the petals, in order to study feature-specific contributions to the scattering. The acoustic signatures of the C. scandens and the R. auriculatum do not change as a result of pollination by a bat and indeed the same individual flower can be pollinated multiple times as long as it contains nectar. The analysis presented in this chapter is subtly different from investigating pre and post bat fed plants but it demonstrates the acoustic changes as the flower degrades, and hence it is a valid experiment for this bat-pollinated plant species. The signatures are analysed and results are discussed with respect to the task of classification of manmade targets.

The authors present an algorithm that allows an interceptor aircraft equipped with an airborne radar to meet another air target (the intercepted) by developing a guidance law and automatically adapting and optimising the transmitted waveform on a pulse-to-pulse basis. The algorithm uses a Kalman filter to predict the relative position and speed of the interceptor with respect to the target. The transmitted waveform is automatically selected based on its ambiguity function and accuracy properties along the approaching path. For each pulse, the interceptor predicts its position and velocity with respect to the target, takes a measurement of range and radial velocity and, with the Kalman filter, refines the relative range and range rate estimates. These are fed into a linear quadratic Gaussian controller that ensures the interceptor reaches the target automatically and successfully with minimum error and with the minimum guidance energy consumption.

Multiple-Beam Radar Systems (MBRS) based on waveform diversity require a set of orthogonal waveforms in order to generate multiple channels in transmission and extract them efficiently at the receiver using digital signal processing. Chirp signals are extensively used in radar systems due to their pulse compression properties, Doppler tolerance and ease of generation. In this paper, we investigate the level of isolation between two linear frequency modulated (LFM) chirps as a function of the frequency slope and of the chirp starting frequency. Results are derived analytically and verified with a set of measurements at S-band.

With the proliferation of low-intensity conflict, landmines have proven to be one of the weapons of choice for both government and guerrilla forces around the world. Recent improvements to mine technology pose increasingly significant problems for demining operations, requiring the constant upgrading of countermine technologies. Ground Penetrating Radar (GPR) is one of the most exhaustively researched topics in the detection of buried mines as it can be used to detect non-metallic and plastic mines. However, identification and recognition are still unsolved problems, due to the scattering similarity between mines and clutter objects. This study provides an experimental evaluation of the improvements that a bistatic approach could yield and what can be gained from investigating the angular dependencies of the landmine radar signature.

In this chapter we introduce the theory behind sonar micro-Doppler signatures and highlight advantages and disadvantages with respect to their radar counterpart. We present the results of two experimental trials carried out at (1) the U.S. Army Research Laboratory and (2) University College London in which sonar micro-Doppler signatures of personnel targets were collected at 40 and 80 kHz, respectively. This chapter shows that ultrasound sensors could represent a cost-effective alternative to radar systems for applications that do not require long-range detection. They certainly demonstrate that target detection and possibly classification by ultrasound micro-Doppler signatures are achievable and they might potentially open up a new research avenue looking at the exploitation of these techniques in future systems.

In this chapter we aim to exploit experience of the natural world in which echolocating mammals are able to detect and classify objects with apparent ease. These observations suggest that waveform diversity and orientation strategies play an important role. It is this hypothesis that we test and show to be valid, as confirmed through real-world radar experiments. Specifically, there is additional information contained in different perspectives of a target that can help classify it, thus boosting performance. There is also a law of diminishing returns, in that the opportunity to extract additional new information reduces as the number of perspectives increases.

Echolocating mammals such as bats, whales and dolphins are able to detect, select and attack prey even in dense cluttered and often hostile environments. They have developed echolocation for over 50 million years and rely on exceptional performance for their survival. Although the frequencies and waveform parameters used by radar sensors and by echolocating mammals are not the same, there remain close parallels that suggest lessons can be learnt from nature. In this chapter, we investigate the behaviour and performance of echolocating bats in terms of detecting, locating, tracking and capturing prey. We show how echolocation calls are diversified in a dynamic and intelligent manner according to the task performed and relate the results to typical flight trajectories. We discuss how echoes may be transformed into a meaningful perception of the target and finally we explore how this information can be used to develop a new architecture for radar automatic target recognition (ATR).