Search Results (96)

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

Background/Objectives: Unilateral hearing loss (UHL), particularly single-sided deafness (SSD), disrupts spatial hearing in children, leading to academic and social challenges. This study aimed to (1) compare azimuthal sound-localization accuracy and compensatory strategies between children with single-sided deafness (SSD) and their normal-hearing (NH) peers within a virtual reality environment, and (2) investigate sound-localization performance across various azimuths by contrasting left-SSD (L-SSD) and right-SSD (R-SSD) groups. Methods: A cohort of 44 participants (20 NH, 24 SSD) performed sound localization tasks in a 3D virtual environment. Unsigned azimuth error (UAE), unsigned elevation error (UEE), and head movement distance were analyzed across six azimuthal angles (−75° to 75°) at 0°elevation. Non-parametric statistics (Mann–Whitney U tests, Holm–Bonferroni correction) compared performance between NH and SSD groups and within SSD subgroups (L-SSD vs. R-SSD). Results: The SSD group exhibited significantly higher UAE (mean: 22.4° vs. 3.69°, p < 0.0001), UEE (mean: 5.95° vs. 3.77°, p < 0.0001) and head movement distance (mean: 0.35° vs. 0.12°, p < 0.0001) compared with NH peers, indicating persistent localization deficits and compensatory effort. Within the SSD group, elevation performance was superior to azimuthal accuracy (mean UEE: 3.77° vs. mean UAE: 22.4°). Participants with R-SSD exhibited greater azimuthal errors at rightward angles (45°and 75°) and at −15°, as well as increased elevation errors at 75°. Hemifield-specific advantages were strongest at extreme lateral angles (75°). Conclusions: Children with SSD rely on insufficient compensatory head movements to resolve monaural spatial ambiguity in order to localize sounds. Localization deficits and the effort associated with localization task call for action in addressing these issues in dynamic environments such as the classroom. L-SSD subjects outperformed R-SSD peers, highlighting hemispheric specialization in spatial hearing and the need to study its neural basis to develop targeted rehabilitation and classroom support. The hemifield advantages described in this study call for further data collection and research on the topic. Full article

Azimuth ambiguity caused by spectral aliasing severely degrades the quality of Synthetic Aperture Radar (SAR) images. To suppress azimuth ambiguity while preserving image details as much as possible, this paper proposes an azimuth ambiguity suppression method for SAR based on time-frequency joint analysis. By exploiting the distribution differences of ambiguous signals across different sub-spectra, the method locates azimuth ambiguity in the time domain through multi-sub-spectrum change detection and fusion, followed by ambiguity suppression in the azimuth time-frequency domain. Experimental results demonstrate that the proposed method effectively suppresses azimuth ambiguity while maintaining superior performance in preserving genuine targets. Full article

Single-frequency global navigation satellite system/inertial navigation system (GNSS/INS) integration has wide application prospects in urban environments; however, correct integer ambiguity is the major challenge because of GNSS-blocked environments. In this paper, a sequential strategy of partial ambiguity resolution (PAR) of GNSS/INS for tightly coupled integration based on the robust posteriori residual, elevation angle, and azimuth in the body frame using INS aids is presented. First, the satellite is eliminated if the maximum absolute value of the robust posteriori residuals exceeds the set threshold. Otherwise, the satellites with a minimum elevation angle of less than or equal to 35° are successively eliminated. If satellites have elevation angles greater than 35°, these satellites are divided into different quadrants based on their azimuths calculated in body frame. The satellite with the maximum azimuth in each quadrant is selected as the candidate satellite, the candidate satellites are eliminated one by one, and the remaining satellites are used to calculate the position dilution of the precision (PDOP). Finally, the candidate satellite with the lowest PDOP is eliminated. Two sets of vehicle-borne data with a low-cost GNSS/INS integrated system are used to analyze the performance of the proposed algorithm. These experiments demonstrate that the proposed algorithm has the highest ambiguity fixing rates among all the designed PAR methods, and the fixing rates for these two sets of data are 99.40% and 98.74%, respectively. Additionally, among all the methods compared in this paper, the proposed algorithm demonstrates the best positioning performance in GNSS-blocked environments. Full article

To satisfy the requirement of the modern spaceborne synthetic aperture radar (SAR) system, SAR imaging mode design makes a trade-off between resolution and swath coverage by controlling radar antenna sweeping. Existing spaceborne SAR systems can perform earth observation missions well in various modes, but they still face challenges in data acquisition, storage, and transmission, especially for high-resolution wide-swath imaging. In the past few years, sparse signal processing technology has been introduced into SAR to try to solve these problems. In addition, sparse SAR imaging shows huge potential to improve system performance, such as offering wider swath coverage and higher recovered image quality. In this paper, the design scheme of spaceborne sparse SAR imaging modes is systematically introduced. In the mode design, we first design the beam positions of the sparse mode based on the corresponding traditional mode. Then, the essential parameters are calculated for system performance analysis based on radar equations. Finally, a sparse SAR imaging method based on mixed-norm regularization is introduced to obtain a high-quality image of the considered scene from the data collected by the designed sparse modes. Compared with the traditional mode, the designed sparse mode only requires us to obtain a wider swath coverage by reducing the pulse repetition rate (PRF), without changing the existing on-board system hardware. At the same time, the reduction in PRF can significantly reduce the system data rate. The problem of the azimuth ambiguity signal ratio (AASR) increasing from antenna beam scanning can be effectively solved by using the mixed-norm regularization-based sparse SAR imaging method. Full article

Spaceborne high-resolution wide-area SAR image formation processing faces critical challenges induced by orbital curvature, Earth rotation, and spherical ground surfaces. The Spherical Geometry Algorithm (SGA) offers an effective solution to these problems. However, the standard SGA is inherently limited to spotlight mode SAR data processing and cannot be directly extended to other operational modes. To overcome this constraint, this paper proposes an enhanced SGA framework tailored for sliding spotlight mode SAR data processing. Firstly, this paper presents a rigorous analysis of time–frequency relationship variations during the classical SGA processing under sliding spotlight mode, and gives the reasons why the classical SGA can not be directly applied to the data processing in sliding spotlight mode. Then, a modified SGA processing framework is proposed to address the signal sampling ambiguity problem faced by the SGA in processing sliding spotlight mode data. The improved algorithm avoids the sampling ambiguity problem during azimuthal resampling and azimuthal IFFT by introducing an instantaneous Doppler central frequency correction processing before azimuthal resampling and a suitable amount of oversampling during azimuthal resampling. Finally, the effectiveness of the algorithm is verified by measured real data processing. Full article

A scanning radar based on terahertz metamaterial phased array (TMPA) is a novel system for forward-looking imaging. In this paper, a waveform optimization method based on random hopping frequency (RHF) and amplitude modulation is proposed to improve the performance of TMPA scanning imaging radars. The RHF signal waveform is employed to reduce the sidelobes of the range ambiguity function for improving the measurement accuracy in range, while the amplitude modulation is applied to optimize the convolution matrix composed of samples of the antenna pattern, thereby enhancing the azimuth super-resolution. Interestingly, amplitude modulation for waveform design is based on the criterion of minimizing the residual between the real echo and the reconstructed echo with the limited transmission power, without any assumptions about the statistical distribution of target scattering. The effectiveness of the proposed waveform optimization method for enhancing the performance of a TMPA scanning imaging radar is verified through simulations and experiments. Full article

In ground-based bearing-only tracking of multiple maneuvering targets, there are difficulties in data association due to the reliance solely on azimuth information, making it challenging to distinguish and identify multiple targets. This problem is particularly pronounced when targets are close or overlapping, leading to disassociation or target loss. Moreover, bearing-only information struggles to accurately capture the dynamic changes in maneuvering targets, significantly affecting tracking accuracy. To address these issues, this paper proposes an Improved Maneuver Detection-Based Multiple Hypothesis Bearing-Only Target Tracking (IMD-MHRPCKF) algorithm. To begin with, the observation range is segmented into multiple sub-intervals through a distance parameterization technique, and within each sub-interval, a Cubature Kalman Filter (CKF) is applied. The Multiple Hypothesis Tracking (MHT) algorithm is then used for data association, solving the measurement ambiguity problem. To detect target maneuvers, the sliding window average of the innovation sequence is calculated. When a target maneuver is detected, the sub-filter parameters are reinitialized to ensure filter stability. In contrast, if no maneuver is detected, the filter parameters remain unchanged. Finally, simulations are used to compare this algorithm with various other algorithms. The results show that the proposed algorithm significantly improves system robustness, reduces tracking errors, and effectively tracks bearing-only multiple maneuvering targets. Full article

Sparse Synthetic Aperture Radar (SAR) imaging has garnered significant attention due to its ability to suppress azimuth ambiguity in under-sampled conditions, making it particularly useful for high-resolution wide-swath (HRWS) SAR systems. Traditional compressed sensing-based sparse SAR imaging algorithms are hindered by range–azimuth coupling induced by range cell migration (RCM), which results in high computational cost and limits their applicability to large-scale imaging scenarios. To address this challenge, the approximated observation-based sparse SAR imaging algorithm was developed, which decouples the range and azimuth directions, significantly reducing computational and temporal complexities to match the performance of conventional matched filtering algorithms. However, this method requires iterative processing and manual adjustment of parameters. In this paper, we propose a novel deep neural network-based sparse SAR imaging method, namely the Self-supervised Azimuth Ambiguity Suppression Network (SAAS-Net). Unlike traditional iterative algorithms, SAAS-Net directly learns the parameters from data, eliminating the need for manual tuning. This approach not only improves imaging quality but also accelerates the imaging process. Additionally, SAAS-Net retains the core advantage of sparse SAR imaging—azimuth ambiguity suppression in under-sampling conditions. The method introduces self-supervision to achieve orientation ambiguity suppression without altering the hardware architecture. Simulations and real data experiments using Gaofen-3 validate the effectiveness and superiority of the proposed approach. Full article

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) is a promising scheme for high-resolution wide-swath (HRWS) imaging. After echo separation processing, a MIMO-SAR system can provide many equivalent phase centers (EPCs) in azimuth. However, EPC duplication occurs for traditional monostatic systems with uniform antenna arrays, leading to system resource waste. Moreover, range ambiguity suppression is a necessary process for wide-swath SAR systems. In this paper, a novel MIMO-SAR echo separation and range ambiguity suppression processing framework is proposed for HRWS imaging. A set of transmission delays is introduced to the transmit channels to displace the repetitive EPCs. The transmission delays can also be used to flexibly control the performance of echo separation. A wide-null beamformer is employed to accomplish echo separation and ambiguity suppression simultaneously. The proposed framework is designed for real-time processing and therefore does not require frequency-domain operations. Finally, the proposed framework is verified through point target and distributed scene simulation experiments. Full article

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications. Full article

Airborne Frequency Diversity Array (FDA) radar operating at a high pulse repetition frequency encounters severe range-ambiguous clutter. The slight frequency increments introduced by the FDA result in angle and range coupling. Under these conditions, conventional space-time adaptive processing (STAP) often exhibits diminished performance or fails, complicating target detection. This paper proposes a method combining elevation oblique subspace projection with azimuth-Doppler STAP to suppress range-ambiguous clutter. The method compensates for the quadratic range dependence by analyzing the relationship between elevation frequency and range. It uses an elevation oblique subspace projection technique to construct an elevation adaptive filter, which separates clutter from ambiguous regions. Finally, residual clutter suppression is achieved through azimuth-Doppler STAP, enhancing target detection performance. Simulation results demonstrate that the proposed method effectively addresses range dependence and ambiguity issues, improving target detection performance in complex airborne FDA radar environments. Full article

Multichannel Synthetic Aperture Radar (MC-SAR) systems, such as Azimuth Multi-Phase Centre (AMPC) SAR, provide an effective solution for achieving high-resolution wide-swath (HRWS) imaging by reducing the pulse repetition frequency (PRF) to increase the swath width. However, in an Electronic Countermeasures (ECM) environment, the image quality of multichannel SAR systems can be significantly degraded by electromagnetic interference. Previous research into interference and counter-interference techniques has predominantly focused on single-channel SAR systems, with relatively few studies addressing the specific challenges faced by MC-SAR systems. This paper uses the classical spatial filtering technique of adaptive digital beamforming (DBF). Considering the Doppler ambiguity present in the echoes, two schemes—Interference Reconstruction And Cancellation (IRC) and Channel Grouping Nulling (CGN)—are designed to effectively eliminate suppressive interference. The IRC method eliminates the effects of interference without losing spatial degrees of freedom, ensuring effective suppression of Doppler ambiguity in subsequent processing. This method shows significant advantages under conditions of strong Doppler ambiguity and low jammer-to-signal ratio. Conversely, the CGN method mitigates the effect of interference on multichannel imaging at the expense of degrees of freedom redundant to Doppler ambiguity suppression. It shows remarkable interference suppression performance under weak-Doppler-ambiguity conditions, allowing for better image recovery. Simulations performed on point and distributed targets have validated that the proposed methods can effectively remove interfering signals and achieve high-resolution wide-swath (HRWS) SAR images. Full article

In order to address the problems of range ambiguity and azimuth ambiguity in the wide-swath imaging of synthetic aperture radar (SAR), this paper proposes a multi-beam SAR two-dimensional ambiguity suppression method based on azimuth phase coding (APC). The scheme employs an elevation simultaneous multi-beam transmission system with azimuth under-sampling, transmitting different APC waveforms to various range-ambiguous sub-regions. After receiving the echoes, the azimuth digital beamforming (DBF) is used to separate the APC waveform echoes with multi-order Doppler ambiguity, achieving azimuth reconstruction and range ambiguity suppression simultaneously. Finally, the elevation nulling DBF is used to further suppress range ambiguity and obtain the SAR wide-swath image. The superiority of this scheme is reflected in the following aspects: the azimuth DBF simultaneously suppresses azimuth and range ambiguity, the influence of height fluctuations on the ability to suppress range ambiguity is weakened, the use of elevation nulling DBF further enhances the level of range ambiguity suppression, and different range sub-regions can adopt different range resolutions and working modes. The feasibility of this scheme is verified through theoretical analysis and simulation. Full article

High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) imaging with azimuth multi-channel always suffers from channel phase and amplitude errors. Compared with spatial-invariant error, the range-dependent channel phase error is intractable due to its spatial dependency characteristic. This paper proposes a novel parameterized channel equalization approach to reconstruct the unambiguous SAR imagery. First, a linear model is established for the range-dependent channel phase error, and the sharpness of the reconstructed Doppler spectrum is used to measure the unambiguity quality. Furthermore, the intrinsic relationship between the channel phase errors and the sharpness is revealed, which allows us to estimate the optimal parameters by maximizing the sharpness of the reconstructed Doppler spectrum. Finally, the results from real-measured data show that the suggested method performs exceptionally for ambiguity suppression in HRWS SAR imaging. Full article