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Automatic target classification is a critical capability for non-cooperative drone surveillance radars in several defence and civilian applications. It is accordingly a well-established research field and numerous algorithms exist for recognising targets, including miniature unmanned air systems (i.e., small, mini, micro and nano platforms), from their radar signatures. They have notably benefited from advances in machine learning (e.g., deep neural networks) and are increasingly able to achieve remarkably high accuracies. Such classification results are often captured by standard, generic, object recognition metrics and originate from testing on simulated or real radar measurements of drones under high signal to noise ratios. Hence, it is difficult to assess and benchmark the performance of different classifiers under realistic operational conditions. In this paper, we first outline the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance metrics, from an end-user perspective. They are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustrations. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar.

This paper presents a novel approach to simulating micro-Doppler signatures caused by drones. The focus of this work is to produce realistic signatures that represent the variation that is observed in live radar measurements. In order to accomplish this, the kinematics and dynamics of a drone flight are modelled to capture the changing rotor rotation rates. The simulation results show realistic variation that is representative of measured drone flights.

Staring radars use a transmitting static wide-beam antenna and a directive digital array to form multiple simultaneous beams on receive. Because beams are fixed, the radar can employ long integration times to detect slow low-RCS targets, such as drones, which present a challenge to traditional air surveillance radar. The use of multiple spatially separated receivers cooperating with the staring transmitters in a multistatic network allows multi-perspective target acquisitions that can help mitigate interference and ultimately enhance the detection of drones and reduce estimation errors. Here, the development and experimental results of a passive, dual-channel, L-band receiver are presented. The receiver has been used to take measurements of both moving vehicles of drones in flight using a bistatic staring transmitter. An analysis of the receiver is presented using GPS is used to quantify the estimation performance of the receiver.

The operation of Unmanned Aerial Systems (UAS) is widely recognised to be limited globally by challenges associated with gaining regulatory approval for flight Beyond Visual Line of Sight (BVLOS) from the UAS Remote Pilot. This challenge extends from unmanned aircraft flights having to follow the same ‘see and avoid’ regulatory principles with respect to collision avoidance as for manned aircraft. Due to the technical challenges of UAS and Remote Pilots being adequately informed of potential traffic threats, this requirement effectively prohibits BVLOS UAS flight in uncontrolled airspace, unless a specific UAS operational airspace is segregated from manned aviation traffic, often achieved by use of a Temporary Danger Area (TDA) or other spatial arrangements. The UK Civilian Aviation Authority (CAA) has defined a Detect and Avoid (DAA) framework for operators of UAS to follow in order to demonstrate effective collision avoidance capability, and hence the ability to satisfy the ‘see and avoid’ requirement. The National BVLOS Experimentation Corridor (NBEC) is an initiative to create a drone experimentation facility that incorporates a range of surveillance and navigation information sources, including radars, data fusion, and operational procedures in order to demonstrate a capable DAA System. The NBEC is part located within an active Airodrome Traffic Zone (ATZ) at Cranfield Airport, which further creates the opportunity to develop and test systems and procedures together with an operational Air Traffic Control (ATC) unit. This allows for manned and unmanned traffic to be integrated from both systems and procedural perspectives inside segregated airspace in a first stage, and then subsequently transiting to/from non-segregated airspace. The NBEC provides the environment in which a number of challenges can be addressed. This paper discusses the lack of target performance parameters, the methodology for gaining regulatory approval for non-segregated BVLOS flights and for defining peformance parameters for counter UAS (cUAS).

This paper focuses on the need for good target recognition in order to provide effective tracking and on good tracking to provide effective recognition in the application of radar to drone surveillance. The joint function, merging drone tracking and recognition, referred to as Simultaneous Tracking and Recognition (STaR) of drones is discussed and the benefits are presented through examples. This includes real measurements from Aveillant's Gamekeeper Radar.

The concept of a traditional monostatic radar with co-located transmit and receive antennas naturally imposes performance limits that can adversely impact applications. Using a multiplicity of transmit and receive antennas and exploiting spatial diversity provides additional degrees of design freedom that can help overcome such limitations. Further, when coupled with cognitive signal processing, such advanced systems offer significant improvement in performance over their monostatic counterparts. This will also likely lead to new applications for radar sensing. In this paper we explore the fundamentals of multistatic network radar highlighting both potential and constraints whilst identifying future research needs and applications. Initial experimental results are presented for a 2-node networked staring radar.

This paper presents an analysis of the radar signature of micro-UAVs whilst in flight when subjected to realistic environmental flight conditions. These highly dynamic signatures have been found to be significantly different from modelled signatures or those expected from the RCS when measured in a benign environment. The time varying radar returns of different micro-UAV targets, measured in different conditions, are presented and characterized. Also the varying characteristics of the micro-Doppler rotor returns are analyzed. The impact of results on radar systems design is discussed.

Staring radars have the potential to offer significant advantages in the detection of some hitherto difficult to detect targets. This paper sets out the advantages of staring radars, introduces the Alarm radar and presents new results from fundamental detection and tracking radar performance evaluations comparing staring radars with scanning radars.

Staring surveillance radars have the potential to offer significant advantage in the detection of some hitherto difficult to detect targets. This paper sets out the advantages of staring radars and gives two examples where they have demonstrated unique capabilities.

We present a system for detecting discrete periodic events over a short interval and in the presence of interference. In the first stage symmetries are identified using scale-space representation. This process detects signal events with a low signal-to-noise ratio but has the potential to introduce a number of false responses. This process is followed by an entropy-based algorithm that can robustly extract periodicities from a set of observed discrete events in the presence of a large number of false alarms. The event detection and period extraction processes have a low computational cost and can extract signal periodicity after a short observation time. This scheme was evaluated against four previously reported methods. Results demonstrate that the period extraction algorithm presented here is more reliable than three of the previously reported algorithms. The reliability of the algorithm presented here was similar to that of the fourth method but the computational cost was much less.

We describe and evaluate a model-based scheme for feature extraction and model-based signal identification which uses likelihood criteria for “edge” detection. Likelihood measures from the feature identification process are shown to provide a well behaved measure of signal interpretation confidence. We demonstrate that complex, transient signals, from one of 6 classes, can reliably be identified at signal to noise ratios of 2 and that identification does not fail until the signal to noise ratio has reached 1. Results show that the loss in identification performance resulting from the use of a heuristic, rather than an exhaustive, search strategy is minimal.

This chapter quantifies the effect of interference on radar systems and provides a diverse range of waveform design and processing techniques that allow the mitigation of interference between radars and other service users within a given band and hence allow more users in the finite available bandwidth.

Chaotic signals add to the design repertoire for radar. This paper discusses the properties of chaotic signals, their generation and use, including transmitter hardware and efficiency, with reference to results in communications research and recent theoretical and practical results in sonar, and development throughout the world for radar. Practical issues arising from the unique properties of chaotic systems are considered. Phase locking and target identification benefits are also suggested. Results of sonar experiments and trials to support prediction from simulation findings are given. It is concluded that chaotic signals have the potential to offer exciting additional capabilities.

The results and findings of an OFCOM funded project concerned with efficient radar waveforms for spectral sharing will be presented. Measures to improve spectral efficiency of in-service radar, future convention radar systems and novel radar architectures are discussed. Spectrally efficient waveforms are reviewed as are spectrum sharing techniques that would form valuable additions to the future civil radar system designer's arsenal.

This paper serves to provide a thorough introduction to chaotic signal generation and chaotic radar waveform modulation, emphasizing the diversity and ease of variation of such waveforms. Results of their adaptation to a specific waveform performance objective are presented as is their potential use as target matched waveforms.

Chaotic signals add to the design repertoire for radar and sonar. Building on findings in communications systems, the ability of chaotic radar and sonar to offer improvements in transmitter hardware, covertness and coverage of the search volume are discussed. Monostatic and multistatic operation and dual use are covered. Practical issues arising from the unique properties of chaotic systems are considered and the results of acoustic experiments and trials to support prediction from simulation findings are given. It is concluded that chaotic signals offer advantages over noise signals.

Major advances in base technologies of computer processors and low cost communications have paved the way for a resurgence of interest in unattended ground sensors. Networks of sensors offer the potential of low cost persistent surveillance capability in any area that the sensor network can be placed. Key to this is the choice of sensor on each node. If the system is to be randomly deployed then nonline of sight sensor become a necessity. Acoustic sensors potentially offer the greatest level of capability and will be considered here. As a passive sensor, only time of arrival or bearing information can be obtained from an acoustic array, thus the tracking of targets must be done in this domain. This work explores the critical step between array processing and implementation of the tracking algorithm. Specifically, unlike previous implementations of such a system, the bearings from each frequency interval of interest are not averaged but are used as data points within a Kalman filter. Thus data is not averaged and then filtered but all data is put into the tracking filter.

This work describes the development of a tool that predicts the coverage and performance of sensor networks. Specifically it examines weapon locating radars and acoustic sensors in different terrain and weather conditions. The computer environment and multiple sensor models are presented. Fusion of sensors takes multiple predicted accuracy metrics from the single sensor performance models and combines them to show networked performance. Calculations include Cramer-Rao lower bound computation of the sensors and the fused sensors' source location error. Results are presented showing the outputs of the models in the form of sensor accuracy maps superimposed onto terrain maps and early conclusions assessing feasibility of model reduction to simple algorithms are given.

This paper describes the development of a tool that predicts the coverage and performance of sensor networks. Specifically it examines weapon locating radars and acoustic sensors in different terrain and weather conditions. The computer environment and multiple sensor models are presented. Fusion of sensors takes multiple predicted accuracy metrics from the single sensor performance models and combines them to show networked performance. Calculations include Cramer-Rao lower bound computation of the sensors and the fused sensors source location error. Results are presented showing the outputs of the models in the form of sensor accuracy maps superimposed onto terrain maps.

Major advances in base technologies of computer processors and low cost communications have paved the way for a resurgence of interest in unattended ground sensors. Networks of sensors offer the potential of low cost persistent surveillance capability in any area that the sensor network can be placed. Key to this is the choice of sensor on each node. If the system is to be randomly deployed then non line of sight sensor become a necessity. Acoustic sensors potentially offer the greatest level of capability and will be considered here. In addition, there is a trade off between sensor density and tracking technique that will impact on cost. As a passive sensor, only time of arrival or bearing information can be obtained from an acoustic array, thus the tracking of targets must be done in this domain. This paper explores the critical step between array processing and implementation of the tracking algorithm. Specifically, unlike previous implementations of such a system, the bearings from each frequency interval of interest are not averaged but are used as data points within a Kalman filter. Thus data is not averaged and then filtered but all data is put into the tracking filter.

We describe a system for detecting complex discrete periodic events by identifying symmetries in their scale-space representation using a medial-axis transform. Whilst allowing events with varying characteristics and very low signal to noise ratios to be detected, this also has the potential to introduce a large number of false alarms. We, therefore, also present an entropy-based algorithm that can robustly extract periodicities from a set of observed events with a large proportion of missing or false alarms. The problem of detecting discrete periodic signals and extracting their characteristics is frequently encountered in communications, radar and speech processing applications. The event detection and period extraction processes described here have a low computational cost and can extract signal periodicity after a short observation time (less that 10 repetitions of the period). We demonstrate a period extraction algorithm that is faster than previously reported algorithms and more robust than many, including those based on histogramming and Kalman filtering. When the number of false alarms equals that of detected events the period is correctly determined in 90% of cases (compared to 40% for a Fourier based algorithm). A technique using circular statistics gives 95% success but requires 10 times more computation. (6 pages)