Novel two-dimensional (2D) and three-dimensional (3D) antenna array geometries for smart antenna application are introduced. Minimum mean square error beamforming algorithm, using these arrays, in presence of signal, noise and interferences is implemented. Beamforming approach is used for every type of array assuming uniform and log-normal distributions for the interference amplitudes. Equal volume cylindrical or prism type arrays with circular, hexagonal, triangular, square and star cross-sections with equal number of elements are considered. Novel geometries consisting of rotated cross-sections are studied as well. In each case, the relative signal-to-interference ratios (SIRs) are compared in these geometries, showing that the triangular and the star cross-section prisms have a better performance with respect to other cylindrical geometries. Monte-Carlo simulations show that the rotated geometries have better performance than the conventional 3D geometries both in terms of direction finding resolution and SIR values. Given the performance evaluation of these arrays in terms of direction finding resolution and beamforming precision, smart antenna realisation becomes plausible with these novel geometries for practical purposes.

This study considers a team of aerial robots towing the boundary of a capture net so that the net intercepts a fast unmanned aerial vehicle (UAV) with variable velocity. The robot team tows the net boundary so that the net captures the targeted UAV as fast as possible. The centre of the net is controlled so that the net centre moves while not rotating the line-of-sight connecting the pair (the net centre and the UAV). MATLAB simulations are used to verify the effectiveness of the authors’ approach.

Coherently combining dual identical radars is a promising technology that can obtain a 2³ times signal-to-noise ratio promotion over a single radar. The basic principle of dual-radar coherently combining is to adjust transmitting/receiving time and phase of each radar via the coherent parameters (CPs), with the purpose of achieving coherent-on-transmit and -receive. However, this technology has not been researched systematically, especially in the target moving scenario. In this paper, a signal processing paradigm for dual-radar coherent combination is proposed. Two stages exist in the proposed paradigm: coherence preparation stage and coherence maintenance stage. The former stage is considered as a means of bootstrapping to the latter stage, by offering the necessary coherent combining information (predicted transmit CPs and estimated synchronisation errors). And a great coherent gain is obtained in the latter stage, which allows dual-radar works in a fully coherent transmit and receive mode. In both stages, CPs estimation and prediction are the indispensable key steps, whose corresponding algorithms and constraints are developed, respectively. Given to this proposed paradigm, a dual-radar coherently combining example is implemented under a ballistic target tracking scenario, whose results verify the effectiveness of the proposed paradigm.

For global navigation satellite system (GNSS) receivers with dual-polarised sensitive arrays (DPSAs), two beamforming algorithms, which can mitigate interferences and preserve GNSS signals in the joint space–frequency–polarisation domain, are proposed. Firstly, the space–frequency–polarisation adaptive processing (SFPAP) architecture is put forward, where the received signal is divided into several frequency bins so that the narrowband array processing technique can be applied. After that, the received signal model corresponding to the SFPAP architecture is established, based on which the covariance matrix for each frequency bin is estimated. Furthermore, two beamforming algorithms based on the SFPAP architecture are proposed for GNSS receivers. Especially, the constraints corresponding to the two proposed SFPAP algorithms are designed to cancel interference and meanwhile form beams towards the desired GNSS satellites in the joint space–frequency–polarisation domain. Moreover, the computational complexity between the proposed SFPAP algorithms and the corresponding space–time–polarisation adaptive processing algorithms is compared. Finally, simulation results show that the proposed algorithms are effective in cancelling interferences and steering beams towards the desired GNSS satellites in the joint space–frequency–polarisation domain.

In this study, the authors consider a low-complexity robust adaptive beamforming problem in a collocated multiple-input multiple-output (MIMO) radar. This study is motivated by the fact that in practical applications, the conventional adaptive beamforming algorithm for MIMO radar requires a large computational complexity and suffers from a great performance degradation because of the finite number of training snapshots, the desired signal steering vector mismatch and the corruption of training data by the desired signal. Since the dimension of the virtual steering vector of the MIMO radar is relatively large, the proposed method can estimate the covariance matrix by using a low-complexity method to effectively improve the computational efficiency of the adaptive beamforming algorithm and the robustness of the beamformer against the covariance matrix uncertainty. Besides, based on the estimated covariance matrix, the proposed method can also correct the desired signal steering vector mismatch to efficiently prevent the desired signal cancellation phenomenon. Simulation results demonstrate that the performance of the proposed method is always close to that of the optimal processing in a wide range of signal-to-noise ratio or of the number of training snapshots.

Fault subsystems are entirely isolated on the fault-detected epoch in traditional fault-tolerant integrated navigation systems. However, this strategy ignores the asymptotic change and component differences of the soft failure influence. This study proposes a resilient fusion navigation algorithm based on the failure influence level evaluation. The failure influence mapped on the local estimation components is modelled. Then, the influential level of soft failure on the locally estimated states is evaluated. The failure influence levels are used for asymptotic resilience tuning in the global fusion. The simulation results indicate that the fault-tolerant performance under soft failure is improved significantly when using the proposed resilient fusion navigation algorithm based on the failure influence level evaluation.

Recently, radar target detection based on fractal theory has received great attention. Especially in the sea clutter background, fractal modelling, fractal dimension, and multifractal spectrum are widely used in radar signal processing. Here, a novel target detection method based on cross-correlation singularity power spectrum (CSPS) is proposed. Firstly, the CSPS theory and algorithm is derived based on SPS; secondly, based on the SPS and CSPS analysis of radar sea clutter, radar target detection parameters are designed, and the detection flow and algorithm based on the CSPS method is presented in detail. The proposed methodology is tested on sea clutter, both with and without targets, from the Ice Multi-parameter Imaging X-Band radar data set. The simulation results indicate that the proposed method performs better than the conventional multifractal-based methods and traditional CFAR methods, and the detection probability of detecting low-observable targets within sea clutters is close to 100%.

The authors propose a distributed state estimation algorithm based on optimal track-to-track fusion for local posteriors in terms of Gaussian mixtures. The track-to-track fusion system is implemented with both parallel and sequential structure based on generalised covariance intersection rule. They obtain the optimal fusion coefficients in a computationally efficient manner via Monte Carlo importance sampling method. The Dirac mixture approximation method is proposed for the computation of arbitrary power of a Gaussian mixture density. The resulting Gaussian mixture fusion rule is analytical and applicable to the multi-sensor case. Numerical examples are presented to demonstrate the performance advantages of the proposed method in comparison with existing track-to-track fusion algorithms.

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.

In this study, to reduce the performance degradation due to iron-clutter, the authors propose a suppression method for stationary clutter under iron-structures for an automotive radar using a frequency-modulation continuous-wave (FMCW) radar. The presence of a large number of iron-structures such as iron-bridges, soundproof walls, and iron-tunnels creates a harsh environment that deteriorates the associated radar detection performance owing to clutter. To mitigate the adverse effects of clutter, they propose a clutter suppression scheme using up- and down-chirp signals based on an analysis of signal characteristics. In particular, it is shown that the stationary clutters can be suppressed by making that the phases of the up-chirp and down-chirp are similar. To experimentally verify the proposed method, 77 GHz FMCW radar data for adaptive cruise control was acquired along a road on which several iron-structures were located. The experimental results show that using the proposed clutter suppression scheme, the frequency of the moving target was detected well using the proposed clutter suppression scheme in an iron-clutter-rich environment. Through the application of the proposed clutter suppression scheme, stable target detection performance of the radar can be obtained.

Current light detection and ranging (LIDAR) based odometry solutions that are used for spacecraft relative navigation suffer from quite a few deficiencies. These include an off-line training requirement and relying on the iterative closest point (ICP) that does not guarantee a globally optimum solution. To encounter this, the authors suggest a robust architecture that overcomes the problems of current proposals by combining the concepts of 3D local feature matching with an adaptive variant of the H∞ recursive filtering process. Trials on real laser scans of an EnviSat model demonstrate that the proposed architecture affords at least one order of magnitude better accuracy compared to ICP.

With the rapid development of the integrated circuit manufacturing technology and the radar technology, the demand for miniaturisation, low power consumption, and real-time performance of synthetic aperture radar (SAR) imaging system is becoming higher and higher. By designing reconfigurable IP cores and configuring parameters through CPU, a reconfigurable missile-borne SAR imaging SoC chip based on IP reuse is designed to meet this urgent demand. Using 0.13μm CMOS process, the SoC chip has been taped out and verified successfully. Compared with the traditional ‘DSP + FPGA’ platform, this chip can ensure the real-time performance of the missile-borne SAR imaging system and has more advantages in scale and power consumption.

The authors consider the design of multiple near-orthogonal transmit waveforms for a high-frequency surface wave radar (HFSWR) system operating in a congested spectral environment where the radar must adhere to strict regulations on the interference it can cause to on-going communication links. The HFSWR application necessitates multiple waveforms to increase the overall unambiguous radar range. Ideally, the waveforms would be constant amplitude, have low autocorrelation sidelobes, and low pulse-to-pulse cross-correlations, all while meeting the imposed spectral constraints; however, it is impossible to know a priori that such waveforms exist. They propose an algorithm based on alternating successive convex approximations and projections to design waveforms with low pulse-to-pulse cross-correlation which meet strict spectral and autocorrelation sidelobe constraints while minimising the amplitude modulation. In the simulation, the proposed algorithm is found to converge rapidly and when compared to similar methods from the recent literature, the proposed algorithm is found to generate waveforms with significantly lower peak-to-average power ratios and better pulse compression properties.

For an airborne-phased array radar system, space-time adaptive processing (STAP) is supposed to be a crucial technique for improving its target detection performance in a strong clutter background. Here, the authors consider the extreme heterogeneous case, i.e. the number of available training samples is limited to one. With only one training sample, the sparse recovery (SR) technique is utilized to estimate the clutter patches in the angular-Doppler plane, i.e. the pair of Doppler frequency and spatial frequency corresponding to the grid in angular-Doppler plane is determined. Based on the observation that some clutter components may be missed for one secondary data sample, the clutter covariance matrix (CCM) is estimated through reconstructing some missed clutter components by utilising the symmetric property of clutter return. From the simulation results, the proposed approach can achieve great performance of clutter suppression with only one training sample compared with conventional SR-based STAP algorithms.

This study handles a situation in which an underwater robot approaches a moving emitter while measuring the sound made by the emitter. Since radio waves are easily dissipated in underwater environments, sound is the main energy transferred from the emitter to the underwater robot. In order to chase the emitter while measuring the emitter's sound, it is desirable that the robot can measure the sound energy made by the emitter as much as possible. In this study, the robot's path is designed to maximise the emitter's sound measured by the robot as well as to minimise the time spent until encountering the emitter. The effectiveness of the proposed path plan approach is demonstrated utilising extensive simulations.

Both polarimetry and Doppler characteristics can be employed in remote sensing applications. Doppler information provides insight into the dynamic properties of radar targets, and polarimetry is associated with the intrinsic attributes of targets. Determining how to obtain accurate polarisation measurements from Doppler information has been a concern. For high-speed targets, the relativistic effect on radar wideband echoes may be notable. With the conventional matched filter, this scale effect may produce significant amplitude and phase errors between two columns of the polarisation scattering matrix (PSM), except for signal-to-noise ratio (SNR) loss. On the basis of the Taylor series expansion, the amplitude and phase expressions are derived in this study. Theoretical analysis shows that the amplitude and phase errors are related to the time–frequency allocation of the orthogonal waveforms and that the accuracy of the PSM is more susceptible to scale factors using frequency modulation waveforms than phase-coded waveforms. To suppress these amplitude and phase errors, a compensation method is proposed for orthogonal linear frequency modulation signals. Furthermore, the performances of the SNR loss and the velocity error effect are given. Finally, some numerical experimental results are provided to illustrate the effectiveness of the compensation.

In this study, a robust decentralised state estimation algorithm named decentralised Huber-based cubature filtering (DHCF) for formation flying spacecraft is proposed. In this algorithm, a decentralised architecture is designed, in which each deputy estimates its relative state with respect to the chief based on its own exteroceptive sensor measurement. The relative motion equation considering the non-spherical influence of the earth is derived. The relative measurements’ information contain not only the line-of-sight between the deputy and the chief, but also the ranges among the deputies, which improves the redundancy of the relative navigation task. A set of cubature points are selected using spherical-radial rule to map the probability distribution, which is more accurate than the linearisation of the extended Kalman filter (EKF). The non-Gaussian noise in formation is approximated by Gaussian mixture models. To deal with the non-Gaussian noise, the Huber technique is used in measurement update of each deputy and the extra measurement uncertainty caused by linearisation is compensated. Simulation results indicate that the proposed DHCF provides better performance in state estimation accuracy and robustness when compared to decentralised EKF and decentralised cubature Kalman filtering in the presence of non-Gaussian measurement noise.

To date, high-precision time synchronisation has been applied in many fields, such as high-precision indoor positioning, multistatic Synthetic Aperture Radar (SAR) and etc. This study focuses on the use of only global positioning system (GPS) L1 civilian signals to achieve regional nanosecond time synchronisation. GPS errors are analysed in this study to determine their effect on GPS timing. This study also examines the specific features of the error in fixed-position timing technology and related error reduction techniques to illustrate the feasibility of nanosecond high-precision time synchronisation with GPS L1 signals in a fixed-position timing mode. Then, the results of a specific GPS timing receiver working in a fixed-position mode and using GPS L1 signals are shown to indicate that the use of GPS L1 signals to achieve nanosecond-accuracy time synchronisation in a local area is feasible. Finally, this study also analyses the impacts of the imprecise position in a fixed-position timing mode.

The imaging performance and efficiency are two important issues for the multireceiver synthetic aperture sonar (SAS). Back projection (BP) algorithm is characterised by the high performance and low efficiency. In this study, the authors first recall the standard BP algorithm based on the interpolation for the multireceiver SAS. Then, two improved BP algorithms based on the range Fourier transformation (FT) are presented. Considering the fact that the time delay in the time domain can be carried out by the phase shifting in the frequency domain, an FT shifting based BP algorithm avoiding the interpolation error is presented. Although this method produces the focusing result with high performance, it is very time consuming. In order to improve the imaging efficiency without loss of imaging performance, the authors propose an oversampling-based BP algorithm, which is based on the fact that the zero-padding in the frequency domain is equivalent to the interpolation in the time domain. After that, the computation complexity of three BP algorithms is analysed in detail. Finally, simulations and real data are exploited to validate the presented methods.

The rapid development of intelligent navigation systems has speed development for its uses in military and civilian applications. On-board integrated navigation and multiple sensors are essential for real-time data update. Using multiple sensors may sever from the disconnection of the readings due to sensor(s) transient faults. So, the need for sensor(s) transient fault correction becomes an important action to improve performance. In this study, a novel proposed optimal pseudo sensor enhancement method (OPSEM) is introduced, investigated and implemented. The proposed OPSEM has multiple missions: (i) optimal arrangement for the sensors architecture, (ii) detecting, isolating, and compensating for the sensor(s) transient fault. The simulation results of the traditional and the proposed OPSEMs for multiple sensor architecture have been obtained, analysed and compared. Also, the proposed OPSEM has been applied to the proportional navigation guidance law to check for the improved performance. Finally, the integrated OPSEM navigation system has been realised and implemented. The proposed OPSEM practical system has been examined using unmanned aerial vehicle and fixed wing aircrafts to illustrate the improved operation of the proposed OPSEM system.

The authors demonstrated a vehicle detection and classification method based on long-term evolution (LTE) communication infrastructure-based environment-sensing instrument, termed as LTE-CommSense by the authors. This technology is a novel passive sensing system which focuses on the reference signals embedded in the sub-frames of LTE resource grid. It compares the received signal with the expected reference signal, extracts the evaluated channel state information (CSI) and analyses it to estimate the change in the environment. For vehicle detection and subsequent classification, authors’ setup is similar to a passive radar in forward scattering radar (FSR) mode. Instead of performing the radio frequency (RF) signals directly, the authors take advantage of the processing that happens in a LTE receiver user equipment (UE). The authors tap into the channel estimation and equalisation block and extract the CSI value. CSI value reflects the property of the communication channel between communication base station (eNodeB) and UE. The authors use CSI values for with vehicle and without vehicle case in outdoor open road environment. Being a receiver-only system, there is no need for any transmission and related regulations. Therefore, this system is low cost, power-efficient and difficult to detect. Also, most of its processing will be done by the existing LTE communication receiver (UE). Here, the authors establish authors’ claim by analysing field-collected data. Live LTE downlink (DL) signal is captured using modelled LTE UE using software defined radio (SDR). The detection analysis and classification performance show promising results and ascertain that LTE-CommSense is capable of detection and classification of different types of vehicles in outdoor road environment.

High-frequency skywave radar signals propagating through the ionosphere are often affected by travelling ionosphere disturbances (TIDs) which modulate the signal phase and frequency. This obscures slow moving targets. Conventionally, ionospheric contamination is detected by comparing Doppler spectra with different coherent integration times (CITs). However, this approach requires sometimes unacceptable observation time. In this study, the authors propose an alternative and simple, real-time method which utilises the high-order instantaneous moment (HIM) and high-order ambiguous function (HAF) of the echo signal. Their proposed algorithm uses symmetry of the peak structure of HIM and the stationarity of HAF to instantaneously detect contamination of echo signal. The effectiveness and instantaneity of the algorithm in detecting TIDs are demonstrated by comparing the traditional MultiCIT method. The processing time for both methods is of the same order of milliseconds, but the observation time for the new method is half of that for MultiCIT.