Showing papers in "Iet Radar Sonar and Navigation in 2018"

State-of-the-art technologies for UAV inspections

Abstract: Visual condition inspections serve as the basis for determining the need and schedule for service tasks such as maintenance and remediation projects to preserve the proper functioning of power facilities and infrastructure. An increasing accumulation of service projects has recently surfaced due to the lengthy, labour-intensive and subjective qualities of the current method for inspection. These processes are also costly due to the temporary closure of the infrastructure as well as the requirement of special inspection equipment. Unmanned aerial vehicles (UAVs), commonly known as drones, offer potential as a useful tool for infrastructure inspections. UAVs provide visual assessments of structures while eliminating the need for manual inspections. Thus, aerial systems have the potential to reduce the cost of inspections as well as limit the disruption of the public while allowing engineers to have a better three-dimensional understanding of the system. However, the implementation of UAV inspection includes several difficulties such as flight stability, control accuracy, and safety. This study summarises the context for UAV inspection of power facilities and structures. Technologies to address the hindrances preventing UAV integration into the current practice are reviewed. Existing challenges and future work in research for UAV inspections are also presented.

Review of radar classification and RCS characterisation techniques for small UAVs or drones

Abstract: This review explores radar-based techniques currently utilised in the literature to monitor small unmanned aerial vehicle (UAV) or drones; several challenges have arisen due to their rapid emergence and commercialisation within the mass market. The potential security threats posed by these systems are collectively presented and the legal issues surrounding their successful integration are briefly outlined. Key difficulties involved in the identification and hence tracking of these `radar elusive' systems are discussed, along with how research efforts relating to drone detection, classification and radar cross section (RCS) characterisation are being directed in order to address this emerging challenge. Such methods are thoroughly analysed and critiqued; finally, an overall picture of the field in its current state is painted, alongside scope for future work over a broad spectrum.

Practical classification of different moving targets using automotive radar and deep neural networks

Abstract: In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed.

Radar-ID: human identification based on radar micro-Doppler signatures using deep convolutional neural networks

Abstract: Human identification is crucial in various applications, including terrorist attack preventing, criminal seeking, defence and so on. Traditional human identification methods are usually based on vision, biological features, radio-frequency identification cards and so on. In this study, the authors propose an identification method based on radar micro-Doppler signatures using deep convolutional neural networks (DCNNs) for the first time, which can identify human in non-contact, remote and no lighting status. They employ a K-band Doppler radar to acquire the raw signals due to its stationary clutter rejection and movement detection ability as well as its short wavelength which can generate larger Doppler shift. Then short-time Fourier transform is applied to the raw signals to characterise micro-Doppler signatures. They adopt the DCNNs to deal with the spectrograms for human identification problem. The DCNNs can learn the necessary features and classification conditions from raw micro-Doppler spectrograms without employing any explicit features. While the traditional supervised learning techniques relying on the extracted features require domain knowledge of each problem. It is shown that this method can achieve average accuracy ~97.1% for 4 people, 90.9% for 6 people, 89.1% for 8 people, 85.6% for 10 people, 77.4% for 12 people, 72.6% for 16 people and 68.9% for 20 people.

Development of frequency diverse array radar technology: a review

Abstract: Electronic beam steering with greater efficiency is a vibrant feature of a phased array antenna, but for all the range cells, it is fixed at a specific angle. To mitigate this problem, frequency diverse array (FDA) antenna was proposed. This study presents a review on the development of FDA technology in radar and navigation applications. FDA is different from a conventional phased array antenna radar in a sense that it uses a small frequency offset across the array, which helps to generate a range, angle and time-dependent beam pattern. This pattern assures the energy transmission towards the desired angle and range cell. In addition, this study also focuses the research on getting range-angle uncoupled beam patterns along with diverse hybrid cognitive FDA design, available in the literature, for improved radar performance.

Parameter estimation and suppression for DRFM-based interrupted sampling repeater jammer

Abstract: By partial interception and multiple forwarding of a radar transmitting signal, digital radio frequency memory-based interrupted sampling repeater jamming can yield a partial processing gain and form multiple false target groups in the range direction, achieving jamming effects of both suppression and deception. Various improved jamming strategies have been proposed, while jamming suppression problems have not been fully addressed. In this study, a jamming suppression method based on the idea of `reconstruction and cancellation' is proposed by analysing the jamming principle. The method firstly analyses the pulse compression results with time-frequency analysis to obtain the intercepted slice number and forwarding times; then, the slice width is estimated by deconvolution processing; finally, iterative cancellation is used to suppress the jamming. Performance of the method was verified by Monte Carlo simulation. The results show that the normalised error of the slice width estimation is 16 dB.

Experimental demonstration and analysis of cognitive spectrum sensing and notching for radar

Abstract: 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.

Human body and limb motion recognition via stacked gated recurrent units network

Abstract: This study proposes a new radar-based human body and limb motion recognition method that exploited the temporal sequentiality of the motions. A stacked gated recurrent units network (SGRUN) is adopted to extract the dynamic sequential human motion patterns. Since the time-varying Doppler and micro-Doppler signatures can commendably represent such motion patterns, the spectrogram is utilised as the input sequence of the SGRUN. Numerical experiments verify that an SGRUN with two 34-neuron gated recurrent unit layers well classifies and recognises six distinct human body and limb motion types.

Robust and accurate UWB-based indoor robot localisation using integrated EKF/EFIR filtering

Abstract: A novel ultra wideband (UWB)-based scheme is proposed to provide robust and accurate robot localisation in indoor environments. An extended Kalman filter (EKF), which is suboptimal, is combined in the main estimator design with an extended unbiased finite impulse response (EFIR) filter, which has better robustness. In the integrated EKF/EFIR algorithm, the EFIR filter and the EKF operate in parallel and the final estimate is obtained by fusing the outputs of both filters using probabilistic weights. Accordingly, the EKF/EFIR filter output ranges close to the most accurate one of the EKF and EFIR filters. Experimental testing has shown that the EKF/EFIR-based UWB-range robot localisation is more robust than the EKF- and EFIR-based ones in uncertain noise environments.

Modulation classification method for frequency modulation signals based on the time–frequency distribution and CNN

Abstract: Signal modulation classification is an important research subject in both military and civilian field. This study proposed a novel blind modulation classification method based on the time-frequency distribution and convolutional neural network (CNN). This is the first attempt to treat the time-frequency map as a picture and use an outstanding (CNN-based) algorithm in computer vision area for signal recognition. The combination offers a novel feature extraction strategy, to some extent, which also conforms to intuition. Simulation results show that the method proposed in this study is efficient and robust and enables a high degree of automation for extracting features, training weights and making decisions. Additionally, a remarkable performance emerges with small samples and repeated training, which distinguishes this method from many other classification methods.

Radar emitter classification based on unidimensional convolutional neural network

Abstract: Radar emitter classification (REC) is an essential part of electronic warfare (EW) systems. In REC tasks, after deinterleaving, the intercepted radar signals are classified into specific radar types. With new radar types arising and the electromagnetism environment getting complicated, REC has become a big data problem. Meanwhile, there exist inconsistent features among samples. These two problems can affect the performance of classification. In this work, first, the authors designed a novel encoding method to deal with the inconsistent features. High-dimension sequences of equal length are generated as new features. Then a deep learning model named unidimensional convolutional neural network (U-CNN) is proposed to classify the encoded high-dimension sequences with big data. A large and complex radar emitter's dataset is used to evaluate the performance of the U-CNN model with the encoding method. Experiments show that the authors' proposal gains an improvement of 2-3% in accuracy compared with the state-of-the-art methods, while the time consumed for identifying 45,509 emitters is only 1.95 s using a GPU. Specifically, for 12 indistinguishable radars, the classification accuracy is improved about 15%.

Improved method for deinterleaving radar signals and estimating PRI values

Abstract: In the electronic countermeasure, the electronic support measure (ESM) receiver intercepts the interleaved radar pulse streams, which radiates from surrounding radar emitters. At present, deinterleaving algorithm based on time of arrival which is called pulse repetition interval (PRI) deinterleaving occupies an important part of the ESM. The sequential difference histogram (SDIF) is a well-known and effective PRI deinterleaving algorithm, but it has several disadvantages. When the PRI jitter and pulse missing exist in the pulse streams, the SDIF algorithm cannot estimate the real PRI value reliably, and then cannot separate the radar pulse trains corresponding to the real PRI value from the interleaved pulse streams. In order to overcome these disadvantages, an improved SDIF algorithm using clustering algorithm and PRI transform is proposed. Simulation results have proved that this improved algorithm has higher reliability and accuracy in the presence of PRI jitter and pulse missing.

Application of Doppler Beam Sharpening for Azimuth Refinement in Prospective Low-THz Automotive Radars

Abstract: In this study, the authors investigate the application of the Doppler beam sharpening (DBS) technique for angular refinement to the emerging area of low-terahertz (THz) radar sensing. Ultimately this is to improve radar image quality in the azimuth plane to complement the excellent range resolution and thus improve object classification in low-THz radar imaging systems for autonomous platforms. The study explains the fundamental theory behind the process of DBS and describes the applicability of DBS to automotive sensing, indicating the potential for synthetic beamwidths of a fraction of a degree. Low-THz DBS was experimentally tested under controlled laboratory conditions, not only to accurately localised target objects in Cartesian space but also to provide unique object imaging at low-THz frequencies with wide azimuthal beamwidth antennas. It was shown that a stationary (i.e. non-scanned) wide beam antenna mounted on a moving platform can deliver imagery at least comparable to that produced by physical beamforming, be that steering arrays or narrow beam scanning antennas as in the experimental case presented.

Recent evolution of automotive imaging radar and its information content

Abstract: Driven by the demand to develop self-driving cars, automotive radars evolve rapidly at this time. Current high end radars therefore have very little in common with the radars that were developed just a few years ago and are being built into today's car series. Anyway, since at least two decades such sensors are referred to as imaging radars, although the set of possible applications has changed significantly. This study displays the leap in the evolution of automotive imaging radars on the example of the Astyx HiRes radar and elucidates the additional information that becomes available for autonomous driving functions like freespace modelling and target classification. In particular, it will be shown that the image quality of a modern high end radar suffices to apply computer vision techniques that were reserved for optical or light detection and ranging (LIDAR) images until now.

Strong tracking SCKF based on adaptive CS model for manoeuvring aircraft tracking

Abstract: A novel tracking algorithm is proposed by the integration of the adaptive current statistical (CS) model and the modified strong tracking (ST) square-root cubature Kalman filter (SCKF) for the manoeuvring aircraft tracking problem. Firstly, the acceleration recursion equation and the acceleration mean input estimation are combined in order to realise the adaptive adjustment of the CS model. Then, the introduced position of the fading factor is relocated from the orthogonality principle and a new formula is put forward. Additionally, the strong manoeuver detection function is established to adjust the manoeuvring frequency of the CS model. The simulation results show that the proposed algorithm possesses better tracking accuracy than the multiple-fading-factor SCKF based on the CS model, the SCKF-ST filter based on the modified CS model and the interacting-multiple-model (IMM)-SCKF. Moreover, the proposed algorithm decreases the runtime by 40% compared with the IMM-SCKF.

Range–Doppler reconstruction for frequency agile and PRF-jittering radar

Abstract: Agility radar with the carrier frequency random hopping and the pulse repetition frequency (PRF) staggering from pulse to pulse achieves superior performance against the electromagnetic jamming. This novel scheme leads to the discontinuity of phase in a coherent processing interval, thus the fast Fourier transform-based method is no longer a valid way to estimate the velocity of a target. A novel sparse optimisation method based on compressed sensing is proposed for high-resolution range-Doppler reconstruction from random frequency hopping and PRF-jittering pulses. The performance of moving target detection of the proposed method for frequency agile and PRF-jittering radar is analysed by comparing it with parameters-fixed pulse Doppler radar. Both simulation and field experimental results demonstrate the effectiveness of the proposal.

Algebraic solution for three-dimensional TDOA/AOA localisation in multiple-input–multiple-output passive radar

Abstract: The problem of estimating the location of a single target from time difference of arrival (TDOA) and angle of arrival (AOA) measurements using multi-transmitter multi-receiver passive radar system with widely separated antennas is discussed. A closed-form two-step target position estimator is presented and analysed. Using the measured AOAs, the method is able to resolve the weakness of the TDOA-based methods in estimating the target height. Several weighted least-squares minimisations are employed by the method to produce a location estimate. A weighting matrix in each step is employed to provide a significant improvement in the performance of the algorithm. The Cramer-Rao lower bound (CRLB) for target localisation accuracy is also developed. The proposed estimator is analytically shown to reach the CRLB for Gaussian TDOA and AOA noises at moderate noise level. Simulation studies indicate that the proposed hybrid TDOA/AOA location scheme performs better than any of the other algorithms, especially in the z -direction.

Multifunction cognitive radar task scheduling using Monte Carlo tree search and policy networks

Abstract: 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 (BB 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.

Cost function design for the fully adaptive radar framework

Abstract: 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.

Adaptive particle filter based on Kullback–Leibler distance for underwater terrain aided navigation with multi-beam sonar

Abstract: Terrain aided navigation (TAN) is a promising approach to bound accumulated errors inherent to inertial navigation system by comparing terrain measurement with a reference map. Due to the non-linear nature of terrain, particle filters (PFs) are extensively studied for TAN because of its strong capability of dealing with non-linear problems. So far, most existing PFs for TAN manually select a fixed number of sampling particles during the entire filtering process. However, it can be highly inefficient, since the probability distribution of the state often varies drastically over time. To improve the efficiency, the Fox's adaptive PF based on Kullback-Leibler distance (KLD) is introduced for TAN, referred to as the normal KLD-PF here. In the normal KLD-PF, the number of sampling particles is adjusted online by KLD-sampling according to the size of state space. However, the normal KLD-PF has a fixed bin size, which easily causes the number of particles to surge at the early filtering stage. Thus, an improved KLD-PF with a variable bin size is proposed through limiting the total number of particles below certain value. Using a multi-beam sonar, simulation experiments with real underwater reference map demonstrate the efficiency of the proposed method.

Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures

Abstract: 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.

Photonic approach for on-board and ground radars in automotive applications

Abstract: While automotive radars are driving the development of high-performance technologies for remote sensing, pushing radiofrequency systems to higher frequencies, photonics is gradually changing the approach to micro- and millimetre wave RF generation and distribution. With its unique features, photonics can extend the potential of radars, in particular for ground-based traffic surveillance and on-board automotive applications, enhancing traffic safety and enabling the deployment of smart driver-less vehicles. In fact, microwave photonics offers unprecedented flexibility and stability, such as −113 dBc/Hz (at 100 kHz offset frequency) at 80 GHz, with the capability of generating an extremely broad range of carrier frequencies. Moreover, it can employ signals which span up to several GHz of bandwidth, thus allowing higher precision in target detection and discrimination. This study compares photonic and electronic technologies, and it demonstrates, through simulation results, the benefits of a multiple input, multiple output photonic radar when applied to automotive case-study scenarios.

Convolutional neural network for classifying space target of the same shape by using RCS time series

Abstract: Warhead and decoy classification is one of the most important and difficult technical problems in ballistic missile defence. The conventional methods extract features from the measured data and employ some classification algorithms. However, it is hard to extract all the information embedded in the raw data, and there might be contradictory features lowering the classification ability. A one-dimensional convolutional neural network structure named RCSnet was proposed to classify the warhead and decoy targets of the same shape in midcourse, which directly utilises the radar cross-section (RCS) time series. It was compared with 5 conventional classification algorithms which used 26 selected features on simulation dataset, and it outperformed them in both classification performance and predicting speed. Different training algorithms and networks of the RCSnet structure with different filter numbers were explored for better utilising the RCSnet.

Random phase code for automotive MIMO radars using combined frequency shift keying-linear FMCW waveform

Abstract: Automotive radar is a key component for self-driving cars and advanced driver assistant systems. The major requirements of recent automotive radars are high angular resolution and multiple target detection with the constraints of small size, low power, and low cost. With appropriate transmitter spacing, co-located multiple-input–multiple-output (MIMO) radar can emulate larger aperture arrays, producing the required high angular resolution. However, MIMO radar requires waveforms that are orthogonal in frequency, time, or code domain, and orthogonal waveforms developed for pulse radars are unsuitable for automotive frequency modulated continuous waveform (FMCW) radars. This study proposes a code division multiplexing method for automotive MIMO radars by selecting the combined frequency shift key-linear FMCW waveform. The authors show the performance through simulation and discuss constraints. The proposed method is suitable for automotive radars because not only can high angular resolution be achieved by a small number of arrays, but also multiple targets can be detected with the low sampling rate and computational power.

Fusion of radar sensing and wireless communications by embedding communication signals into the radar transmit waveform

Abstract: A joint radar-communication (JRC) system with both radar sensing and communication abilities is proposed to improve spectrum utilisation efficiency. The transmitter of the JRC consists of multiple antenna subarrays transmitting orthogonal waveforms. If the communication receivers are in radar sidelobe directions, the communication data symbols are embedded in the magnitude ratio as well as the phase shift between transmit waveform pairs. In the case of the communication receivers in radar main lobe direction, the data symbols are embedded in the phase shift between transmit waveform pairs only to preserve optimum radar target detection performance. Novel symbol mapping constellation scheme is designed to achieve a high data transmission rate while maintaining a relatively low symbol error rate. The simulation results show that with medium pulse repetition frequency radar waveforms used, the data transmission rate of the proposed JRC system is in the range of Mbits/s.

Off-grid effects mitigation exploiting knowledge of the clutter ridge for sparse recovery STAP

Abstract: In this study, a novel method for overcomplete dictionary construction for sparse recovery (SR) space-time adaptive processing (STAP) is proposed, which can effectively mitigate the off-grid effect. The proposed method utilises the clutter ridge, calculated via some prior knowledge such as radar system parameters and modern inertial navigation system information, to discretise the grids in the angle-Doppler plane. The authors also investigate the accuracy of clutter ridge in the presence of several typical non-ideal factors such as array errors, intrinsic clutter motion and aircraft crab. In particular, a parameter is introduced to adaptively adjust the grid interval to avoid the strong column coherence of the dictionary. The simulation results show that the signal-to-clutter-plus-noise ratio for the SR STAP with the proposed dictionary is significantly improved, compared with the one utilising conventional dictionary. Moreover, it is shown that the novel method not only works more robustly for the side-looking array, but also has better performance for other array orientations.

Deployment of multistatic radar system using multi-objective particle swarm optimisation

Abstract: The authors consider an optimisation problem of multistatic radar system (MSRS) deployment, which includes both the antenna placement and the transmitted power allocation. To improve the surveillance performance of MSRS that equipped with different detection methods, they mainly aim at two goals: (i) to improve the coverage ratio of a surveillance region; (ii) to get an even distribution of signal energy in the surveillance region. Through introducing two objective functions for the above two mentioned goals, respectively, they formulate a multi-objective optimisation problem. To overcome drawbacks caused by the significant difference between the values of these two objective functions, they propose a multi-objective particle swarm optimisation (MOPSO) algorithm with non-dominated relative crowding distance (MOPSO-NRCD). Specifically, through the non-dominative relationship comparison and normalisation of crowding distance, a novel method is proposed to select the global best solution. Moreover, a multi-swarm structure is applied to labour division of all particles. Comparing with the MOPSO with the traditional crowding distance, simulation results show that the MOPSO-NRCD can provide better candidate schemes, which are capable to satisfy more stringent performance requirements.

Radar target stimulation for automotive applications

Abstract: Automated driving is seen as one of the key technologies that influences and shapes our future mobility Modern advanced driver assistance systems (ADAS) play a vital role towards achieving this goal of automated driving Depending on the level of automation, the ADAS takes over the complete or partial control of the movement of the car Hence, it is mandatory that the system reacts reproducibly and safely in a wide range of possible situations Especially in complex and potentially dangerous traffic scenarios a test system with the ability to simulate realistic scenarios is required The authors present an implementation of a vehicle-in-the-loop (ViL) test system which accomplishes these goals in a defined environment Of the great plenty of sensors stimulated in this context, the radar sensor takes a special position due to its robust and comprehensive information perceiving capability Stimulating the automotive radar sensor in a ViL environment requires supporting the complex movements of the considered traffic scenarios For this task, a modular and highly scalable radar target stimulator is necessary, which is capable of stimulating multiple independent moving targets with realistic parameters The authors are discussing the underlying concepts of the suggested solution and are presenting its performance

Communication-embedded OFDM chirp waveform for delay-Doppler radar

Abstract: A fundamental problem in the fusion of wireless communications and radar is to design suitable waveforms that can be simultaneously used for information transmission and radar sensing. In this study, the authors extend the orthogonal frequency division multiplexing (OFDM) waveform by embedding communication codes to communication-embedded OFDM chirp waveforms for delay-Doppler radar applications. The waveforms are characterised by jointly utilising the advantages of classic phase modulation in communications and chirp frequency modulation in high-resolution radar. The integrated radar-communication system scheme is formulated, which can operate as wireless communications and delay-Doppler radar simultaneously. In a generic monostatic radar setting, a closed-form expression is derived to evaluate the ambiguity function of the designed waveforms, along with the average and variance analysis of the ambiguity function.

ISAR imaging and cross-range scaling of high-speed manoeuvring target with complex motion via compressive sensing

Abstract: For targets with extreme manoeuvres, inverse synthetic aperture radar (ISAR) imaging suffers from translational motion (TM), which is modelled as a one-dimensional (1D) phase error, and non-uniform rotational motion (RM), which is a multidimensional (MD) phase error that causes severe blurring in ISAR images. Full-aperture data collection is often unachievable because of interference with other radar activities, resulting in sparse-aperture (SA) data. In this study, the authors present a new framework for SA-ISAR imaging and cross-range scaling for manoeuvring targets based on compressive sensing. Instead of solving conventional optimisation problems constrained by a sparsity of signals, the proposed method utilises the sensing-matrix estimation technique for ISAR image reconstruction using parametric signal-model reconstruction. To do this, it looks for basis functions that best represent the behaviour of a sensing-dictionary matrix comprising the observed SA data. The sensing-matrix reconstruction is based on a modified orthogonal matching pursuit-type basis function-searching scheme. Finally, they generate a well-focused and scaled ISAR image from the recovered complete ISAR signal using the conventional Fourier transform after the removal of signals corresponding to 1D TM and MD RM phase errors. They utilise both simulated and real measured datasets to confirm the effectiveness of proposed method.