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.

By emulating the cognitive perception–action cycle believed to be at the core of animal cognition, cognitive radars promise to improve radar performance over standard systems. The fully adaptive radar (FAR) framework provides a generalised approach to implementing a single cognitive perception–action cycle for radar systems, but complex adaptive problems necessitate the interaction of multiple perception–action cycles. This study describes the general form of the hierarchical FAR (HFAR) framework. The HFAR framework is applied to a single-target tracking, sensor fusion problem, and real-time experimental results demonstrate the efficacy of the proposed architecture for handling problems of varying scales in a consistent, adaptive fashion.

By emulating the neuropsychological processes underpinning animal cognition, the field of cognitive radar seeks to improve performance compared to non-adaptive systems. The fully adaptive radar (FAR) framework is an application agnostic means of implementing the perception–action cycle in radars. This work proposes a method of designing the FAR framework's component cost functions inspired by the field of multi-objective optimisation. As an illustration, the general cost functions were used to implement waveform adaptation for single target tracking. Both simulated and experimental results demonstrated how altering the cost functions can tailor the FAR performance to specific radar operating modes.

The authors present an algorithm for adaptive selection of pulse repetition frequency or antenna activations for Doppler and direction of arrival estimation. The adaptation is performed sequentially using a Bayesian filter, responsible for updating the belief on parameters, and a controller, responsible for selecting transmission variables for the next measurement by optimising a prediction of the estimation error. This selection optimises the Weiss–Weinstein bound (WWB) for a multi-dimensional frequency estimation model based on array measurements of a narrow-band far-field source. A particle filter implements the update of the posterior distribution after each new measurement is taken, and this posterior is further approximated by a Gaussian or a uniform distribution for which computationally fast expressions of the WWB are analytically derived. They characterise the controller's optimal choices in terms of signal-to-noise ratio and variance of the current belief, discussing their properties in terms of the ambiguity function and comparing them with optimal choices of other WWB constructions in the literature. The resulting algorithms are analysed in simulations where they showcase a practically feasible real-time evaluation based on look-up tables or small neural networks trained off-line.

Echolocating bats have evolved the ability to detect, resolve and discriminate targets in highly challenging environments using biological sonar. The way bats process signals in the receiving auditory system is not the same as that of radar and sonar and hence investigating differences and similarities might provide useful lessons to improve synthetic sensors. The Spectrogram Correlation And Transformation (SCAT) receiver is an existing model of the bat auditory system that takes into account the physiology and the neural organisation of bats that emit broadband signals. In this study, the authors present a baseband receiver equivalent to the SCAT that allows an analysis of target echoes at baseband. The baseband SCAT (BSCT) is used to investigate the output of the bat-auditory model for two closely spaced scatterers and to carry out an analysis of range resolution performance and a comparison with the conventional matched filter. Results firstly show that the BSCT provides improved resolution performance. It is then demonstrated that the output of the BSCT can be obtained with an equivalent matched-filter based receiver. The results are verified with a set of laboratory experiments at radio frequencies in a high signal-to-noise ratio.

In this study, the authors present two biologically-inspired angular localisation techniques for radar which separately use the magnitudes and phases of the wideband received signals as the cues for angular target localisation. By comparison with predetermined map functions, the angle to a target may be estimated with good accuracy and over a wide angular range of operation. These techniques are implemented in a radar system with a single transmitter and two offset receiving antennas, allowing us to draw upon cues derived from biological systems that are often only explored in psychology, biology, and psychoacoustics.

Modern phased-array multifunction radars have the ability to change or schedule the tasks of the beam in order to accomplish all their missions in an optimised fashion. The resource manager of the radar must then control the update rate of the measurement task of target active tracking, so as to minimise the radar computer load without losing targets. Scarce measurements lead to low radar load, but they also lead to an increased number of illuminations at each measurement epoch to find the target. On the basis of this rationale, a sound procedure was proposed by Blackman and van Keuk to derive an optimal measurement rate. Their optimisation criterion is established using a linear Singer target model and a linear Kalman filter. In this study, their method is extended, and the authors propose a versatile optimal update rate algorithm that is applicable to virtually any non-linear target model combined with any non-linear filter able to output an error covariance matrix. This includes extended Kalman filter (EKF), Unscented Kalman Filter (UKF), Interacting Multiple Model (IMM) algorithm, and particle filters. For numerical experiments and validation, they consider a non-linear target model based on Frenet–Serret three-dimensional equations, and the tracking is performed by a non-linear invariant EKF.

Cognitive radars are systems capable of optimising emission and processing by exploiting knowledge about environment and operational scenario. Those improvements are achieved by controlling new degrees of freedom offered by modern radar technology. In particular, active phased-array radars can perform two-dimensional beam-steering and beam-forming. Those new capabilities allow adaptation of radar search patterns to localised constraints. The authors present an improvement from their previous set cover approximation for radar search pattern time-budget minimisation, accounting for localised constraints of terrain masking and direction-specific scan update rates. The addition of those constraints, however, does not modify the underlying mathematical structure of the problem, which can be solved using integer programming methods.

A modern radar may be designed to perform multiple functions, such as surveillance, tracking, and fire control. Each function requires the radar to execute a number of transmit–receive tasks. A radar resource management (RRM) module makes decisions on parameter selection, prioritisation, and scheduling of such tasks. RRM becomes especially challenging in overload situations, where some tasks may need to be delayed or even dropped. In general, task scheduling is an NP-hard problem. In this work, the author develops the branch-and-bound (B&B) method which obtains the optimal solution but at exponential computational complexity. On the other hand, heuristic methods have low complexity but provide relatively poor performance. They resort to machine learning-based techniques to address this issue; specifically, they propose an approximate algorithm based on the Monte Carlo tree search method. Along with using bound and dominance rules to eliminate nodes from the search tree, they use a policy network to help to reduce the width of the search. Such a network can be trained using solutions obtained by running the B&B method offline on problems with feasible complexity. They show that the proposed method provides near-optimal performance, but with computational complexity orders of magnitude smaller than the B&B algorithm.

The authors present cognitive automotive radar (CARr) as a significant capability to the sensing technology suite of autonomous driving vehicles. CARr is a closed-loop intelligent radar system utilising the automotive frequency bands of 24–25 and 76–77 GHz for the purposes of ground vehicle target signature identification. The authors consider specific cases of ground vehicle recognition and ground vehicle class identification in the presence of aspect angle uncertainty using forward-looking automotive radar. The transmit-adaptive waveforms are based from signal-to-noise ratio and mutual information optimisation metrics. In this study, two new adaptive waveform techniques with the flexibility to accommodate angular uncertainty probability distributions are introduced. The classification performance of these new waveforms is compared against the receive-adaptive wideband pulsed and other transmit-adaptive waveforms in the presence of angular uncertainties characterised by the uniform and truncated normal distributions. To ensure the validity of results, high-fidelity electromagnetic simulated radar cross-section signatures generated from scaled-to-true-physical-size ground vehicle computer-aided design models are utilised.

Spectrum sensing and transmit notching is a form of cognitive radar that seeks to reduce mutual interference with other spectrum users in the same band. This concept is examined for the case where another spectrum user moves in frequency during the radar's CPI. The physical radar emission is based on a recent FM noise waveform possessing attributes that are inherently robust to sidelobes that otherwise arise for spectral notching. Due to increasing spectrum sharing with cellular communications, the interference considered takes the form of in-band OFDM signals that hop around the band. The interference is measured each PRI and a fast spectrum sensing algorithm determines where notches are required, thus facilitating a rapid response to dynamic interference. To demonstrate the practical feasibility and to understand the trade-space such a scheme entails, free-space experimental measurements based on notched radar waveforms are collected and synthetically combined with separately measured hopping interference under a variety of conditions to assess the efficacy of such an approach, including the impact of interference hopping during the radar CPI, latency in the spectrum sensing/waveform design process, notch tapering to reduce sidelobes, notch width modulation due to spectrum sensing, and the impact of digital up-sampling on notch depth.

This study addresses the problem of beam-space post-Doppler reduced-dimension (RD) space–time adaptive processing (STAP) using a modified generalised sidelobe canceller (GSC) in an airborne collocated multiple-input multiple-output (MIMO) radar. Steering vectors corresponding to the nulls of beamforming are used to construct the blocking matrix (BM) of the GSC processor with optimal signal to clutter plus noise ratio. However, it is difficult to determine the beampattern nulls for a collocated MIMO radar which is equivalent to a virtual array with coherent amplitude weights. To build a valid BM of the GSC-based STAP processor, the authors propose a BM modification method based on the modified Gram–Schmidt orthogonalisation algorithm. The authors prove that the modified RD-GSC processor can achieve the performance equal to that of the fully optimal processor conditionally. The authors also show that the proposed method has reduced computational complexity and fast convergence rate. Numerical simulations are presented to demonstrate the effectiveness of the proposed methods.

Data association with great efficiency is an important problem in multiple extended target tracking. It is more challenging than that in point target tracking as the measurements of extended targets are more. This study presents a graphical model formulation of data association for extended target tracking, which is solved by belief propagation (BP) to obtain estimations. To construct the graphical model, a simplified scheme for measurements is proposed to improve the tracking efficiency, which is based on minimal spanning tree algorithm. Then, the data association problem according to the simplified measurement set is presented, solved by BP. Finally, experiment results show the proposed algorithm has advantages over the algorithm based on joint probabilistic data association and the previous data association algorithm, introduced by Vivone and Braca, in terms of robustness and efficiency.

As more and more spacecraft launched into the space, the probability of radiofrequency (RF) interference raises and the burden of RF spectrum management increases as well. The monitoring of global RF beam information and identifying of spot beam emitters location can effectively obtain space data resources, which will do great help for mitigating global RF interference and optimising space spectrum resources. This study proposes a novel location awareness method for spot beam emitters based on the nanosatellite platform. The proposed method identifies the position of spot beam emitters on a global scale by flying a low earth orbit CubeSat constellation through the coverage of the spot beams. The properties such as geometry, the target motion equation, the measurement process, and the Particle Filter equations are all addressed with respect to the location methods. The authors illustrate a realistic scenario simulated within the Systems Tool Kit, in which CubeSats receive signals from the spot beam emitters. The scenario shows that their location awareness method proposed in this study is available and efficient.

The use of vector sensors as receivers for underwater acoustic communications systems is gaining popularity. It has become important to obtain performance measures for such communication systems to quantify their efficacy. The fundamental advantage of using a vector sensor as a receiver is that a single sensor is able to provide diversity gains offered by multiple input, multiple output systems. In a recent work, a novel framework for evaluating capacity of underwater channel was proposed. The approach is based on modelling the channel as a set of paths along which the signal arrives at the receiver with different angles of arrival. Here, the authors build on that framework to provide a bound on the achievable capacity of such a system. The analytical bounds have been compared against simulation results for a vector sensor-based single input, multiple output underwater communications system. The channel parameters are modelled by analysing the statistics generated with Bellhop simulation tool. This representation of the channel is flexible and allows for characterising channels at different geographical locations and at different time instances. This characterisation in terms of channel parameters enables the computing of the performance measure (channel capacity bound) for different geographical locations.

Low probability of interception (LPI) time refers to a time during which a certain airborne radar system is tracking the target without being intercepted by a high-sensitivity electronic support measurement (HSESM) on the target. LPI time is determined by the tracking process of the certain airborne radar system and the interception performance of an HSESM. This study proposes a track mission shifting (TMS) tactic of the distributed airborne radar system (DARS) or distributed airborne formation radar system (DAFRS) based on LPI time. First, this study defines a short-time average interception probability of HSESM on frequency search mode. Then, with adaptive dwell time design and the maximum illumination interval algorithm based on predicted tracking error covariance matrix, this study proposes to take interactive multiple models Kalman filter to describe target tracking process and introduces binary hypothesis test of chi-square and non-central chi-square distributions as target detection criterion during target tracking. Finally, with a partial prior knowledge of the opposed HSEMS on the target, this study proposes a TMS tactic model based on LPI time. Simulations show that LPI time is a significant reference value for DARS or DAFRS to turn over track mission against an opposed HSEMS.

In this study, the authors introduce a new framework for 1-bit compressed synthetic aperture radar (SAR) imaging by using time-varying thresholding. They show how to recover sparse SAR images from noisy measurements which have been quantised to 1-bit with time-varying thresholds. In the conventional 1-bit compressive sensing (CS) SAR imaging methods, 1-bit quantisation is implemented by comparing the received signal to a zero threshold. This makes the information about the magnitude of the signal to be lost and exact signal recovery becomes impossible. One-bit quantisation with time-varying thresholds allows them to reconstruct the magnitude of the signal more accurately and an explicit unit-norm constraint is no longer required in the proposed optimisation formulation. Using the proposed approach, the authors formulate 1-bit CS SAR imaging reconstruction problem as an unconstrained optimisation problem where the objective function includes an data-fidelity term and a non-smooth regularisation function. In order to solve this unconstrained optimisation problem, they use variable splitting and the alternating direction method of multipliers based approach which is computationally efficient and easy to implement. The results from experiments with synthetic and real SAR images validate the effectiveness of the proposed method named as BCST-SAR (binary CS with time-varying thresholds in SAR imaging).

The problem of resolving and detecting masked weak moving targets in the presence of fast strong targets is particularly difficult due to side lobes produced by ordinary signal processing techniques and becomes even harder when a partially returned signal is received. Where return signals coincide with the transmission of a pulse, pulse eclipsing can occur which results in detection performance loss. In this approach, the output of a matched filter (MF) was utilised as the input to the post-processing reiterative minimum mean square error (RMMSE) algorithm. The mismatch created in the received signal by Doppler phase shift and pulse eclipsing degrades adaptive pulse compression (APC) filter performance. This study presents a new method to account for both Doppler phase shift and pulse eclipsing using the modified APC repair (MAPCR) algorithm. Simulation results show that the proposed MAPCR algorithm mitigates the eclipsing effect and reduces range side lobes significantly, in addition to providing improved range resolution. The authors compare their results for single point and extended targets placed in the eclipsed region with other algorithms such as the standard MF, pulse compression repair, APC-eclipsing repair and MF-RMMSE.

Based on sequential inverse synthetic aperture radar (ISAR) images, the three-dimensional target structure can be reconstructed using the factorisation method. However, it requires accurate scatterer trajectory formation, which is difficult due to the occlusion and trajectory crossing. To address this problem, the authors propose a novel scatterer trajectory association method based on Markov chain Monte Carlo (MCMC) algorithm. First, they derive the ellipse movement characteristics of each scatterer trajectory under stationary rotational motion model of the observed target. Then, by computing the signal-to-noise ratio of the compressed echoes, the number and positions of the scatterers in each ISAR image can be extracted precisely and efficiently through two-dimensional estimation of signal parameters via rotational invariance techniques. Next, they present a Bayesian model and inference algorithm for the scatterer trajectory association problem. MCMC is applied to estimate the scatterer trajectory matrix. Particularly, they design new prior and likelihood evaluation criterions in MCMC by making use of the ellipse movement characteristics of each scatterer trajectory. Experimental results on simulated data validate the effectiveness of the proposed method.