Due to the limited platform space of shipborne high-frequency surface wave radar (HFSWR), the aperture of the antenna array is reduced, thereby degrading the radar’s direction of arrival (DOA) estimation performance. Traditional aperture extension methods, based on single-domain information, limit the aperture extension ability and are not applicable to scenarios where non-target signals dominate. To address the above issues, this letter proposes an aperture extension method based on range-Doppler domain time-frequency information fusion (RDTFF), which utilizes multiple carrier frequencies and time division techniques. Compared with traditional methods, the proposed method achieves target echo separation and extraction through RD-domain processing, thereby extending the aperture and making the method suitable for shipborne HFSWR scenarios. Additionally, by fusing the time-frequency information of the target echo, a larger virtual aperture can be obtained, which further improves the DOA estimation performance of the array.

Marine oil spills pose a significant risk to the ecological balance and human health. It is crucial to promptly and accurately identify the type of oil spill to facilitate emergency response and inform scientific decisions. Remote sensing technology is at the forefront of current research on oil type identification. This article presented comprehensive research on the systematic identification of oil types. The optical and thermal infrared data were gathered for various typical oils to elucidate their optical and thermal characteristics (OTC). On this basis, we developed the oil-type OTC dual-driven identification model (OTC-DDIM). This model incorporates a sample expansion module [OTC-conditional generative adversarial network (CGAN)] to increase sample diversity, a characteristic extraction module (OTC-EM) to extract OTC, and an adaptive identification module to fuse and enhance OTC for identifying oil-spill types. Further research revealed the critical role of optical characteristic screening in eliminating redundant information interference and improving the identification accuracy and efficiency. Temperature, a dominant environmental factor (EF), played a key constraint on the generation of high-quality thermal infrared extension samples by OTC-CGAN. Under ideal oil-spill scenarios, the model demonstrated excellent identification capabilities, achieving an overall accuracy (OA) of 96.15%, with both Kappa and average$F_{1}$-score reaching 0.96. The method verification and application were conducted under simulated oil-spill scenarios. The experimental results demonstrated that OTC-DDIM could accurately and reliably identify oil-spill types using OTC, achieving accuracies of 91.71%, 0.92, and 0.90, respectively. In summary, this study could provide essential technical support for emergency responses to marine oil-spill accidents.

Marine oil spills and emulsions are highly detrimental to the ecological environment and human health. Optical and thermal infrared remote sensing technologies provide important approaches for marine oil-spill detection, but accurate cross-spatiotemporal remote sensing detection in complex scenarios is still challenging. In this article, optical and thermal infrared observation experiments on oil spills and emulsions were designed and conducted to analyze the optical and thermal response characteristics and the technical feasibility of remote sensing detection. Then, a detection model coupling the optical and thermal response characteristics is proposed to break through the bottleneck of oil-spill detection across time and space in complex scenes, which can enhance the diversity of samples by exploiting the 3-D spatial-spectral feature (SSF) generation adversarial expansion module constrained by the sun glint intensity index (SGII). Meanwhile, based on the thermal infrared super-resolution enhancement module guided by texture features, the thermal infrared spatial geometric features of oil spills are improved, and the model’s ability to resist cloud and fog interference is enhanced. Besides, under the guidance of the optical feature guidance (OFG) module, the SSF information can be extracted based on the SSF convolution deep belief network (SSF-CDBN) to realize the oil-spill remote sensing migration detection both temporally and spatially. The results show that the model can accurately detect oil spills and emulsion type, the overall accuracy (OA) and Kappa are larger than 84.46% and 0.758 in ideal scenarios, 80.11% and 0.745 in complex scenarios, which is expected to provide new technical support for oil-spill accidents.

Crude oil and its emulsions seriously threaten marine ecological environment and human health. Thermal infrared remote sensing technology is an important means of optical remote sensing marine oil-spill monitoring. In this paper, based on the UAV thermal infrared radiometer, a 24-hour continuous oil-spill brightness temperature image acquisition experiment was carried out, and oil-spill BTD polar coordinate thermal model was constructed to analyze the thermal infrared response characteristics of crude oil and emulsified oil. The results show that the thermal infrared response characteristics of oil-in-water (OW) emulsified oil and seawater are difficult to distinguish, which of crude oil and water-in-oil (WO) emulsion are similar. During 11:00~14:00, there is strong thermal infrared separability between oil and seawater, and the BTD values of crude oil and WO emulsified oil are both greater than 15. During 10:30~15:00, there is a strong positive correlation between BTD and oil film thickness, R2is greater than 0.9, so it is determined that the optimal time window for oil-spill thermal infrared monitoring is 11:00~14:00. The thermal infrared remote sensing monitoring based on oil-spill BTD model and optimal time window is expected to provide new technical and method support for marine oil-spill emergencies.

Abstract Recently, popular machine learning algorithms have successfully been applied to the direction of arrival (DOA) estimation. An implementation of determination of DOA estimation is presented based on deep neural networks (DNNs) to reduce the computational complexity of traditional superresolution DOA estimation methods. The classical DOA estimation algorithms have limitations due to unforeseen effects, such as array perturbations. Instead of computing an inverse mapping based on the incomplete forward mapping that relates the signal directions to the array outputs, the DOA problem is approached as a mapping, which can be approximated using a suitable DNN trained with input output pairs. The neural network architecture is based on a multilayer perception and a group of parallel DNNs to perform detection and DOA estimation, respectively. Simulation results are performed to investigate the effect of network parameters on estimation accuracy so that they can be roughly determined in the case of one signal scenario. Based on a set of simulations and experimental measurements, the performance of the optimum network is also assessed and compared to that of the classical DOA estimation methods for multiple signals. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also dramatically reduce the computational complexity and the memory space.

Marine oil-spill accidents seriously threaten both the marine ecological environment and human health. It is important to accurately identify the type of oil spills and detect the thickness of oil films on the sea surface to obtain the amount of oil spill for on-site emergency responses. Remote sensing is an important method for marine oil-spill detection and identification. In this study, thermal infrared remote sensing images of oil spills were obtained using thermal infrared imaging camera and UA V, and a marine oil-spill thermal infrared detection SVC model was proposed to conduct oil-spill detection research. The results of the land-based experiment show that there is a strong correlation between the thick oil film with different thicknesses and the surface temperature, and the R2 is larger than 0.92. The results of the UA V detection experiment show that the OA is larger than 76.84%, and the Kappa coefficient is larger than 0.740, which show the potential of UA V thermal infrared remote sensing for oil-spill detection.

This paper proposes a covariance matrix completion based algorithm for underdetermined direction of arrival (DOA) estimation in the presence of unknown nonuniform noise using nested sparse circular array (NSCA) with only N sensors. The proposed algorithm provides a systematic procedure to complete a covariance matrix for a virtual uniform circular array (UCA) with M sensors (M > N). Compared with the covariance matrix of the NSCA, the completed covariance matrix is capable of increasing degrees of freedom (DOFs), and is noise-free to mitigate the effect of nonuniform noise. The elements of the completed covariance matrix are from three steps: (1) elements from covariance matrix of the NSCA; (2) elements generated from the properties of the UCA; (3) elements produced from output of oblique projection operator based on initial DOAs. Then compressive sensing (CS) method is used to estimate DOAs based on the completed covariance matrix for better performance. The computational complexity of the proposed algorithm, and CRB are also given. Simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art methods in estimation accuracy.

Frequency/direction-of-arrival (DOA) estimation via grid searching or sparse representation is time-consuming in 2D cases. Few dimension-reduction methods exist for arbitrary temporal/spatial sampling. In this letter, we propose the Continuous Approximation based Dimension-Reduced Estimation (CADRE) framework to address this issue. By the linear approximation of vectors from a continuous space using only a few bases, dimension reduction is achieved. For some complicated manifolds or realistic scenarios with only a discrete set of steering vectors available, a discrete simplification is also effective. For parameter estimation, parameter-space multiple signal classification and a group-sparse based algorithm are proposed. Simulations verify the superiority of the proposed estimators in both speed and accuracy.

The sensor geometry design problem of time difference of arrival (TDOA) localisation systems based on Cramer–Rao bound is studied. Sensor placement constraints are considered, which means the available placement area is limited. This makes sensor geometry design a sensor selection problem. In two‐dimensional (2D) TDOA localisation or 3D TDOA localisation on the earth surface, sensor selection can be implemented through solving a fractional integer programming problem. However, traditional fractional integer programming methods are either suboptimal or too time consuming. For this reason, a new method named path‐varying sphere decoding is proposed in two steps. In step one, the programming problem is relaxed into two sphere decoding (SD) subproblems. Solving these subproblems leads to the optimal solution, and the required computational complexity is much less than those of traditional optimal methods. In step two, the structure of the cost function is explored. This makes it possible to calculate the path‐varying upper‐bound of a quadratic function. Thus the quadratic function constraint used in one SD subproblem becomes tighter and the calculation speed is enhanced. Theory analyses and simulation results show that the proposed method is not only optimal but also much faster than traditional optimal methods when solving large‐scale programming problems.

In this letter, we consider the coherent direction-ofarrival estimation problem in impulsive noise. An ℓp-norm-based variant of the classical relaxation technique is proposed to tackle this problem. The proposed method successively minimizes the cost function along block coordinate directions. Specifically, at each iteration, only one block of the signal component is updated, while the remaining blocks are kept fixed. Then, instead of solving each block exactly, the proposed method optimizes the parameters in the block iteratively by solving a surrogate function that upper bounds the ℓp-norm. Numerical results show that the proposed scheme offers substantial performance improvement over the state-of-theart algorithms.

In this letter, we consider the problem of underdetermined direction-of-arrival estimation of wideband signals using nested arrays in the framework of sparse signal recovery. The problem is recast into recovering multiple nonnegative sparse signals, which share the same sparse support but correspond to dictionaries of different frequency bins. By constructing parameterized dictionaries and exploiting the joint sparsity structure, we develop an iterative minimization method that can jointly estimate the sparse signal and refine the parameterized dictionaries. Numerical results are provided to verify the practical effectiveness of the proposed scheme.

This study focuses on the design methods for uniform linear array (ULA) based directional antenna arrays by optimising the radiation characteristics of elements. To improve the performance of direction‐of‐arrival (DOA) estimation in a predetermined objective spatial sector which includes all the potential directions of incidence, Cramér–Rao bound based optimisation models are established by utilising the least squares fitting technique. Besides, a modified simulated annealing (SA) algorithm with the iteration of parameters is proposed, aiming to solve the optimisation problems when the classic SA is invalid. Compared with the corresponding conventional ULA, an optimised array can obtain higher accuracy of DOA estimation in the objective spatial sector with little fluctuation. Additionally, the optimised design of radiation characteristics can also suppress the ambiguities, and remains effective for the arrays with different aperture. Simulation results verify the effectiveness of the proposed methods and the superiority of the optimised arrays.

Root‐MUSIC can be applied to uniform circular array (UCA) to achieve computationally efficient direction of arrival (DOA) estimation via beamspace transformation (BT). When the number of sensors of a UCA is small, the residual errors introduced by the BT will have significant values, resulting in performance degradation for DOA estimation. To solve this problem, an algorithm that enables the modification of the beamspace sample covariance matrix (BSCM) by considering the residual components is proposed. First, the residual components in the BSCM are calculated based on the initial DOAs estimated by the UCA root‐MUSIC‐based methods. Then the BSCM is modified by removing the undesirable terms. Finally, better DOA estimation performance is obtained by using the revised BSCM. The computational complexity and estimation error are derived. The significant advantages of the proposed algorithm are demonstrated by the simulation results.

The detection of vessel targets could be effectively resolved in a high-frequency surface wave radar (HFSWR). However, signals reflected from vessels are concealed by sea clutter in the Doppler spectrum, where such detections are performed. Consequently, differences between these features in the Doppler domain cannot be readily observed, which greatly increases the difficulty in detecting vessel targets. In this letter, in situ sea state information is utilized to facilitate the detection of targets within sea clutter. First, the sea clutter spectrum, which is absent of vessel, is constructed. Second, sensitive sea clutter features that are influenced by vessel targets are selected and analyzed. Third, anomalies in sensitive sea clutter features are detected by obtaining respective thresholds. Finally, vessel targets are identified by the synthesized anomaly detection. Experimental results demonstrate the effectiveness of the proposed method, and the vessels detected using the HFSWR are further verified using synchronous automatic identification system information.

Without maintaining the internal constraint of the received data, conventional two-step passive localization methods are considered to be suboptimal. Following the thought of direct processing, a novel localization method based on the information of time-difference-of-arrival and angle-of-arrival (AOA) is proposed in this letter. Similar to other direct localization algorithms, the proposed method does not require the procedure of data association. By taking advantages of the spatio-temporal processing, the proposed method can handle the underdetermined scenario (i.e., the number of emitters exceeds the number of sensors from all the stations) without the prior knowledge about the number of emitters. Compared with the localization algorithms based on AOA only, the proposed method has superior performance on the condition of low signal-to-noise ratio and large bandwidth.

This study deals with the direction‐of‐arrival (DOA) estimation problem for hybrid completely polarised (CP) and partially polarised (PP) source signals using arbitrary polarimetric antenna arrays. An oblique projection‐based polarisation insensitive direction estimation (OPPIDE) algorithm is proposed by exploiting the spatial‐sparsity property of the sources. The OP technique is utilised to provide spatial filters, which are insensitive to the state of polarisation of signals, so that the potential source signals in the spatial domain can be separated later. The DOA estimation is finally implemented by identifying the sources’ spatially sparse structure with the separated signals. Theoretical analysis indicates that the OPPIDE is applicable to any hybrid CP and PP signals, and is independent of special polarimetric array configurations. The effectiveness and superiority of the proposed OPPIDE are substantiated through making performance comparison with the present counterpart algorithms.

In this paper, a noncircular deterministic maximum likelihood (NC-DML) estimator for direction-of-arrival estimation of strictly NC signals is devised. Unlike the conventional DML solution for arbitrary signals, the NC-DML exploits the NC properties of the sources by reconstructing the parameter set, significantly decreasing the number of parameters to be considered. For computing the NC-DML, we present a novel NC alternating projection (NC-AP) approach. The NC-AP solution is carried out based on an augmented virtual array structure. Moreover, it also takes the impact of the initial phase shift of the NC signals into account. Simulation results are included to illustrate the superiority of the proposed method.

Filters with antenna arrays are widely used for interference suppression in the temporal domain, frequency domain, space domain and so on. Meanwhile, the interference mitigation performance of a single‐domain‐based filter depends on a noticeable difference between the target signal and interference in the corresponding domain. However, the interference cannot be efficiently suppressed by a single‐domain‐based filter when the difference in this individual domain is small. To solve this problem, a multi‐domain collaborative oblique projection filter is proposed in this study. First, multi‐domain spaces are theoretically derived to distinguish the target signal and the interference, and then a multi‐domain oblique projection operator is provided to recover the original target and suppress the interference. Depending on the multi‐domain spaces, the filter has excellent performance when the difference is insignificant in each individual domain, whereas the performances of the single‐domain‐based filter and cascade filter degrade significantly. Finally, a space–polarisation–frequency domain collaborative filter based on oblique projection is given as an illustration. Performance analysis and simulation results are provided to illustrate the superiority of the proposed filter for interference mitigation.

To suppress the effects of ultra-wideband receiver caused by non-Gaussian noise and difference of channel profile, an adjustable observation window length equalisation receiver based on H∞ criterion is proposed. In contrast to the existing fixed observation window length (FOWL) equalisation receivers based on the minimum mean square error (MMSE) criterion, the proposed receiver is found on H∞ criterion and can adaptively adjust the observation window length according to the specific channel profile. The proposed receiver so designed is shown to outperform the FOWL equalisation receivers based on the conventional MMSE criterion in a non-Gaussian noise environment.

Efficient timing synchronization in an IDMA system must be obtained before signal detection and decoding. However, the research on timing synchronization in IDMA system has not been studied widely. In this paper a new timing acquisition scheme for asynchronous IDMA uplink based on sliding correlation is proposed and its performance in terms of the probability density function is developed. The simulation results show that the proposed scheme is effective even under high MAI conditions.

Abstract To suppress narrowband interference (NBI) in an ultra‐wideband (UWB) communications environment, a null phase‐shift polarization (NPSP) filter is proposed. The proposed NPSP filter is a combination of a linear polarization‐vector transformer (PVT), a conventional single notch polarization (SNP) filter, and an amplitude and phase compensator (APC). The NBI, which has polarized states different from those of the UWB, can be suppressed completely and the UWB signal can be recovered without distortion if the polarized states can be estimated exactly. Analytical and simulation results indicate that the signal‐to‐interference ratio (SIR) can be improved effectively after NPSP filtering. The proposed NPSP filter can be implemented in a time‐hopping spread spectrum (TH‐SS) or a direct‐sequence spread spectrum (DS‐SS) UWB system. Copyright © 2007 John Wiley & Sons, Ltd.

A new multiplexing scheme with high diversity gain for multicarrier systems, which is called multicarrier convolutional multiplexing (MCCM), is proposed. In this scheme the data symbols are spread onto several subcarriers by a convolutional spreader. Compared to the conventional OFDM (orthogonal frequency division multiplexing) system, the MCCM system using well designed spreading codes can achieve diversity with order equal to the length of the codes in frequency selective fading channels. A simple free distance searching algorithm is utilized to search the codes with the optimized performance. The best codes of length four are presented and their performance is evaluated by numerical simulations.

A novel polarization receiver for suppressing narrowband interference (NBI) in ultra-wideband (UWB) communication systems is proposed. A null phase-shift polarization (NPSP) filter, consisting of linear polarization vector transformation (PVT), conventional single notch polarization (SNP) filtering, and amplitude and phase compensation (APC), is utilized. The NBI, which has polarized states different from those of the UWB, can be suppressed completely and the UWB signal can be recovered without distortion. Analytical and simulation results indicate that the signal-to-interference ratio and BER performance can be improved effectively after NPSP filtering. The proposed receiver can be implemented in a time-hopping spread spectrum (TH-SS) or a direct-sequence spread spectrum (DS-SS) UWB system.