Improvements in target detection are typically achieved by increasing the signal-to-interference-plus-noise ratio (SINR) of the received signal. For a small radar platform with power and aperture constraints, increases in SINR must come through temporal integration. Two techniques are presented here for enhancing the integration of temporal signal energy to improve detection of weak, slow-moving targets over conventional methods. Both techniques involve modifications to pre-Doppler space-time adaptive processing (STAP), a reduced-dimension STAP method that is implementable in real-time on a power-constrained platform. The two modifications include (i) development of an alternative pre-Doppler temporal weighting method, based on linear prediction, to increase the SINR of the pre-Doppler temporal output signal and (ii) development of an extended dwell temporal processing (EDTP) algorithm to integrate temporal signal energy collected over a long dwell. The EDTP algorithm is based on frequency domain analysis of the extended dwell time signal; the detection decision exploits the differences in the distribution of signal energy and noise energy over an extended dwell. EDTP algorithm performance is superior to traditional temporal processing methods in many practical scenarios: the EDTP algorithm detection rate is three times the detection rate of non-coherent integration for very low-velocity, low-SNR targets.

The authors consider the problem of covariance matrix estimation in heterogeneous environments for radar signal processing applications, where the secondary data exhibit heterogeneity in local power and share the same covariance structure. Without resorting to the complete statistical characterisation of the sample support, a class of estimators, each of them defined as the geometric barycenter of a set of basic covariances estimates (obtained from the available secondary data) with a specific distance employed, is proposed. The basic estimates are obtained by exploiting the characteristics of positive-definite matrix space and a condition number upper bound constraint. Finally, they evaluate the detection capabilities of an adaptive normalised matched filter with the proposed estimators in the presence of compound-Gaussian disturbance comparing it with existing alternatives.

Multiple-component model-based decompositions (MCSMs) of polarimetric synthetic aperture radar (PolSAR) data often exhibit overestimation of volume scattering power, which makes the oriented built-up areas show volume scattering rather than double-bounce scattering. Deorientation processing has been incorporated into the three- and four-component decomposition algorithms to overcome this limitation, where the coherency matrix is rotated to minimise the cross-polarised term. However, even with the deorientation, some urban areas with large orientation angles are still misjudged as vegetation. In this study, the performance of deorientation processing on the MCSM is discussed and then an improved polarimetric model-based decomposition method for PolSAR urban areas is proposed, which is inspired by Sato's decomposition method. Since the cross-polarised HV scattering component is caused not only by vegetation but also by oriented buildings, the volume scattering model of original multiple-component decomposition is extended to describe the HV scattering from these two different land covers. A general volume scattering model is adopted to describe the HV scattering from vegetated areas while the orientation angle of built-up areas is adaptively considered for modelling the HV scattering from oriented buildings. Experiments with the phased array type L-band synthetic aperture radar data demonstrate that the authors’ proposed method can get better decomposition results over urban areas than other methods.

This study reports the experimental validation of the first photonics-based dual-band radar system in a real maritime scenario, detailing the operation principles as well as the implementation of the demonstrator. The prototype, exploiting photonics for both the generation and the detection of the radar signals, is able to simultaneously handle two radar waveforms in the S-band and the X-band, ensuring also their reciprocal phase coherence. The proposed system implementation is fully detailed and its characterisation is reported, demonstrating a minimum detectable signal of −124 dBm. Finally, the system is tested in a real scenario, nearby the port of Livorno (Italy), where it successfully detects multiple naval targets, and tracks a cooperative vessel for 8 NM. The obtained results are compared with reference data from automatic identification system and global positioning system recorders, showing a perfect match in terms of range and velocity, both in the S- and X-band. Moreover a theoretical analysis of the power budget, considering also the radar cross section in the different frequency bands, is carried out and supported by the experimental observations. Beyond the actual implementation of the proposed system, the outcomes of these tests confirm that the photonics-based approach can lead new advances in radar architecture, through the development of frequency-agile photonic transceivers.

Operating mode identification of multi-function radars (MFRs) is critical in radar threat evaluation and electronic jamming decisions. The hidden Markov model (HMM) in conventional methods relies on the MFR prior knowledge and cannot accurately describe the MFR signal sequence with regularity. An identifying method is presented that is based on predictive state representation (PSR) models without the help of prior information, which is unlikely to be obtained. Simulations of synthetic MFR signals are used to demonstrate the applicability of the novel method and to summarise the training parameter setting principles, which are of practical significance in applications of the method. The PSR-based method is shown to be more effective than the HMM-based methods, in particular when applied to the radar signal sequence with unknown regularity. The method also contains a novel approach to reducing the dimension of the system-dynamics matrix, which enables the algorithm to perform well in conditions of heavy noise. Simulation results attest to the validity of the proposed method. With the proposed method, the operating modes of radars can be identified accurately, which is significant in supporting adaptive countermeasures against MFRs.

A new method for distributed multi-target tracking with multistatic radar systems is presented.The proposed method is based on using generalised covariance intersection (GCI) of multi-object densities for fusion of the posteriors within a multi-object Bayesian filtering scheme. The presented solution is particularly formulated for sensor fusion with posterior densities that are parameterised as generalised multi-Bernoulli (GMB) distributions which are the unlabelled version of VoCVo densities by discarding the labels. To obtain a closed-form solution for fusing GMB densities, the authors use an efficient approximation to the densities. The approximated density is another GMB density that preserves both the first-order moment (intensity or PHD) and the cardinality distribution of the original density. As such, it is called the second-order approximation of the GMB (SO-GMB) density. The resulting explicit expressions for the GCI fusion using SO-GMB approximations allow distributed sensor fusion, not only with VoCVo filters, but also with M-generalised labelled multi-Bernoulli and labelled multi-Bernoulli filters being in place as local filters in the multistatic radar system. In two challenging multi-target tracking scenarios, the tracking performance of the proposed method is shown to outperform the state of art.

The use of multi-carrier waveforms, such as Orthogonal Frequency Division Multiplexing (OFDM) as used in radio communication, for radar operations has gained strong interest recently. While the authors have completed research on using multi-carrier waveforms for simultaneous radar and communications operations, this study focuses on the recent research on using these waveforms for communications. Namely, they demonstrate how to employ OFDM to modulate Multi-Frequency Complementary Phase Coded sequences for wireless communications.

The problem of detecting a distributed target in the presence of compound-Gaussian noise with unknown covariance matrix is studied in this paper. Since no uniformly most powerful test exists for the problem at hand, two detectors based on the Rao and Wald tests are devised. Remarkably, the persymmetric structure of the covariance matrix is exploited in the design of the proposed detectors. Simulation results show that the proposed detectors outperform the traditional detectors, especially in training-limited scenarios.

A threat evaluation and jamming allocation (TEJA) system is proposed and implemented to optimise the jamming strategy of a platform. This TEJA system accounts for the different effects of jamming techniques on threats and radar modes, the interaction between jamming techniques and channels, the relative frequency and bandwidth used by threats, the uncertainty of the threat environment, and models the progression of threats through various radar modes from initial search to final guidance. Performance of the TEJA system is evaluated for a complex mission which considers a platform with two jammers penetrating an area with ten threats. The TEJA system is shown to be computationally efficient by using an exhaustive search to determine the optimum jamming strategy. The developed jamming strategy allows the platform to survive a mission despite its complexity.

An important concern in radar is understanding the consequence of waveform design constraints on the signal-to-interference-plus-noise ratio (SINR) performance. In this study, the authors develop performance models for the attendant drop in SINR as an integrated sidelobe (ISL) constraint is placed on the waveform. The performance models are developed using an eigenbasis subspace approach. The authors validate the performance models against measured data and conclude that the proposed approach provides reasonable performance modelling of the SINR as a function of the ISL constraint.

Multiple antenna receivers have been proposed for interference nulling in Global Navigation Satellite Systems (GNSS). An open-loop anti-jam approach is considered and an interference direction-of-arrival (DOA) estimation technique using fully augmentable non-uniform linear arrays is introduced. The DOAs of incoming jammers are estimated utilising the minimum output power method applied to the GNSS receiver coarray and through two different approaches of spectrum sensing and polynomial rooting. Subsequently, the strong interferers are cancelled utilising a subspace projection approach, whereas the 30 dB despreading gain of GNSS receivers protects the satellite signals against weak jammers. Supporting simulation results demonstrate that DOA estimation based on polynomial rooting outperforms spectrum sensing and validate the effectiveness of the proposed anti-jam strategy.

Simulation of the wakes excited by a submerged body in high-resolution synthetic aperture radar (SAR) imaging is presented. The hydrodynamic model consists of two sets of ocean dynamics closely relevant to SAR imaging, namely time evolution of the wake and wind waves, and orbital motion of Bragg waves riding on them. For the wake, the orbital motion is reconstructed through a least square-based solution from the known elevation map. Time domain backscattering from the electrically very large ocean scenario is computed by a quasi-stationary algorithm, in which a physical optics phase correction of the two-scale model is proposed to take account the Doppler effects caused by wave orbital motions. SAR images of the wake are simulated as a function of the size, speed and depth of the submerged body, in which a range of wake features are observed, such as azimuth displacement, velocity bunching and smearing. This work might provide insights into the feasibility of using a SAR sensor to detect the wakes of a submerged body.

An Low-angle target tracking problem is investigated via the refined maximum likelihood (RML) algorithm. The results of the RML algorithm reveal that increasing the operating frequency of radar does not always reduce the mean-squared error (MSE) of angle estimate and thus an appropriate selection of the operating frequency can improve the angle estimation accuracy. Here, a frequency-agile RML algorithm is proposed, which adaptively adjusts the operating frequency during target tracking to minimize the MSEs of angle estimate. Theoretical analysis and simulation are made to verify the effectiveness of the frequency-agile RML algorithm.

A novel target identification scheme that utilises high-resolution range profiles (HRRPs), which are obtained using bistatic (BS) radar (BS-HRRPs) and four-dimensional (4D) parameter sets (PSs), is described. These PSs uniquely determine the BS-HRRPs. In the proposed scheme, a new feature to cope with the scale variances of the BS-HRRPs is devised by using the RELAX algorithm and the resampling process. In addition, a proper classifier to effectively exploit both the BS-HRRPs and 4D PSs is designed to improve the BS identification capability.

This study addresses the detection of multiple objects in acoustic image. A fast two-dimensional (2D) subset censored constant false alarm rate (CFAR) method is proposed. The proposed method uses a multi-subset sliding window with reference cells and guard cells in 2D, and excludes the potential interference object by subset censoring. Specifically, the reference cells are equally divided into four subsets, the subset with the largest summation is censored, and the remaining subsets are used to calculate the local threshold. A fast algorithm based on integral image is employed for lowering computational load. It requires only 32 operations for a single test cell averagely, despite the total number of reference cells. The proposed method is assessed on real multi-beam acoustic image sequences, of which the background follows a distribution of Weibull. Experimental results are compared with the results acquired from the method including cell averaging CFAR and censored mean-level detector CFAR in 2D. The proposed method has been proved to be efficient and robust in multi-object environment, especially as multiple objects placed closely.

This study proposes a new estimation method, referred as the high-degree cubature Huber-based filtering (HCHF). The novel algorithm makes use of the fifth-degree cubature rule to numerically compute Gaussian-weighted integrals, which, with a set of non-linear state equations, are used to compute weighted means and covariance required for measurement update. The measurement update is designed based on the Huber technique which is a combined minimum of l ₁ and l ₂ norm estimation. Therefore, the HCHF could exhibit robustness when the measurement noise is highly non-Gaussian. The method is applied for tracking ballistic missile in presence of non-Gaussian noise in satellite line-of-sight measurements. The tracking performance is compared with that of extended Kalman filtering and conventional cubature Kalman filtering. The simulation results verified the effectiveness of the proposed non-linear filtering.

Knowledge of performance for different signal options in difficult environments is vital for improving modern satellite navigation systems. Currently, the accuracy of the different transmission signals in realistic multipath environments is still not known in current literature. In this study, different classical and advanced signals have been simulated using an urban multipath channel model standardised by the International Telecommunication Union. For the given multipath channel, signal and receiver effects have been investigated. The performance of GPS C/A and GALILEO open service signals has been compared. Additional simulation of wideband navigation signals lead to the uncovering of an important conflict between robustness and accuracy in terms of signal bandwidth. This conflict is signal inherent and not associated to a particular receiver. As a result of this finding, an improved satellite signal extension for robust urban navigation has been proposed. On the receiver side, pure line-of-sight (LOS) conditions have been identified in which a novel particle filter-based receiver shows a comparable performance as a classical delay locked loop (DLL). In a mixture of LOS and shadowing conditions the particle receiver clearly outperformed the classical DLL. For the classical DLL, critical scenarios have been identified that are often causing a loss of lock.

The accuracy of Kalman filtering (KF) relies on the quality of the observations as well as the dynamic model. However, the dynamic model is usually assumed to be invariant, which is not realistic in real global navigation satellite system (GNSS) navigation applications due to unexpected vehicular motion. A new algorithm is proposed to enhance the KF by using the least-squares support vector machine (LSSVM) technique. The LSSVM-enhanced KF (LSSVM-KF) adaptively estimates the dynamic modelling bias from historical information, and then uses the bias estimate to compensate the dynamic model. The algorithm treats the dynamic model bias as a time-variant ambiguous function which is trained with the LSSVM. A k-fold cross-validation method is developed to tune the training parameters of the LSSVM. With the corrected dynamic model, the KF is implemented to estimate the navigation parameters. To integrate LSSVM with KF, the unscented transformation is introduced to numerically compute the covariance of the LSSVM training. To verify the algorithm, simulation, semi-simulation and real GNSS vehicular experiments were carried out. The results show that the LSSVM-KF can adequately adapt to time-variant dynamics and achieve a reliable and accurate GNSS navigation solution.

In this study, the authors consider a multi-target tracking (MTT) problem in a cluttered environment. Due to the difficulty of the problem, the methods relying only on spatial information such as range, bearing and Doppler velocity can be unreliable. To overcome this, they additionally exploit the amplitude information, commonly provided by radar and sonar, for MTT. However, the usage of amplitude information is not straightforward because the signal-to-noise ratio (SNR) should be known in advance or estimated at the same time. To this end, they first propose a novel SNR estimation algorithm based on a maximum a posteriori approach, which helps the tracker to exploit the amplitude information effectively. Based on the estimated SNR, they then propose a complete framework for MTT, which is mainly composed of data association and track state update parts. They extensively evaluate the proposed system in a series of challenging scenarios, and the experimental results verify the effectiveness and robustness of the authors’ methods.