Search

We propose a novel two-stage training strategy with ambiguity boosting for the self-supervised learning of single view depths from stereo images. Our proposed two-stage learning strategy first aims to obtain a coarse depth prior by training an auto-encoder network for a stereoscopic view synthesis task. This prior knowledge is then boosted and used to self-supervise the model in the second stage of training in our novel ambiguity boosting loss. Our ambiguity boosting loss is a confidence-guided type of data augmentation loss that improves the accuracy and consistency of generated depth maps under several transformations of the single-image input. To show the benefits of the proposed two-stage training strategy with boosting, our two previous depth estimation (DE) networks, one with t-shaped adaptive kernels and the other with exponential disparity volumes, are extended with our new learning strategy, referred to as DBoosterNet-t and DBoosterNet-e, respectively. Our self-supervised DBoosterNets are competitive, and in some cases even better, compared to the most recent supervised SOTA methods, and are remarkably superior to the previous self-supervised methods for monocular DE on the challenging KITTI dataset. We present intensive experimental results, showing the efficacy of our method for the self-supervised monocular DE task. [ABSTRACT FROM AUTHOR]

Translation averaging is known to be more difficult than rotation averaging due to scale ambiguity, estimation sensitivity, and solution uncertainty. Existing approaches have exposed their limitations in terms of accuracy, robustness, simplicity, or efficiency. To tackle this tough problem, a simple yet effective translation averaging pipeline, termed as Incremental Translation Averaging (ITA), is proposed in this paper. It combines the advantages of high accuracy and robustness in incremental parameter estimation pipeline and the advantages of high simplicity and efficiency in global motion averaging approach. Unlike the traditional translation averaging methods which estimate all the absolute camera locations simultaneously and suffer from inaccuracy in parameter estimation and incompleteness in scene reconstruction, our ITA computes them novelly in an incremental way with higher accuracy and robustness. Thanks to the introduction of incremental parameter estimation thought into the translation averaging pipeline, 1) our ITA is robust to measurement outliers and accurate in parameter estimation; and 2) our ITA is simple and efficient because of its less dependency on complicated optimization, carefully-designed preprocessing, or additional information. Comprehensive evaluations on the 1DSfM dataset demonstrate the effectiveness of our ITA and its advantages over several state-of-the-art translation averaging approaches. [ABSTRACT FROM AUTHOR]

Text is a new way to guide human image manipulation. Albeit natural and flexible, text usually suffers from inaccuracy in spatial description, ambiguity in the description of appearance, and incompleteness. We in this paper address these issues. To overcome inaccuracy, we use structured information (e.g., poses) to help identify correct location to manipulate, by disentangling the control of appearance and spatial structure. Moreover, we learn the image-text shared space with derived disentanglement to improve accuracy and quality of manipulation, by separating relevant and irrelevant editing directions for the textual instructions in this space. Our model generates a series of manipulation results by moving source images in this space with different degrees of editing strength. Thus, to reduce the ambiguity in text, our model generates sequential output for manual selection. In addition, we propose an efficient pseudo-label loss to enhance editing performance when the text is incomplete. We evaluate our method on various datasets and show its precision and interactiveness to manipulate human images. [ABSTRACT FROM AUTHOR]

By using uniformly distributed phases for each bit, one can generate a waveform with an almost ideal aperiodic ambiguity function (thumbtack). Moreover,a set of such codes will potentially exhibit low cross-correlation, making them a family of codes suitable for multiple and diverse applications, ranging from 1) MIMO radars, 2) addressing the joint radar-communication problems, 3) mitigating the interference issues of coexistence of multiple radar systems operating in the same region, and 4) increasing the robustness of the system to jamming. This article presents the full analysis of the probability density function of the peak sidelobe level of such codes, as well as a full treatment of the ambiguity function to show mathematically what could be obtained by generating random phase codes. A simple and effective greedy algorithm for generating multiple codes with improved performance is devised increasing the practicality of this waveform family, and numerical results are provided to support the theoretical ones. [ABSTRACT FROM AUTHOR]

This article considers the detection and tracking of weak targets in multiple pulse repetition frequency (multi-PRF) radars using the track-before-detect (TBD) technique. By exploring the measurement independence among different PRFs, we decompose the joint multi-PRF and multiframe optimization problem into several lower dimensional maximizations, each of which corresponds to an intra-PRF multiframe processing. An efficient two-stage TBD procedure with a parallel structure is proposed for the multi-PRF radars. Specifically, in the first stage, a constraint inequality is derived analytically and used to decouple the measurement ambiguities from the nonlinear conversion relationship between polar and Cartesian coordinates. The intra-PRF multiframe integration can then be carried out concurrently using the ambiguous measurements and the target-like measurement plot sequences with different ambiguities are extracted for different PRFs. In the second stage, a covariance combination fusion-based inter-PRF joint disambiguation and estimation algorithm is proposed to solve the ambiguities of the plot sequences and output high-accuracy target tracks. Simulation experiments show that the proposed algorithm can provide a good detection performance and higher tracking accuracy with much lower complexity. [ABSTRACT FROM AUTHOR]

To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power flow (RD-LPF) model. It applies to both transmission and distribution systems and can achieve better robustness than the recent data-driven models. The key idea is to probabilistically constrain the worst-case errors through distributionally robust chance-constrained programming. It also allows guaranteeing the linearization accuracy for a chosen operating point. Comparison results with three recent LPF models demonstrate that the worst-case error of the RD-LPF model is significantly reduced over 2- to 70-fold while reducing the average error. A compromise between computational efficiency and accuracy can be achieved through different ambiguity sets and conversion methods. [ABSTRACT FROM AUTHOR]

In this paper we present methods for estimating shape from polarisation and shading information, i.e. photo-polarimetric shape estimation, under varying, but unknown, illumination, i.e. in an uncalibrated scenario. We propose several alternative photo-polarimetric constraints that depend upon the partial derivatives of the surface and show how to express them in a unified system of partial differential equations of which previous work is a special case. By careful combination and manipulation of the constraints, we show how to eliminate non-linearities such that a discrete version of the problem can be solved using linear least squares. We derive a minimal, combinatorial approach for two source illumination estimation which we use with RANSAC for robust light direction and intensity estimation. We also introduce a new method for estimating a polarisation image from multichannel data and provide methods for estimating albedo and refractive index. We evaluate lighting, shape, albedo and refractive index estimation methods on both synthetic and real-world data showing improvements over existing state-of-the-art. [ABSTRACT FROM AUTHOR]

In this paper, we propose an ambiguousness-aware state evolution (AASE) method which represents the uncertainty of the input sequence and evolves the subsequent skeletons to generate a reasonable full-length sequence for action prediction. Unlike most existing methods that enforce partial sequences with the labels of full-length videos and ignore the semantic information of the subsequent action, we develop an evolution method by predicting the instructional actions and generating the reasonable candidate subsequent actions, so that the ambiguity of the full sequence’s label supervising for the partial actions can be effectively alleviated. Our method generates the rational subsequent actions under the instructional action class to complement the partially observed action sequence. We design two criteria for a rational generation: 1) the instruction of subsequent action keeps the semantic consistency with the observed sequence; 2) the generation sequence is satisfied with the distribution of the sequence of real data. Moreover, we design an uncertainty module to decide the instructional action class for the generation network. AASE predicts instructional actions with uncertainty learning and evolves different instructional actions by generating the subsequent skeletons, which find the most probable action to represent the partially observed action by learning the way of perceiving the tendency of the ongoing action. We conduct experiments on seven widely used action datasets: NTU-60, NTU-120, UCF101, UT-Interaction, BIT, PKU-MMD and HMDB51, and our experimental results clearly demonstrate that our method achieves very competitive performance with state-of-the-art. [ABSTRACT FROM AUTHOR]

The filtered backprojection (FBP) algorithm is viewed as a preferred candidate for general bistatic synthetic aperture radar (BiSAR) imaging since it does not pose any restrictions on SAR configurations or flight paths. However, high-efficient autofocus methods such as phase gradient autofocus (PGA) or Mapdrift (MD) cannot be effectively integrated with the FBP algorithm due to the unknown properties of the BiSAR FBP imagery spectrum. In this article, a novel Fourier-based interpretation of the BiSAR FBP algorithm is presented. Based on the new viewpoint, spectral characteristics of the BiSAR FBP imagery in the wavenumber domain, including range spectral ambiguity, space-variant spectral support, and the structural 2-D phase error, are derived in detail. Using these characteristics, a computationally efficient 2-D autofocus approach is proposed. First, a preprocessing is performed to eliminate the range spectral ambiguity and to align the skewed spectrum support, which facilitates the following phase error estimation and correction. Then, an estimation of the 1-D azimuth phase error (APE) is applied by combining multiple estimation results from different subband data. Finally, the 2-D phase error is computed directly from the estimated APE by exploiting the derived analytical structure of the 2-D phase error, which is then applied to restore the BiSAR FBP image. The simulation results are presented to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article proposed an iterative pose estimation for the planar object to deal with the pose ambiguity in the Perspective n Points (PnP) problem. Specifically, by utilizing the unit virtual sphere, the PnP pose estimation problem can be performed as a minimization of the error function with three independent transitional parameters, referred to as the coupling position. Then, Levenberg–Marquardt (LM) optimization algorithm is applied to acquire the coupling position of the first local minimum. Furthermore, the coupling position is represented as the two Euler angles and the vector length, which are combined with the planar points to compute the second local minimum approximation as the initialization of the second LM algorithm. Consequently, once the global coupling position in the two local minimum is decided by the lower error, the orientation and position are directly decoupled by using the singular value decomposition. It is shown that the designed pose estimation is able to achieve prescribed performance for locating and distinguishing two local minimum, and meanwhile guarantee the superior computation behavior under the pose ambiguity for the planar object. Finally, numerical simulation and physical experiment are conducted to validate the effectiveness of the proposed method, compared with the state-of-the-art PnP methods. [ABSTRACT FROM AUTHOR]

In this article, we define an ambiguity function (AF) for the generalized wideband multiple-input multiple-output (MIMO) radar, wherein the AF combines the signal processing in the fractional Fourier domain with all the functions of conventional AFs. We name it as the fractional AF for the wideband MIMO radar. In order to obtain the AF, we start from introducing the fractional order that relates to fractional signal processing into the matched-filter exploited by the AF, based on which we then derive the explicit expression of the AF. We provide explanations and implications of the fractional AF, and also derive its simplifications in terms of the case of slow-moving targets in far field. Moreover, we derive corresponding properties for the fractional AF, and meanwhile, we conduct analysis on the computational complexity of the AF. On the basis of the AF definition as well as its simplifications, we establish relationships of our proposed fractional AF to conventional AF forms. We show that our defined fractional AF can serve as a generalized AF form since conventional AFs are special cases of it. Our simulations show that the proper selection of fractional-orders can enable low sidelobe levels of the fractional AF for wideband MIMO radar. [ABSTRACT FROM AUTHOR]

Ambiguity resolution (AR) is a fundamental problem for carrier phase based signal processing tasks to leverage the superhigh precision of wavelength-level range and velocity measurements. With the elaborately designed waveform and coordinated running of the space-based satellite system, the antenna-array observations of global navigation satellite system (GNSS) signals feature a phase measurement model. In this article, the AR of baseline estimation with GNSS carrier phase measurements only in the AR step is examined from an array signal processing perspective. The array-geometry-aided ambiguity resolution approach, coined as AGAR, is proposed for growing the baseline estimation provided by a search-free algorithm to the accuracy of the aperture level in an effective way. First, the single-epoch and search-free $2q$-order AR method is further investigated in size and statistical independence. Second, the conventional phase beampattern is defined to characterize the similarity of carrier phase measurement vector of signals from different directions. Third, a simple and effective ambiguity-lookup-table approach after the conventional phase beampattern is proposed which fulfills the goal of baseline growing to the array aperture level. Fourth, the identifiability, success rate, Cramér–Rao bound (CRB), and computation complexity are analyzed. As a result, the phase-based and complex-based processings are distinguished, resulting in an alternative analytical prediction of outlier probability that well approximates AR's success rate. The relationship between the success rate of AR and CRB is also clarified. Numerical simulations are carried out to verify the proposed analytical prediction and AR approach. The AR success rate increased from 10% to 93% at a relatively large measurement error of 0.05 cycle. [ABSTRACT FROM AUTHOR]

Ambiguity clutter for airborne radar systems is usually caused by a uniform pulse repetition interval (UPRI) waveform, which significantly degrades target detection and location performance. To address this issue, this article proposes an ambiguity clutter suppression method resorting to a pseudorandom pulse repetition interval (PrPRI) waveform. First, an airborne radar clutter model accounting for multiple range rings with PrPRI waveform is developed. Next, a nonuniform coherent processing framework is introduced to eliminate the clutter folded in the range and Doppler domain. In particular, the reasons for the enlargement of the area free of clutter corresponding to PrPRI mode are analyzed, as well as the maximum unambiguous Doppler frequency, maximum unambiguous range, and design principles for PRI are derived. Finally, numerical examples are designed to verify that the nonuniform coherent processing framework can enlarge the clutter-free area and achieve unambiguity target detection and location. [ABSTRACT FROM AUTHOR]

Due to the minimum antenna area constraints of synthetic aperture radar (SAR), high-resolution and wide-swath (HRWS) imaging is difficult to be achieved using classical modes, such as scan, spotlight, and so on. Range ambiguity is one of the main technical challenges limiting HRWS imaging in spaceborne SAR. Inspired by the cocktail party effect, an advanced scheme based on blind source separation (BSS) is put forward to suppress the range ambiguity of spaceborne SAR in this article. In the scheme, multiple subantennas are used to collect multiple echo datasets, which are given different patterns. In this way, the echo signal of the desired region and that of the ambiguous region are weighted using different coefficients. During postprocessing, a detailed flow with the BSS algorithm is proposed to separate the desired echoes from those datasets. To verify the scheme, point target and scene simulations based on GF-3 are carried out. The results indicate that the range ambiguity can be reduced by more than 15 dB using the proposed scheme, the processing flow is effective, and the scheme has a good performance without increasing the complexity of the system design under the current SAR system conditions. The scheme has the potential to be adopted in future spaceborne SAR missions. [ABSTRACT FROM AUTHOR]

Monocular 3D human pose estimation is challenging due to depth ambiguity. Convolution-based and Graph-Convolution-based methods have been developed to extract 3D information from temporal cues in motion videos. Typically, in the lifting-based methods, most recent works adopt the transformer to model the temporal relationship of 2D keypoint sequences. These previous works usually consider all the joints of a skeleton as a whole and then calculate the temporal attention based on the overall characteristics of the skeleton. Nevertheless, the human skeleton exhibits obvious part-wise inconsistency of motion patterns. It is therefore more appropriate to consider each part’s temporal behaviors separately. To deal with such part-wise motion inconsistency, we propose the Part Aware Temporal Attention module to extract the temporal dependency of each part separately. Moreover, the conventional attention mechanism in 3D pose estimation usually calculates attention within a short time interval. This indicates that only the correlation within the temporal context is considered. Whereas, we find that the part-wise structure of the human skeleton is repeating across different periods, actions, and even subjects. Therefore, the part-wise correlation at a distance can be utilized to further boost 3D pose estimation. We thus propose the Part Aware Dictionary Attention module to calculate the attention for the part-wise features of input in a dictionary, which contains multiple 3D skeletons sampled from the training set. Extensive experimental results show that our proposed part aware attention mechanism helps a transformer-based model to achieve state-of-the-art 3D pose estimation performance on two widely used public datasets. The codes and the trained models are released at https://github.com/thuxyz19/3D-HPE-PAA. [ABSTRACT FROM AUTHOR]

Human detection and pose estimation are essential for understanding human activities in images and videos. Mainstream multi-human pose estimation methods take a top-down approach, where human detection is first performed, then each detected person bounding box is fed into a pose estimation network. This top-down approach suffers from the early commitment of initial detections in crowded scenes and other cases with ambiguities or occlusions, leading to pose estimation failures. We propose the DetPoseNet, an end-to-end multi-human detection and pose estimation framework in a unified three-stage network. Our method consists of a coarse-pose proposal extraction sub-net, a coarse-pose based proposal filtering module, and a multi-scale pose refinement sub-net. The coarse-pose proposal sub-net extracts whole-body bounding boxes and body keypoint proposals in a single shot. The coarse-pose filtering step based on the person and keypoint proposals can effectively rule out unlikely detections, thus improving subsequent processing. The pose refinement sub-net performs cascaded pose estimation on each refined proposal region. Multi-scale supervision and multi-scale regression are used in the pose refinement sub-net to simultaneously strengthen context feature learning. Structure-aware loss and keypoint masking are applied to further improve the pose refinement robustness. Our framework is flexible to accept most existing top-down pose estimators as the role of the pose refinement sub-net in our approach. Experiments on COCO and OCHuman datasets demonstrate the effectiveness of the proposed framework. The proposed method is computationally efficient (5-6x speedup) in estimating multi-person poses with refined bounding boxes in sub-seconds. [ABSTRACT FROM AUTHOR]

Numerous deep-learning methods have been successfully applied to semantic segmentation (SS) and height estimation (EH) of remote-sensing imagery. It has also been proved that such a framework can be reusable for multiple tasks to reduce computational resource overhead. However, there are still some technical limitations due to the semantic inconsistency between 3-D and 2-D features and the strong interference of different objects with similar spectral–spatial properties. Previous works have sought to address these issues through hard (HPS) or soft parameter sharing (SPS) schemes. But due to unintentional integration, the specific information transmitted between multiple tasks is not clear or in a lot of redundancy. Furthermore, tuning the weights by hand between classification and regression loss functions is challenging. In this article, a novel multitask learning (MTL) method, termed associatively segmenting semantics and estimating height (ASSEH), is proposed to associatively segment semantics and estimate height from monocular remote-sensing imagery. First, considering semantic inconsistency across tasks, we design a task-specific distillation (TSD) module containing a set of task-specific gating units (TSGUs) for each task at the cost of fewer parameters. The module allows for task-specific features to be tailored from the backbone while allowing for task-shared features to be transmitted. Second, we leverage the proposed cross-task propagation (CTP) module to construct and diffuse the local pattern graphlets at the common positions across tasks. Such a high-order recursive method can bridge two tasks explicitly to effectively settle semantic ambiguities caused by similar spectral characteristics with less computational burden and memory requirements. Third, a dynamic weighted geometric mean (DWGeoMean) strategy is introduced to dynamically learn the weights of each task and be more robust to the magnitude of the loss function. Finally, the results of the ISPRS Vaihingen dataset and the urban semantic 3-D (US3D) dataset well demonstrate that our ASSEH achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Frequency diverse array (FDA), which employs a small frequency increment across its array elements to generate controllable degrees-of-freedom (DOFs) in range dimension, has attracted extensive attentions in recent years. In order to extract and exploit the DOFs in range and angle dimensions, FDA has been combined with multiple-input multiple-output (MIMO) technique, which shows sufficient performance improvement in clutter suppression in scenarios with range ambiguities. Therefore, clutter rank evaluation is an important issue for FDA-MIMO radar mounted on a moving platform. In this article, a clutter rank evaluation criterion is developed with respect to different transmit/receive array configurations and different number of range ambiguities. It is based on a subspace transformation matrix that decouples the clutter subspace and allows a direct determination of clutter rank. With the proposed clutter rank evaluation criterion, the maximum resolvable number of range ambiguities can be determined, which is bounded by the system's DOFs. Three cases according to different relationships between uniform array element spacings are studied. Then, the aforementioned clutter rank evaluation method is extended to nonuniform array configurations. Moreover, the robustness of the method is studied in the presence of gain-phase errors. Several simulation results are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Target localization with unambiguous range and angle estimation of the signal-of-interest is a key task of a radar system, especially for wide region surveillance. In this article, we develop a novel dual-mode array radar scheme, namely phased array and frequency diverse array cooperated radar, to provide unambiguous range and angle estimation. Our approach combines the advantages of the PA in high transmit gain and that of the FDA in resolving range ambiguity. In general, two kinds of cooperative parameter estimation algorithms are derived to determine the range and angle parameters jointly. The proposed algorithms can be interpreted as different combination methods of the PA and FDA, including the uniform combination and weighted combination based on weighted least squares, where the weight is designed with user tunable angle parameters. Particularly, the design criterion for the tunable parameter is given. At the analysis stage, the Cramér–Rao bounds are studied to verify the performance of parameter estimation. Extensive simulation results demonstrate the effectiveness of parameter estimation with the dual-mode array radar. [ABSTRACT FROM AUTHOR]

Solving the phase ambiguity problem is crucial to achieving a wide-range and high-precision measurement for the frequency-domain sampling (FDS)-based surface acoustic wave (SAW) delay-line sensor systems. This study proposes an improved phase estimation algorithm called dual-band phase estimation (DBPE) to solve the problem. By using DBPE, the SAW sensor system can obtain an extensive and alterable measuring range without further requirements for sensor design or transmitted signals. Thus, it can be widely used in various FDS-based SAW delay-line sensor systems. Monte Carlo simulations and temperature measuring experiments, based on a YZ-cut LiNbO3 SAW delay-line sensor and a switched frequency-stepped continuous wave (S-FSCW) reader, are performed to demonstrate the algorithm’s validity. The Monte Carlo simulations show that DBPE can effectively solve the phase ambiguity problem and has better performance than frequency estimation in measuring precision at a low signal-to-noise ratio (SNR). The temperature-sensing experiments show that DBPE has a good performance in measuring range and precision, serving as a phase ambiguity solver in the temperature sensor system. [ABSTRACT FROM AUTHOR]

Due to the constraint of minimum antenna area, 2-D ambiguity resolving is a challenging task in ship focusing of single-channel SAR mounted on high-speed maneuvering platforms. In order to accommodate the issues, a ship focusing and positioning algorithm based on 2-D ambiguity resolving is proposed. First, we analyze the constraint of minimum antenna area and the distribution of the 2-D ambiguity area. An optimal-PRF SAR concept is proposed, where the signal ambiguity is evenly distributed to the range and azimuth directions. In this concept, the timing sequence of the orthogonal phase-coded waveform is designed to make the target echoes in different regions orthogonal. Then, an orthogonal matching filter is used to suppress the signal energy in range ambiguity regions. Aiming at the target defocus and position shift caused by the Doppler ambiguity, we propose an azimuth ambiguity resolving method. The Doppler ambiguity number can be estimated by residual envelope inclination. Subsequently, the target can be relocated and accurately focused by the estimated Doppler parameters. After the operation of each target is completed, the focusing SAR image of the whole scene can be obtained. Finally, simulation results and real data processing are presented to validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

It is hard to tackle the near-range clutter when range ambiguity exists in the non-sidelooking moving target detection (MTD) application. The angle-Doppler spectra of the far- and near-range clutter cannot be aligned simultaneously in this case due to the range dependence, which would significantly degrade the performance of the space–time adaptive processing (STAP) technology. To address this problem, an elevation element multifrequency subpulse coding (EMFSPC) array framework is proposed in this article. For each pulse duration, the main-lobe beam of the proposed framework can automatically sweep the full space by coding the subpulses and the transmitting elements, which steers the subpulses to different directions. Besides, these multiple subpulses occupy different range-frequency bands, and thus corresponding bandpass filters could be carefully designed to extract the unambiguous signals. After that, one can align the clutter spectra centers and employ the full-dimensional or dimension-reduced STAP techniques to achieve clutter cancellation. Furthermore, the simulation experiments are conducted to demonstrate the validity of the proposed EMFSPC system in near-range strong clutter suppression. [ABSTRACT FROM AUTHOR]

The problem of range estimation based on noisy measurements with phase ambiguity can be solved using signals across multiple frequencies by existing algorithms based on either the Chinese Remainder Theorem or lattice theory. The performances of these algorithms are constrained by the trade-off between SNR of measurements and required estimation accuracy. In this paper, we firstly propose an algorithm to find a set of wavelengths satisfying the conditions to apply the closed-form lattice algorithm from a given interval. Then we propose an enhanced lattice algorithm for range estimation based on existing lattice approaches. In the conventional lattice algorithm, the selection of an integer vector by the estimator often fails to be the ground truth because of noise. As a result, the true value can be a neighbouring lattice point of the estimated point where neighbours are defined in terms of a distance depending on the noise characteristics. In this paper, all neighbouring lattice points of the estimated point obtained by a conventional lattice algorithm are regarded as candidates for the new estimate. Then an efficient estimator is presented to choose one of those candidates to be the new estimate. The performance of the proposed algorithm, i.e. the successful probability of reconstruction, is derived mathematically. Additionally, a computation-reduced algorithm is presented to reduce the overhead of computation. The calculated probability and simulation results demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Doppler ambiguity in the range-Doppler domain of linear frequency modulated (LFM) continuous wave radars occurs when a target’s Doppler frequency exceeds half of the chirp repetition frequency. Doppler ambiguity is common in a variety of civil and military radar applications, and an extension of the maximal unambiguous Doppler shift is critical for their practical use. This article utilizes previously neglected high-order phase terms in the received LFM radar echo for extension of maximum unambiguous velocity. A computationally feasible velocity estimation algorithm using hypotheses testing with local maximum likelihood is derived. The ability of the proposed low-complexity algorithm to estimate previously ambiguous target velocity is investigated using recorded automotive radar measurements and via simulations. The performance of the proposed algorithm is evaluated using a novel model of the Doppler estimation errors that accounts for Doppler ambiguity. The ability of the derived model to predict the threshold phenomenon is demonstrated via simulations. [ABSTRACT FROM AUTHOR]

When range ambiguity and range dependence coexist in forward-looking ground moving target indication radar, the traditional space-time adaptive processing (STAP) approaches may fail to work. To cope with this issue, a range-ambiguous clutter suppression approach based on elevation time diverse array (TDA) radar system and azimuth phase coding technique is presented in this article. Due to the advantages of frequency scanning, the elevation TDA radar is able to cover all directions by transmitting a single time-shifted waveform. Therefore, a frequency bandpass filter is devised for each single range bin to achieve the mainlobe echo extraction procedure. In the sequel, the azimuth phase decoding and Doppler filtering are performed to deal with the residual sidelobe ambiguous energy. With two-dimensional filtering, one can extract the unambiguous signals for each range region independently. Then, the traditional clutter compensation and STAP methods are employed to suppress the ground clutter. Finally, the effectiveness of the proposed framework in resolving range ambiguity is verified by the numerical simulation experiments. [ABSTRACT FROM AUTHOR]

Range/Doppler migration and velocity ambiguity are two well-known problems encountered in high-speed moving target detection using a linear-frequency-modulated continuous-wave automotive radar. To mitigate the problems, we introduce a simple Doppler–range processing (DRP) algorithm by first performing Doppler processing via fast Fourier transform (FFT) across slow-time samples, followed by a simple interpolation step, and then range processing via FFT along Doppler migration lines over fast-time samples. The proposed DRP algorithm can achieve full range and full velocity resolutions, as well as full coherent integration gains. It attains a computational complexity comparable to that of the conventional 2-D-FFT-based range–Doppler processing approach, computationally much more efficient than existing approaches. The proposed DRP algorithm can automatically resolve the velocity ambiguity problems. We analyze its velocity ambiguity mitigation capability in relation to the radar bandwidth and the number of slow-time samples within a coherent processing interval. The effectiveness and the computational efficiency of the proposed algorithm are demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

Unlike the conventional phased array, the frequency-diverse array (FDA) employs a tiny frequency increment across the array elements, which is capable of providing range–angle–time-dependent beampattern, and thereby offers potential benefits in target localization and interference mitigation. Reported literature on FDA mostly focuses on its intrapulse (fast-time) property and ignores the interpulse (slow-time) feature induced by frequency offset. In this article, incorporating the neglected inherent slow-time coding feature, a novel slow-time FDA multiple-input multiple-output (FDA-MIMO) technique is proposed for the space-time adaptive processing (STAP) radar. The new coding scheme can separate transmitting (Tx) signals via slow-time Doppler filtering, and the problem of signal aliasing is tackled by appropriately designing the slow-time codes. At the receiving (Rx) side, two Rx schemes are devised for recovering range-dependent Tx degrees-of-freedom. Moreover, the relevant clutter rank and the rule for code design are derived in detail. The outstanding merits of the proposed slow-time FDA-MIMO STAP radar consist of the following: There is no specific restriction on the probing waveform; the negative effect of time-variant pattern is perfectly eliminated; and both the target localization accuracy and clutter suppression performance are superior over the state-of-the-art FDA radar systems. Numerical results corroborate the superiorities of the proposed waveform strategy for STAP application. [ABSTRACT FROM AUTHOR]

The surface wave investigation and monitoring (SWIM) aboard the China-France Oceanography Satellite (CFOSAT), a pioneer conically scanning wave spectrometer, was successfully launched on October 29, 2018. Its innovative configuration composed of one nadir and five rotating near-nadir beams is designed to simultaneously observe the directional wave spectrum at a global scale. In this study, we systematically implement the spectral analysis of the radar backscattering with the periodogram technique to obtain the fluctuation spectrum for each azimuth direction. The 2-D fluctuation spectrum of the three spectral beams ($\theta = 6^\circ $ , 8°, and 10°) combines all the azimuth directions within one entire rotation of 360°. The case study demonstrates that the wave features (peak wavelength and direction) are roughly consistent between the estimated fluctuation spectrum and the collocated WaveWatch III wave slope spectrum. A marked up-to-downwave asymmetry of the fluctuation spectrum with larger spectral level in the upwave direction for all the three spectral beams is observed. A ratio is defined between the fluctuation spectrum within the [0°, 180°] sector relative to the [180°, 360°] sector. Statistics display that this ratio is greater than 1 when it denotes the up-to-downwave ratio and smaller than 1 for the down-to-upwave ratio. This observed spectrum asymmetry is linked to the asymmetric modulation from upwind to downwind. In addition, we employ such finding to help remove the 180° wave direction ambiguity from a practical point of view. Preliminary results of the direction ambiguity removal display a bias of 41.3°, 40.6°, and 36.7° for the beams. The 10° beam shows slightly better performance compared to the other two beams in terms of bias and standard deviation. This shall lay a strong basis for the operational implementation of such algorithm to resolve the direction ambiguity. [ABSTRACT FROM AUTHOR]

Hybrid-polarimetric architecture may become a new option for spaceborne quadrature-polarimetric (quad-pol) synthetic aperture radar (SAR) mainly due to the range ambiguity improvement without loss of polarization information. To inherit the analytical methods developed for the conventional quad-pol SAR data, it is necessary to transform the hybrid quad-pol SAR data into a linear polarimetric basis. However, this operation will result in deterioration of azimuth ambiguity performance. The theoretical analysis on range and azimuth ambiguities in quad-pol SAR has been relatively complete, which is also summarized in this article. Yet, there is no real data to verify the equivalence of polarization information and to compare the ambiguity levels. Based on P-band airborne experiment, real hybrid and conventional quad-pol SAR data are used for comparative verification. In addition, considering that polarimetric target decomposition is often used in the practical applications, we suggest a new perspective from target decomposition for the ambiguities in quad-pol SAR. Identical polarimetric processing results for two sets of comparative data verify that hybrid quad-pol SAR data are indistinguishable from conventional quad-pol SAR data. The demonstrated intensity images and decomposed RGB images with azimuth ambiguities, acquired by the two quad-pol modes, completely conform to the theory. [ABSTRACT FROM AUTHOR]

Modality translation, which aims to translate images from a source modality to a target one, has attracted a growing interest in the field of remote sensing recently. Compared to translation problems in multimedia applications, modality translation in remote sensing often suffers from inherent ambiguities, i.e., a single input image could correspond to multiple possible outputs, and the results may not be valid in the following image interpretation tasks, such as classification and change detection. To address these issues, we make the attempt to utilizing time-series data to resolve the ambiguities. We propose a novel multimodality image translation framework, which exploits temporal information from two aspects: 1) by introducing a guidance image from given temporally neighboring images in the target modality, we employ a feature mask module and transfer semantic information from temporal images to the output without requiring the use of any semantic labels and 2) while incorporating multiple pairs of images in time series, a temporal constraint is formulated during the learning process in order to guarantee the uniqueness of the prediction result. We also build a multimodal and multitemporal dataset that contains synthetic aperture radar (SAR), visible, and short-wave length infrared band (SWIR) image time series of the same scene to encourage and promote research on modality translation in remote sensing. Experiments are conducted on the dataset for two cross-modality translation tasks (SAR to visible and visible to SWIR). Both qualitative and quantitative results demonstrate the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

This article deals with the synthesis and analysis of quasi-orthogonal nonlinear frequency modulation (NLFM) waveforms to mitigate the impairments of ambiguous returns in quadrature-polarimetric (quad-pol) synthetic aperture radars (SARs). To this end, focusing on signals with a continuous piecewise linear instantaneous frequency, the design of a waveform pair exhibiting both a low cross correlation energy (CCE) and low peak to sidelobe ratios (PSLRs), is considered. To handle the resulting nondeterministic polynomial (NP) hard problem, a coordinate descent (CD) method is employed, where, at each step, the marginal minimization is tackled via a MATLAB optimization toolbox. Hence, transmission/reception schemes jointly capitalizing quasi-orthogonal NLFM waveforms and azimuth phase coding (APC) techniques are proposed to suppress ambiguity interference. Moreover, a systematic framework for the evaluation of the resulting azimuth ambiguity-to-signal ratio (AASR) and range ambiguity-to-signal ratio (RASR) is provided. Finally, detailed simulation experiments based on the LuTan (LT-1) parameters are carried out to verify the practicability and effectiveness of the newly proposed transceiver schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose clustering-based blind detection methods with the aid of systematic convolutional low density generator matrix (SC-LDGM) codes. Inspired by the fact that the received signals naturally fall into clusters in the block-fading channels, we develop a system constrained Gaussian mixture model (SCGMM) by taking into account the inherent characteristics of communication systems, in which the parameters can be estimated by the expectation-maximization (EM) algorithm. We also present an initialization method for the proposed SCGMM to accelerate convergence. After clustering, a decoding algorithm of SC-LDGM codes is designed to resolve the centroid ambiguity. To further improve the detection performance in the low signal-to-noise (SNR) and deep fading scenarios, we propose an improved label-assisted (ILA) method, which integrates the label-assisted (LA) information into the blind detection algorithm. Numerical results show that the performance of our methods can closely approach the performance with perfect channel state information (CSI). The simulation results also show that the performance can be improved by increasing the encoding memory. [ABSTRACT FROM AUTHOR]

Millimeter-wave radar interferometry is superior in detecting small displacement motions owing to its short wavelength. However, it is subject to phase ambiguity as the target displacement may often exceed a quarter wavelength. In this article, a modified differentiate and cross-multiply (MDACM) technique is proposed to tackle the phase ambiguity issue for accurate reconstruction of the phase history in millimeter-wave interferometry. In addition, to resolve the imperfections of the interferometric radar system, an MDACM-based integral phase reconstruction approach is presented, which seamlessly integrates alternating current (ac)-coupling-induced distortion correction, $I/Q$ mismatch correction, and direct current (dc) offsets’ calibration. Without causing any phase ambiguity, the proposed technique acts as a black box to take in the raw $I/Q$ signals and correct all the hardware imperfections, and it outputs the desired displacement motions with micrometer accuracy. The simulation results show that the proposed technique can not only linearly recover the displacement motions across a wide range in different noise conditions without any phase ambiguity but also improve the stability by 11 times under ac-coupling-induced distortion. With a custom-designed 120-GHz interferometric radar sensor, experiments were carried out to validate various scenarios including mechanical vibrations and gesture sensing. The experimental results show that the proposed technique can accurately track not only deep subwavelength motion of only $1~\mu \text{m}$ but also multiwavelength displacement of >40 times of the wavelength at 120 GHz. [ABSTRACT FROM AUTHOR]

The high precision positioning of global navigation satellite systems (GNSS), which essentially corresponds to solving the integer least-squares (ILS) problem in the integer phase ambiguity estimation, has emerged as a key problem for the development of industrial internet-of-things (IIoT). In this paper, a novel paradigm for solving the ILS problem is introduced to the integer phase ambiguity estimation. Different from the traditional paradigm of ILS which only involves one parameter to characterize the trade-off between performance and complexity, the proposed ILS paradigm entails two parameters named as the initial searching size $K\geq 1$ and the standard deviation $\sigma >0$ , thus introducing extra degrees of freedom to facilitate the system trade-off. Based on it, explicit analysis can be carried out for a mathematically tractable trade-off, where great potentials could be further exploited for the high precision positioning of GNSS. The equivalent searching algorithm (ESA) is proposed, which achieves the same performance as the classic Fincke-Pohst strategy in sphere decoding (SD) but with tractable complexity measured by the number of visited nodes in the searching stage. Moreover, the candidate protection mechanism is given to further upgrade the equivalent searching algorithm, which makes it not only an optimal but also a sub-optimal ILS estimator given the flexible setup of $K$. [ABSTRACT FROM AUTHOR]

In a conventional frequency-modulated continuous-wave (FMCW) radar, the target distance is estimated by the beat frequency, which is obtained from dechirping the received signal with the transmitted one. The challenge to achieve fine-range sensing is limited by the inaccuracy of beat frequency caused by the nonlinearity of sweeping chirps. In this work, instead of determining the accurate beat frequency, a novel dual-phase-term tracking (DPTT) method is introduced for high-accuracy range sensing even in a severe nonlinear situation. By tracking the phase of the beat signal and observing the phase difference over the up or down period of a triangular chirp, two distinct featured phase terms $\phi _{o}$ and $\Phi _{B}$ are extracted. The phase $\phi _{o}$ represents the carrier round-trip phase delay at $f_{\mathrm {o}}$. Although $\phi _{o}$ is free of the nonlinear chirping effect, the range estimation based on $\phi _{o}$ suffers from the range ambiguity problem. The phase term $\Phi _{B}$ represents the phase change over the first half chirp period and it is monotonically increased across the range ambiguity zones of $\phi _{o}$. Therefore, by combining these phase terms to determine the target range, both the frequency chirping nonlinearity and the range ambiguity can be effectively eliminated such that the range accuracy of tens of micrometers can be achieved even with an extremely narrow chirp bandwidth. To validate the proposed method, a 24-GHz FMCW radar with a 400-MHz chirp bandwidth and a 10-ms chirp period has been designed and implemented for demonstration. At a distance of 0.6 m, the measured range accuracy is $40.1~\mu \text{m}$ for the weak nonlinearity factor of 0.3%. For the severe nonlinear chirp case with a 25.3% nonlinearity factor, the range accuracy of $137.5~\mu \text{m}$ is also successfully obtained, validating the robustness of this proposed DPTT method. [ABSTRACT FROM AUTHOR]

Forward-looking multichannel synthetic aperture radar (FLMC-SAR) is an important tool for modern remote sensing applications, which has the capability to reconstruct the high-resolution image of the front area. However, due to the azimuth-variant characteristics of the motion errors over a long aperture, FLMC-SAR data processing is usually a challenging task, especially when involving the motion compensation (MOCO) coupled with Doppler ambiguity resolving. To accomplish an accurate MOCO for FLMC-SAR, a novel parametric azimuth-variant MOCO approach is proposed in this article. Aiming at the coupling problem of MOCO and Doppler ambiguity resolving over the full aperture, we can decouple them through the subaperture division. As a full synthetic aperture is decomposed into several subapertures, the high-order motion errors of the full aperture can be decomposed into the first-order motion errors of the subaperture. On this basis, the mismatch of the space–time spectrum caused by the motion errors can be solved by spectral estimation, yielding Doppler ambiguity resolving for each subaperture. Meanwhile, the azimuth-variant characteristic of motion errors in FLMC-SAR system is characterized by a parametric angle-dependent quadratic phase error (QPE) model. The motion parameters are estimated by a joint multichannel angle estimation-based signal quadratic decomposition method. Immediately, the MOCO for ambiguous targets with different motion errors can be processed separately to improve the imaging performance. Experimental results based on both simulated and real data demonstrate that the proposed method is suitable for FLMC-SAR system. [ABSTRACT FROM AUTHOR]

Hybrid antenna array consisting of analog subarrays provides a low-cost solution to achieving high beamforming gain in millimeter-wave/terahertz communication systems. For beam adjustment, beam scanning can be readily applied to find the line-of-sight (LoS) path. Conventionally, the beam scanning process is inefficient because the number of grids, together with the scanning period, has to be large to achieve high resolution. To improve the beam scanning efficiency, in this paper we propose an off-gird direction-of-arrival (DoA) estimation method utilizing unequal-sized subarrays. Specifically, we first provide a new solution to overcome the ambiguity problem, i.e., the hybrid antenna array may render the DoA estimate non-unique. That new solution is formed by designing the antenna numbers in the subarrays and can be easily combined with the beam scanning process. We then propose a three-stage beam scanning strategy. At the first stage, coarse scanning is applied to roughly steer the subarrays along the LoS path. At the second stage, a simple DoA estimator is employed to obtain the candidate DoAs. Finally, beam scanning is performed again on the candidate DoAs to obtain the final DoA estimate. Simulation results demonstrate that the proposed method can achieve higher DoA estimation accuracy in shorter scanning period than the conventional beam scanning method; and compared with existing methods developed for equal-sized subarrays, the proposed method is more robust against multi-path interference. [ABSTRACT FROM AUTHOR]

Visual emotion analysis (VEA) has attracted great attention recently, due to the increasing tendency of expressing and understanding emotions through images on social networks. Different from traditional vision tasks, VEA is inherently more challenging since it involves a much higher level of complexity and ambiguity in human cognitive process. Most of the existing methods adopt deep learning techniques to extract general features from the whole image, disregarding the specific features evoked by various emotional stimuli. Inspired by the Stimuli-Organism-Response (S-O-R) emotion model in psychological theory, we proposed a stimuli-aware VEA method consisting of three stages, namely stimuli selection (S), feature extraction (O) and emotion prediction (R). First, specific emotional stimuli (i. e., color, object, face) are selected from images by employing the off-the-shelf tools. To the best of our knowledge, it is the first time to introduce stimuli selection process into VEA in an end-to-end network. Then, we design three specific networks, i. e., Global-Net, Semantic-Net and Expression-Net, to extract distinct emotional features from different stimuli simultaneously. Finally, benefiting from the inherent structure of Mikel’s wheel, we design a novel hierarchical cross-entropy loss to distinguish hard false examples from easy ones in an emotion-specific manner. Experiments demonstrate that the proposed method consistently outperforms the state-of-the-art approaches on four public visual emotion datasets. Ablation study and visualizations further prove the validity and interpretability of our method. [ABSTRACT FROM AUTHOR]

In semantic visual simultaneous localization and mapping (VSLAM), accurate data association of semantic measurement from visual sensor is very crucial for robot state estimation and scene reconstruction. However, most of the related works assume a simple world for semantic association. It is still a challenge to deal with the ambiguity of data association in a cluttered environment. In this article, we propose a novel approach to reduce the uncertainty of data association via multiple hypothesis Dirichlet process (MHDP). The posterior distribution of data association is inferred by Dirichlet process (DP) first. Ambiguous associations from the distribution are tackled by a hypothesis tree, and hypothesis testing-based ambiguity judgment is then proposed for each object measurement to provide a strategy for branch growing of the hypothesis tree. Moreover, the proposed data association approach is integrated with a geometric featured-based simultaneous localization and mapping (SLAM) system in a tightly coupled way. The qualitative and quantitative evaluation on simulated and real-world public data sets demonstrates the robustness and effectiveness of our approach compared to other data association methods and the state-of-the-art SLAM system. [ABSTRACT FROM AUTHOR]

This article introduces a precise radar-based direction-of-arrival measurement system using a Viterbi decoder. Based on the frequency-modulated continuous-wave principle, the presented method employs both the frequency difference and the round-trip phase difference of the radio wave signal impinging at two widely spaced receiving antennas, thereby accomplishing highly accurate and absolute measurements of the direction-of-arrival. In order to resolve angular ambiguity, which is inherently caused by the $2 \pi $ ambiguity in phase measurement, the proposed Viterbi decoder determines the most likely sequence of direction-of-arrivals of the impinging radio wave signals. For proof of concept, experiments based on a 77 GHz radar unit equipped with one transmitting and two widely spaced receiving antennas were carried out. The measurement results show that angular ambiguity can be resolved by the presented Viterbi decoder since absolute, accurate, and repeatable direction-of-arrival estimates were obtained, even for receiver antenna spacings significantly larger than the signal wavelength. [ABSTRACT FROM AUTHOR]

In this article, we study the problem of multi-view sketch correspondence, where we take as input multiple freehand sketches with different views of the same object and predict as output the semantic correspondence among the sketches. This problem is challenging since the visual features of corresponding points at different views can be very different. To this end, we take a deep learning approach and learn a novel local sketch descriptor from data. We contribute a training dataset by generating the pixel-level correspondence for the multi-view line drawings synthesized from 3D shapes. To handle the sparsity and ambiguity of sketches, we design a novel multi-branch neural network that integrates a patch-based representation and a multi-scale strategy to learn the pixel-level correspondence among multi-view sketches. We demonstrate the effectiveness of our proposed approach with extensive experiments on hand-drawn sketches and multi-view line drawings rendered from multiple 3D shape datasets. [ABSTRACT FROM AUTHOR]

Performing as an important technology, microwave interferometry helps understand and analyze plasma characteristics. However, the relatively appropriate diagnosing methods being discovered for investigating fast time-varying plasma have met with limited success so far. The crucial problem of this task lies in eliminating the interference of additive noise while compensating for phase ambiguity of the initial point and of the phase jump points. In particular, randomly varying electron density and noise make it impossible to directly detect these points. Theoretical analysis has found that the time-varying electron density of plasma causes the received signal to rotate on the complex plane. Inspired by this fact, a deep learning (DL) method is proposed to track the time-varying plasma and solve the above two problems, during which the deep neural network (DNN) is utilized to extract the curve from the data, thereby eliminating the noise and detecting the initial point and the phase jump points according to the projection of the data on the curve. Experimental results suggest that the learned curve is in good consistency with the ground-truth curve. Meanwhile, the time-varying electron density and the collision frequency can be accurately diagnosed using the learned curve. [ABSTRACT FROM AUTHOR]

A nonlinear trajectory of a radar platform in synthetic aperture radar (SAR) may lead to severe coupling between the range and the azimuth, which may make the ambiguity function (AF) analysis complicated. The numerical algorithm-based AF analysis may be computationally expensive, while the existing analytical algorithm-based AF analysis may cause large errors because it does not consider the coupling between the range and the azimuth. By observing that the singular value decomposition (SVD) is good to deal with the coupling problem, in this article, we propose an effective AF analysis based on SVD. The key idea is to first use a small amount of sampling points for SVD of the coupled term in the AF and then the decoupled vectors are fitted to high-order polynomials for the analytical AF calculation. It converts the double integral into the product of two single integrals in the calculation. From the proposed SVD-based AF analysis, three parameters, namely, 3-dB resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR), are then effectively computed. The simulated results verify the good performance of the proposed SVD-based AF analysis. [ABSTRACT FROM AUTHOR]

A new modeling methodology for estimation of the parameters of the underlying multipath structure in an ultrawideband (UWB) indoor propagation channel is proposed in this article based on measurement campaign data available for a typical modern laboratory building. Issues inherent in generic multipath parameter estimation procedures such as limitations of curve fitting techniques, ambiguity in identifying cluster regions, and arbitrariness in estimating cluster and ray parameters accentuate a lack of transparency in the whole parameter estimation process and prevent the performance evaluation of different UWB physical layer proposals. A ramification of such techniques is that they are often too complex and intractable. The proposed approach, in this article, addresses these issues and offers a simple, tractable, easy to implement, and accurate solution based on a rationale proposing that the true behavior of the multipath structure is dependent on the structure of the considered environment itself and not on any additional reflectors. The results obtained from the proposed model are compared against the IEEE 802.15.4a CM1 and CM2 channel models. The proposed model establishes a normative to compare and evaluate performances between different UWB propagation models in realistic environments. [ABSTRACT FROM AUTHOR]

This paper considers the problem of high-speed maneuvering target detection, which involves the processing of complex range migration (RM) and Doppler frequency migration (DFM) within the coherent processing interval (CPI). By exploiting the coupling relationship between the range frequency and the Doppler frequency, a detection algorithm is proposed for high-speed maneuvering targets. After the range compression, approximate 2-D range-frequency and Doppler-frequency expressions are derived according to the principle of stationary phase (POSP) and series inversion extension. The mathematical range-Doppler frequency relationships are then exploited to search the target's first-order motion parameters and Doppler ambiguity numbers within a predetermined parameter range, and formulate the corresponding new data from the original 2-D data matrix. Finally, Fourier transform and constant false-alarm rate (CFAR) detector are further applied to detect the targets. Simulation results show that the proposed algorithm has low complexity requirements and is suitable for low signal-to-noise ratio (SNR) scenarios. Moreover, it is not sensitive to the motion order of targets. [ABSTRACT FROM AUTHOR]

We consider the problem of recovering low-rank matrices from random rank-one measurements, which spans numerous applications including covariance sketching, phase retrieval, quantum state tomography, and learning shallow polynomial neural networks, among others. Our approach is to directly estimate the low-rank factor by minimizing a nonconvex least-squares loss function via vanilla gradient descent, following a tailored spectral initialization. When the true rank is bounded by a constant, this algorithm is guaranteed to converge to the ground truth (up to global ambiguity) with near-optimal sample complexity and computational complexity. To the best of our knowledge, this is the first guarantee that achieves near-optimality in both metrics. In particular, the key enabler of near-optimal computational guarantees is an implicit regularization phenomenon: without explicit regularization, both spectral initialization and the gradient descent iterates automatically stay within a region incoherent with the measurement vectors. This feature allows one to employ much more aggressive step sizes compared with the ones suggested in prior literature, without the need of sample splitting. [ABSTRACT FROM AUTHOR]

Phase decorrelation, as one of the main error sources, limits the capability of interferometric synthetic aperture radar (InSAR) for deformation mapping over areas with low coherence. Although several methods have been realized to reduce decorrelation noise, for example, by phase linking and spatial and temporal filters, their performances deteriorate when coherence estimation bias exists. We present an arc-based approach that allows reconstructing unwrapped interval phase time-series based on iterative weighted least squares (WLS) in temporal and spatial domains. The main features of the method are that phase optimization and unwrapping can be jointly conducted by spatial and temporal iterative WLS and coherence matrix bias has negligible effects on the estimation. In addition, the linear formation makes the implementation suitable with small subset of interferograms, providing an efficient solution for future big SAR data. We demonstrate the effectiveness of the proposed method using simulated and real data with different decorrelation mechanisms and compare our approach with the state-of-art phase reconstruction methods. Substantial improvement can be achieved in terms of reduced root-mean-square error (RMSE) in the simulation data and increased density of coherent measurements in the real data. [ABSTRACT FROM AUTHOR]

The azimuth phase coding (APC) technique is known for its very low implementation complexity and its effectiveness for point and distributed ambiguities in conventional synthetic aperture radar (SAR) systems. In recent years, as an extension, the APC technique has been briefly discussed for multichannel SAR systems. However, the properties of the APC technique are no longer guaranteed in the multichannel SAR systems based on the digital beamforming (DBF) on-receive, and only a slight APC gain in the suppression of the range ambiguity can be obtained. In this article, we first provide a more thorough analysis for an APC-multichannel SAR system with respect to a uniform pulse-repetition frequency (PRF). Then, the APC/multichannel SAR system with nonuniform spatial sampling is briefly discussed, and an improved reconstruction approach based on a quadratically constrained optimization model is proposed to increase greatly the APC gain with respect to existing multichannel reconstruction algorithms. This proposed approach allows the minimization of the range ambiguity with a given azimuth-ambiguity constraint. In particular, for some specific PRFs, the proposed method permits a cancellation of the odd-order range ambiguity. Finally, simulation experiments are performed to verify the advantages and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Random frequency and pulse repetition interval (PRI) agile (RFPA) signals bring excellent performance of electronic counter-countermeasures to radar systems and have been received considerable attention in recent years. However, the research on their ambiguity function (AF) is not comprehensive. In this article, the analytical expressions of the AF expectation and variance are given. According to the expressions, the direct relationships between the key metrics of the AF and the waveform parameters of RFPA signals are specified. The results in this article are verified by Monte Carlo simulations and provide some insights into RFPA waveform design. [ABSTRACT FROM AUTHOR]