In high frequency (HF) hybrid sky-surface wave radar, the first-order sea clutter broadening is very severe under the influence of ionosphere and bistatic angle, which affects the detection of slow ship target. This study analyses and simulates the broadening first-order sea clutter spectrum based on a modified first-order ocean surface radar cross-section (RCS) model using wide beam. The authors first introduce the working principle, range equation and positioning principle based on the newly-developed integrated HF sky-surface experimental system; and derive an expression for the first-order Bragg frequency. Also presented is a modified RCS model of first-order sea clutter, in which the HF sea clutter spectrum distribution characteristics and broadening mechanism are fully considered. Finally, the proposed broadening first-order sea clutter spectrum model is examined by measured data. Simulation results indicate that the proposed first-order sea clutter spectrum model can well describe the characteristics of measured sea clutter.

The micro-Doppler (m-D) signature of a marine target is used for detection within sea clutter in this study. The marine target usually has translational and rotational motions, and the corresponding radar returns can be modelled as a quadratic frequency modulated signal over a period of time. To compensate the range walk and Doppler migration of the micromotion target simultaneously, a novel long-time coherent integration detection algorithm, that is, Radon-linear canonical ambiguity function (RLCAF), is proposed. It has been proved that the m-D signal can be well matched and accumulated in the RLCAF domain using the long-time instantaneous autocorrelation function and associated with the parameterisation of the linear canonical transform. Furthermore, to suppress sea clutter and improve signal-to-clutter ratio, the authors propose a RLCAF spectrum subtraction method using the different properties of RLCAF representations between the target and sea clutter. Finally, experiments with real radar dataset indicate that the proposed method can achieve better integration and detection performance of a marine target with micromotion in case of high sea state.

Target classification is a significant research direction in radar field. The range profile is a good target electromagnetic scattering characteristic for real-time target classification. This study proposes a novel method which combines support vector machine (SVM) and subspace methods to achieve complex target classification. The performances of SVM and three representative subspace methods are analysed using range profiles generated by graphical electromagnetic computing method. Experimental results demonstrate that SVM classifier has better robustness in sample variation than conventional classifiers. The auxiliary effects of three subspace methods on classification have respective preponderances in different aspects.

An efficient approach to achieve ground moving target indication (GMTI) for synthetic aperture radar (SAR) is to use the monopulse-SAR system. In this kind of SAR system, moving targets are detected over a so called, called monopulse ratio diagram (MRD) generated by a pixel-to-pixel comparison between the two SAR images acquired from the sum and difference channels. The sample derived from this diagram for the pixel under test (PUT), i.e. the value of monopulse ratio (MR), is employed as the test statistic. This paper examines the statistics of MR when complex Gaussian clutter-plus-noise background in considered. The conditional probability density function (pdf) of MR, given the occurrence that the amplitude of the sum signal is greater than a predetermined threshold, is analysed in detail. Especially, the conditional likelihood function of MR under the null hypothesis is given in a closed-form. Based on these results, an automatic constant false-alarm rate (CFAR) detector for moving target detection (MTD) is proposed and extended to a so called multi-MRD form to further improve the final detection performance. Simulations, as well as experimental results obtained from two groups of real dataset, are presented to examine the detection performance and validate the theoretical analysis.

In a frequency modulation continuous wave and inverse synthetic aperture radar (FMCW–ISAR) system, imaging a target beyond the maximum unambiguous range causes the ambiguity in the range domain. This ambiguity phenomenon greatly reduces resolution and degrades image quality, and limits the application of long-distance imaging of an FMCW–ISAR system. In this study, ambiguity correction method, based on mutual-coherence processing, is proposed for long-distance imaging in an FMCW–ISAR system. In the process, the singularity point of the beat signal caused by the ambiguity phenomenon is determined by the wavelet transform modulus maxima method. All-pole models are used to fit the beat signal subbands of different Doppler frequencies, and then adjust the models until they optimally match. The simulation results validate the effectiveness of the proposed correction method. This study shows the promising prospect of long-distance imaging using an FMCW–ISAR system.

This study considers the problem of detecting a human being in non-line of sight (NLOS) in an urban environment via multiple paths. It presents results obtained from real measurements carried out in an underground curved tunnel. The targets considered during the experiment, a sphere and a human being, were positioned in NLOS of the detection device. The different contributions visible in the measured range profiles were analysed and their origins explained. These measurements permit to show that the target contributions can be detected in NLOS, and that several multipaths can be retrieved. Measured ranges for these multipaths proved to be quite close to the theoretical ranges predicted by a simple propagation model.

Coherent wideband processing for the Galileo E5 AltBOC signal encounters a great challenge because of its modulation complexity, multi-peaked auto correlation function (ACF) and large bandwidth. In this study, a new tracking method called ‘dual binary phase-shift keying (BPSK) tracking’ (DBT), which is derived from its reception models and double estimator technique (DET) methodology, is specially designed for the Galileo E5 signal. While this method can achieve the full potential of the Galileo E5 signal in ranging performance, it features a robust wideband processing technique, backward compatibility with conventional BPSK signal tracking, and easy implementation in hardware. More specifically, the DBT method makes use of the coherence of the lower and upper bands of the Galileo E5 signal and decouples sub-carrier phase and carrier phase through coherently combining correlator outputs of the two bands, and then implements independent tracking for code, sub-carrier and carrier based on the DET methodology. Furthermore, thermal noise performances of this new method are given and verified by processing both simulated and real Galileo E5 signals.

Multilateration and automatic dependent surveillance – broadcast systems are used in air traffic control to detect, locate and identify cooperating aircraft using signals emitted by airborne transponders and received by dedicated ground stations. In areas with a high traffic density, these stations may receive simultaneously several superimposed signals. Present operational systems use only one receiving channel connected to a non-directional antenna. When the received replies are superimposed, that is, ‘garbled’, their detection and/or decoding are severely affected in nowadays equipment. The aim of this study is to transform the single channel problem into a multiple channels problem in order to solve it using specific knowledge about the Mode S signals. In fact, the multiple channels problem is a typical signals separation problem applied to Mode S mixture for which several algorithms already exist. The authors' algorithm, named projection algorithm (PA) single antenna, is based on the existing PA and can be easily implemented on existing receiving stations. The effectiveness of their method is demonstrated using real data collected from their experimental receiver.

For manoeuvring targets, long coherent processing interval observation results in a serious time-varying Doppler modulation that is difficult to achieve high quality three-dimensional (3D) interferometric inverse synthetic aperture radar (InISAR) images. In this study, a novel 3D InISAR imaging algorithm with limited pulses for manoeuvring targets is proposed. First, the authors formulate a joint sparsity-constraint optimisation to reconstruct super-resolution multi-channel inverse synthetic aperture radar images simultaneously. Then, high quality 3D images of the target can be achieved via the conventional interferometry technique. The main advantages of the proposed approach are: (i) by exploiting joint sparsity property of the InISAR multi-channel signals, it can effectively improve the recovery precision of scattering centres of the target; and (ii) it can preserve the cross-channel information, and hence, accurate interferometric phase can be obtained. Both simulated and real data experimental results demonstrate the effectiveness of the proposed algorithm.

Severe ionosphere scintillations have been known to affect the performance and measurement accuracy of Global Navigation Satellite System (GNSS) receivers. The scintillation in signal amplitude and phase reduces the number of available GNSS satellites by causing the loss of lock in GNSS receivers. Hence, the investigation of ionospheric scintillations is imperative for monitoring the activities of the atmosphere, ionosphere and space weather. Scintillations can be modelled as a function of scintillation indices like amplitude scintillation index (S4), phase scintillation index (σØ), C/N and elevation angle with respect to the time. In this study, the GNSS Ionospheric Scintillation and TEC monitor receiver located at the K L University, Vaddeswaram, India, sited in low latitudes, provided the data for the real-time analysis of ionospheric scintillations. This paper describes an ionospheric scintillation model (RTISM), which determines the automatic threshold for different scintillation signals using the Neyman Pearson detector. The results of the RTISM model include estimation, detection and mitigation of ionospheric scintillations using wavelet analysis, Hilbert–Huang transform and binary hypothesis test. The RTISM model has been tested for major scintillation events observed during the geomagnetic storms that occurred in the maximum solar activity periods of the 24th solar cycle (2013–2014).

Non-cooperative moving targets appear defocused within synthetic aperture radar (SAR) images and, in the case of ground targets, the blurring effect because of the uncompensated target motion decreases the radar's detection capabilities. Ground clutter, if sufficiently strong, may also obscure individual scatterers on moving targets resulting in a decreased ability to successfully classify the target. In this study, clutter suppression and inverse synthetic aperture radar (ISAR) imaging are combined to obtain high-resolution images of non-cooperative moving ground targets within SAR images. The clutter suppression technique proposed here is ISAR application oriented and is termed space-Doppler adaptive processing. Results obtained by processing a real dataset demonstrate the effectiveness of the proposed method.

This study demonstrates how the multiple parameters can be exactly obtained in sparse signal reconstruction framework using a cross-dipole array. Instead of using subspace-based methods, first direction of arrival (DOA) estimation of all sources is obtained, by solving a weighted ‘group lasso’ problem in second-order statistics domain. Then a truncated ℓ₁-function is utilised to approximate ℓ₀-norm, and an unbiased estimator is successively proposed to obtain the polarisation and power estimation. A statistical technique is introduced to select the regularisation parameter properly. Compared with the estimation of signal parameters via rotational invariance techniques-based algorithm, the proposed algorithm can provide improved resolution and estimation accuracy. Furthermore, the proposed algorithm can identify two sources with the same DOA successfully, provided that the polarisation parameters are different.

The detection of manoeuvring target for space-based radar is a challenging task because of its weak energy and manoeuvring motion. Prolonging the integration time is an effective method to increase signal energy, which can improve the target detection performance. However, with the increase of integration time, the radial velocity, radial acceleration and radial acceleration rate of manoeuvring target will induce linear range walk, quadratic-range curvature, cubic-range curvature and Doppler frequency migration of echo signal, which may degrade the performance of target detection. To solve this problem, a novel method is proposed that implements a parameter separation transform to isolate the acceleration. Then, the velocity and the acceleration rate can be obtained via one-dimensional search after the compensation of linear and cubic-range migration based on Hough transform and third-order Keystone transform. The kernel steps are as follows: (i) the acceleration is isolated by multiplying the range-compressed data in range frequency domain by its time-reversed data according to the symmetrical property of azimuth slow time; (ii) the cubic-range curvature is corrected by a third-order Keystone transform. The simulation results demonstrate the effectiveness of the proposed method in estimating radial velocity, radial acceleration and radial acceleration rate of a manoeuvring target, thereby the signal energy can be finely accumulated.