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This study investigates crosstalk and leakage in the Software Defined Radio implementation of Frequency Modulated Continuous Wave (FMCW) radar systems. Empirical measurements of leakage power were conducted during the implementation of FMCW radar using National Instruments Universal Software Radio Peripheral (USRP) boards. Mono‐static and bi‐static/distributed Transmitter‐Receiver configurations are compared for low power micro‐Doppler applications. The impact of crosstalk on the dynamic range of the target is also investigated. The impact of electric noise generated by a windy source on the receiving USRP is also investigated. Furthermore, a simple splitter based approach is proposed to overcome the challenge of synchronisation or recovery of transmitted waveform in distributed configurations.

Policy rollout is a method for the online computation of future costs in approximate dynamic programming and has been utilized for various problems, including sensor management. In previous work, it has predominately been applied to the selection of actions from discrete sets. In this article, we present methods for action selection from continuous sets and analyze their tradeoffs. The methods are evaluated on the problem of sensor path planning, with the intent of minimizing the time to localize an emitter using bearing measurements.

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 the 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 target-matched 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.

An article in the September 2019 issue of the AES Magazine described the background of the IEEE Historic Milestone to mark the invention and first demonstration of the radar by the German inventor Christian H€ulsmeyer [1]. On 17 May 1904, H€ulsmeyer arranged a demonstration of his invention in K€oln (Cologne), Germany, which was reported in the K€olner Tageblatt and K€olnischer Zeitung newspapers the following day. This device was conceived as a means of detecting ship targets, even under conditions of poor visibility, and hence preventing collisions. By the standards of modern radar, it was very crude, using a spark-gap transmitter and a coherer-type receiver. He patented his invention both in Germany and in the U.K. On 19 October 2019, the invention was formally marked as an IEEE Historic Milestone. This article describes the event and the various contributions that came together to make a very special day for IEEE AES and for the worldwide radar community.

This study addresses a radar architecture to design a waveform that dynamically fits into the gaps in the radio-frequency spectrum and that matches the target scattering properties. The optimisation metric considered is the mutual information between the received signal and the target impulse response, the maximisation of which makes the radar waveform illuminate a wider number of target spectral components. The proposed scheme is conceived to be part of a cognitive – or fully adaptive – system, which adapts its waveform in a perception-action cycle, by using a trade-off between the observation time and the desired performance. In this work, particular attention is paid to analyse how the chosen metric is influenced by external parameters such as the target, interference and clutter power and radar parameters such as the observation time and the transmit power.

Professor Viktor Chernyak was a visionary whose book Fundamentals of Multisite Radar Systems, published in 1993, set out the principles of multistatic and multiradar systems. This paper summarises Chernyak’s contribution, provides some historical background to the development of networked radar, and discusses the technical issues that will be necessary for practical networked radars to be feasible in the future.

Transmitters form an essential part of all radar systems. Although modern radar signal design techniques promise precise, wideband transmitted signals with excellent spectral containment and low range sidelobes, practical radar transmitters introduce distortion which means that these ideals are difficult to realize.

This article presents the results of a series of measurements of multistatic radar signatures of small UAVs at L‐ and X‐bands. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter‐receiver and two bistatic receivers. NeXtRAD is capable of recording simultaneous bistatic and monostatic data with baselines and two‐way bistatic range of the order of a few kilometres. The paper presents an empirical analysis with range‐time plots and micro‐Doppler signatures of UAVs and birds of opportunity recorded at several hundred metres of distance. A quantitative analysis of the overall signal‐to‐noise ratio is presented along with a comparison between the power of the signal scattered from the drone body and blades. A simple study with empirically obtained features and four supervised‐learning classifiers for binary drone versus non‐drone separation is also presented. The results are encouraging with classification accuracy consistently above 90% using very simple features and classification algorithms.

This paper presents analysis of data captured with the NeXtRAD multistatic radar system during a fortnight of experimental trials in December 2019. The trials saw, for the first time, the NeXtRAD system capturing interleaved X-band and L-band measurements of multiple UAVs in simultaneous monostatic and bistatic configurations. Analysis is presented of the UAV's micro-Doppler signatures, permitting a discussion into the challenges some DAV platforms present for reliable detection. Comparisons are also made between X-band and L-band monostatic and bistatic UAV radar backscatter allowing conclusions to be drawn over the benefits of particular radar configurations for aiding UAV detection.

This chapter reviews how multistatic radar sensor networks can be utilised to sense a series of different moving targets to extract their micro-Doppler (μD) signatures and use these for classification purposes. The field of radar has advanced significantly since its inception in the early twentieth century. From these origins, typical radar systems have evolved from single transmit/receive configurations into complex sensor networks characterised by a multiple nodal dynamic. The future of radar sensing is moving towards intelligent distributed sensing due to the many advantages this brings. There are still significant challenges that need to be addressed through innovative solutions, such as location and synchronisation even within a GNSS-denied environment, but promising solutions are already maturing in Technology Readiness Level (TRL).

Reinforcement learning is the problem of autonomously learning a policy guided only by a reward function. We evaluate the performance of the Proximal Policy Optimization (PPO) reinforcement learning algorithm on a sensor management task and study the influence of several design choices about the network structure and reward function. The chosen sensor management task is optimizing the sensor path to speed up the localization of an emitter using only bearing measurements. Furthermore, we discuss generic advantages and challenges when using reinforcement learning for sensor management.

Interest in bistatic radar has fluctuated since its first demonstration. Modern multistatic and MIMO radar systems have prompted a resurgence in the field, particularly where imaging radar and automatic target recognition are concerned. The lack of openly-available bistatic imagery and corresponding analysis of the unique artefacts which occur within it is a significant barrier to developing automatic target recognition methods for such systems. This paper looks to address these issues by presenting an appropriate simulation methodology for obtaining bistatic synthetic aperture radar imagery of ground vehicle targets and investigating the features that occur within this imagery. In this paper, a number of effects unique to the bistatic case are presented, and the performance degradation of a classifier at several bistatic angles is empirically demonstrated. A version of the final database will be publicly released to promote wider research into this challenge.

The work presented in this paper shows the performance of various time-frequency distributions when gathering ELectronic INTelligence (ELINT) from an electromagnetic environment that contains transmissions from radars operating in a Low Probability of Interception (LPI) mode. A radar device varying waveform parameters on a pulse-by-pulse basis to enhance sensing capabilities and/or to avoid interception warrants a method that can assign a unique Pulse Descriptor Word (PDW) to each pulse detected. The simulations presented here makes use of a Deep Learning classifier that is fed by time-frequency representations of noisy LFM and FMCW pulses that each have unique signal parameters. The performance of the radar pulse classifier is conveyed for multiple time-frequency methods. The results show that the time-frequency representation requirements for accurate PDW generation varies for each signal parameter being estimated whilst also having a dependence on the SNR of the intercepted signal.

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 study is that radar systems should not be classed as cognitive, or not cognitive, but should be graded by the degree of cognition exhibited. The authors 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.

Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification. The time-frequency spectrogram is first obtained by sparsity-driven time-frequency analysis. Then three empirical micro-Doppler features are extracted from the time-frequency spectrogram and a support vector machine is used to classify six kinds of dynamic hand gestures. The experimental results on measured data demonstrate that, compared to traditional time-frequency analysis techniques, sparsity-driven time-frequency analysis provides improved accuracy and robustness in dynamic hand gesture classification.

In this paper, a sparsity-driven method of micro-Doppler analysis is proposed for dynamic hand gesture recognition with radar sensors. First, sparse representations of the echoes reflected from dynamic hand gestures are achieved through the Gaussian-windowed Fourier dictionary. Second, the micro-Doppler features of dynamic hand gestures are extracted using the orthogonal matching pursuit algorithm. Finally, the nearest neighbor classifier is combined with the modified Hausdorff distance to recognize dynamic hand gestures based on the sparse micro-Doppler features. Experiments with real radar data show that the recognition accuracy produced by the proposed method exceeds 96% under moderate noise, and the proposed method outperforms the approaches based on principal component analysis and deep convolutional neural network with small training dataset.

A new simulation and processing methodology based on open source tools to produce high fidelity synthetic aperture radar (SAR) simulations of ground vehicles of varying types, as well as analysis of an applied automatic target recognition (ATR) technique is presented in this Letter. This work is based around the RaySAR open-source model and the outputs have been configured for both monostatic and bistatic geometries. Input CAD models of various military and civilian vehicles are used to produce the SAR imagery. This output imagery was then used to train a tiny you only look once convolutional neural network (CNN) classifier. The classification success of the CNN applied was showed to produce significantly accurate results and the whole pipeline of processing enabled rapid evaluation of potential ATR methods against targets of choice.

Hedy Lamarr (born Hedwig Eva Maria Kiesler, 1914, in Vienna, Austria) is best known as a Hollywood actress of the 1940s and 1950s. Less well known is the fact that she was also a talented and innovative inventor. In 1942, she and avant-garde musician and inventor George Antheil developed and patented a communications scheme for use in a radio guidance system for torpedoes. The key novelty was that this exploited frequency-hopping both so as not be discovered by an opponent and as a counter to jamming, and this may be considered as a forerunner of spread-spectrum techniques which are now widely used in secure communications. The author notes sources for further reseach.

In this paper, the elevation coverage of common passive radar illuminators of opportunity is evaluated. In constructing radar performance metrics, it is imperative to include the radiation characteristics of the source, both in azimuth and elevation. Many passive radar performance studies have considered only one plane, the azimuth, neglecting to incorporate the elevation plane in models. The noninclusion of elevation patterns has primarily been due to lack of precise detail of their specification and nature. The authors have consulted the broadcast antenna manufacturing industry and present here transmitter elevation patterns for the three prevalent passive radar illuminators of opportunity: frequency modulation radio, digital audio broadcast, and digital video broadcast terrestrial. The patterns presented are typical of broadcast patterns in use throughout the world. The patterns have already been used by a number of national research institutes for more accurate passive radar system modeling. Simulations will show that airborne targets frequently reside outside of the elevation main lobe, which has direct consequences for target detection performance.

Klein Heidelberg (KH) was a German WW2 bistatic radar system that used the British Chain Home radars as its illumination source. A paper published in 2010 presented information from previously classified sources on its origins and performance. Subsequently, some further information has been found, in particular concerning the dates by which the different KH stations become operational and their performance. This study summarises this new information, and discusses the dates by which each KH station was operational, the evidence for its effectiveness and the countermeasures considered by the British against KH.

Radar detection of small maritime targets is of great interest in the context of coastal and port security for prevention of activities such as smuggling and piracy. Multistatic radar and forward scatter radar offer detection advantages compared with conventional monostatic systems, such as advantageous multi-perspective target view for the former and target radar cross-section enhancement for the latter. In this study, experimental results are presented to characterise simultaneous measurements investigating the detection of a small inflatable boat by a multistatic radar and a forward scatter radar. These results are believed to be the first example of simultaneous experimental comparison of such systems.

The negative effects wind farm clutter has on the performance of radar systems for Air Traffic Control and Air Surveillance is well-known in the radar research community and several mitigation techniques have been proposed to address this problem. These include bistatic and multistatic radar systems providing multiple views of the area under surveillance, and hence potential additional information that can be used to improve the receiver performance. This paper presents the analysis of a set of experimental data collected simultaneously by two radar systems, one operating at S-band and one at X-band, of echoes from an operational wind farm in the UK near Oxford. This analysis presents several parameters extracted from the time domain data and the Doppler spectra, such as Doppler centroid and bandwidth of the micro-Doppler signature as well as amplitude statistics of the time domain returns. These parameters are characterised using data recorded at monostatic and bistatic nodes, as well as at different polarisation combinations.

An approach for the prediction of topside electromagnetic compatibility in the concept phase of naval ship design is proposed and demonstrated. The approach was developed by utilizing the commercially available numerical computational package, Computer Simulation Technology, to assess the electromagnetic environment of a Royal Navy Type 22 Batch II Frigate. A number of the results of such an assessment were validated using measurements on a 1:50 scale copper model of the Type 22 Batch II Frigate. The approach was then applied to a new concept phase design study for a Future Patrol Ship, produced by UK Ministry of Defence's Naval Design Partnership. This work is expected to be useful in assessing the severity of topside EMI, using numerical modeling and simulations, in Concept-Phase Ship Design.

This paper illustrates the results of a series of measurements of multistatic radar signatures of small UAVs at L and X band. The system employed was the multistatic multiband radar system, NeXtRAD, consisting of one monostatic transmitter-receiver and two bistatic receivers. Results demonstrate the capability of the system of recording bistatic data with baselines and two-way bistatic range of the order of few kilometres., © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

The German MAMMUT radar was a WWII long-range air defence equipment which used a fixed antenna array with electrical steering of the beam over ±50° by means of line-stretcher phase-shifters. As such, it was arguably the first operational phased array radar. This paper describes the origins of the design, the remarkable technology that was developed by German radar engineers for the purpose, and assesses the performance and value of the radar.

Micro-Doppler signatures are extremely valuable in the classification of a wide range of targets. This paper investigates the effects of jamming on the micro-Doppler classification performance and explores a potential deep topology enabling low-bandwidth data fusion between nodes in a multistatic radar network. The topology is based on an array of three independent deep neural networks (DNNs) functioning cooperatively to achieve joint classification. In addition to this, a further DNN is trained to detect the presence of jamming, and from this, it attempts to remedy the degradation effects in the data fusion process. This is applied to the real experimental data gathered with the multistatic radar system, NetRAD, of a human operating with seven combinations of holding a rifle-like object and a heavy backpack that is slung on their shoulders. The resilience of the proposed network is tested by applying synthetic jamming signals into specific radar nodes and observing the networks’ ability to respond to these undesired effects. The results of this are compared with a traditional voting system topology, serving as a convenient baseline for this paper.