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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]

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]

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]

The piecewise constant Doppler (PCD) algorithm is a novel radar imaging process recently proposed for the generalized continuous-wave synthetic aperture radar (GCW-SAR). This article presents a detailed theoretical analysis on the PCD algorithm's performance and proposes a further complexity-reduced PCD algorithm with motion compensation (MOCO) suitable for practical applications. First, the difference between conventional SAR imaging and PCD imaging, i.e., the zeroth-order versus the first-order slant range approximation, is revealed. Exact ambiguity function expressions of the PCD imaging in range and azimuth directions, respectively, are then derived. An error function of the PCD imaging as compared with the ideal matched filtering method is further defined and shown to be a function of an image quality factor, which can be used to quantify the PCD imaging performance. Finally, a faster and more flexible imaging process, called decimated PCD algorithm, is proposed, by which the image azimuth spacing can be easily extended, and hence, the computational complexity can be significantly reduced. The decimated PCD implementation incorporated with the MOCO is developed for practical GCW-SAR applications, and its imaging error lower-bounded by the PCD imaging error function is analyzed accordingly. Simulation and experimental results validate the theoretical analysis of the PCD imaging and show that the decimated PCD algorithm can achieve a high imaging quality at low cost. [ABSTRACT FROM AUTHOR]

Topology design is a critical task for the reliability, economic operation, and resilience of distribution systems. This paper proposes a distributionally robust optimization (DRO) model for designing the topology of a new distribution system facing random contingencies (e.g., imposed by natural disasters). The proposed DRO model optimally configures the network topology and integrates distributed generation to effectively meet the loads. Moreover, we take into account the uncertainty of contingency. Using the moment information of distribution line failures, we construct an ambiguity set of the contingency probability distribution, and minimize the expected amount of load shedding with regard to the worst-case distribution within the ambiguity set. As compared with a classical robust optimization model, the DRO model explicitly considers the contingency uncertainty and so provides a less conservative configuration, yielding a better out-of-sample performance. We recast the proposed model to facilitate the column-and-constraint generation algorithm. We demonstrate the out-of-sample performance of the proposed approach in numerical case studies. [ABSTRACT FROM AUTHOR]

This article considers the dual-use unimodular signal design for a novel dual-functional radar-communication (DFRC) architecture. The information of downlink communication is modulated via the ambiguity function (AF) sidelobe nulling in the prescribed range-Doppler cells. At the same time, the sidelobe around the AF mainlobe is suppressed to ensure the radar detection performance. User equipment (UE) computes the AF of the received signal to make a judgment of the position of nulling for achieving demodulation. To this end, an objective function to evaluate the depth of nulling in AF is developed as figure of merit accounting for the worst-case sidelobe level ratio over the range-Doppler cells of interest. The resultant design is very challenging to solve due to the non-convex and non-smooth quartic fractional objective function and the NP-hard constant modulus constraint. Herein, we develop a fractional-alternating direction penalty method (FADPM) algorithm that invokes the fractional program theory and the ADPM framework. Specifically, we formulate a new ADPM form through introducing an auxiliary variable and resorting to the Dinkelbach's procedure. In each iteration, we transform the original quartic fractional program into two quadratic optimization subproblems both of which are approximated successively through a series of convex subproblems. We also provide the analytical convergence guarantee of the proposed FADPM algorithm. The simulation results verify the performance of the proposed algorithm and exhibit the effectiveness of the DFRC framework in modulation and demodulation process guaranteeing that a high data rate is achievable. [ABSTRACT FROM AUTHOR]

The paper presents a comprehensible phase interferometer system which considers the four-element array as a cascade of two three-element subarrays and explores the mechanism how the short subarray resolves the ambiguity of the long subarray in a general manner. The resulted method can estimate the direction of arrival with high resolution while keeping very low ambiguity probability in the presence of noises over a wide field of view and a broadband with low computation complexity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a long-time coherent integration method for a maneuvering target with the first-, second-, and third-order range migrations, and the complex Doppler frequency migration. In this method, after range compression, the echo signal is first transformed into the range-frequency and Doppler domain based on series reversion, and then an azimuth matched filtering procedure is implemented in the 2-D frequency domain. It can eliminate the coupling effects between range and azimuth jointly caused by the radial velocity, radial acceleration, and radial acceleration rate of a moving target. Due to the linear transform property, the proposed method can work well under low signal-to-clutter and noise ratio. In addition, the Doppler ambiguities, when target azimuth spectrum either is within a pulse repetition frequency (PRF) or spans over neighboring PRF bands, can be well solved. Both simulated and real synthetic aperture radar data processing results are provided to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We present a new algorithm for suppressing azimuth ambiguities when measuring surface currents with an along-track interferometric (ATI) synthetic aperture radar in heterogeneous scenes, such as coastal waters or rivers. The key of the proposed algorithm involves a careful analysis of a parameter called the eigenvalue spectrum entropy (EVSE), which is defined as the entropy of the eigenvalue spectrum of the ATI covariance matrix computed in the Doppler domain. The physical meaning of EVSE is that it serves as a descriptor for the degree to which an unambiguous signal component and an ambiguous one are mixed. With the help of EVSE, azimuth ambiguities can be suppressed. Simulation results demonstrate that as compared with the conventional ATI method without azimuth ambiguity suppression, the proposed algorithm allows for a pronounced improvement in the current measuring accuracy. Other advantages of the proposed algorithm lie in the fact that it is not only adaptive, due to its ability to automatically capture the useful Doppler band whose width and position may both vary for different radar and scene parameters, but it also needs a minimal number of user inputs, making it a quite attractive algorithm for routine implementation. [ABSTRACT FROM AUTHOR]

In pulsed Doppler radars, subsampling causes incorrect estimates of the radial velocity of the detected targets whenever its related Doppler frequency is greater than the pulse repetition frequency of the radar. The classic Chinese remainder theorem is a well-known method to overcome this problem. On the other hand, the robust Chinese remainder theorem is a search-based algorithm that, under some conditions, can guarantee the correct estimation of the Doppler frequency. In this paper, we have derived the exact probability of detection for both methods applied in a pulsed Doppler radar. Simulations have been compared to the analytical results and have shown an excellent agreement. [ABSTRACT FROM AUTHOR]

This paper describes a clutter suppression approach and the corresponding moving target imaging algorithm for a multichannel in azimuth high-resolution and wide-swath (MC-HRWS) synthetic aperture radar (SAR) system. Incorporated with digital beamforming processing, MC-HRWS SAR systems are able to suppress the Doppler ambiguities to allow for HRWS SAR imaging and null the clutter directions to suppress clutter for ground moving target indication. In this paper, the degrees of freedom in azimuth for the multichannel SAR systems are employed to implement clutter suppression. First, the clutter and moving target echoes are transformed into the range compression and azimuth chirp Fourier transform frequency domain, i.e., coarse-focused images formation, when the clutter echoes are with azimuth Doppler ambiguity. Considering that moving targets are sparse in the imaging scene and that there is a difference between clutter and a moving target in the spatial domain, a series of spatial domain filters are constructed to extract moving target echoes. Then, using an extracted moving target echo, two groups of signals are formed, and slant-range velocity of a moving target can be estimated based on baseband Doppler centroid estimation algorithm and multilook cross-correlation Doppler centroid ambiguity number resolving approach. After the linear range cell migration correction and azimuth focus processing, a well-focused moving target image can be obtained. In addition, the proposed clutter suppression and imaging approach is not only adapted for uniformly displaced phase center sampling but also for the nonuniform sampling cases. Some simulation experiments are taken to demonstrate our proposed algorithms. Finally, some real measured data results are presented to validate the theoretical investigations and the proposed approaches. [ABSTRACT FROM PUBLISHER]

In this paper, a real-time algorithm that operates on data provided by a network of weather radars in the form of moments is presented. The algorithm implementation, performance, and abilities are presented and discussed. As part of this paper, a methodology for mapping from native spherical coordinates to a common network grid is developed and demonstrated. Metrics which gauge the quality of mapping and quality of data are derived, and their use from a network point of view is demonstrated. The main product of the algorithm, which is an implementation of a vector velocity estimation technique based on a maximum likelihood approach, is shown, and its results are displayed, compared, and discussed. The application of a methodology for velocity unfolding and reflectivity display enhancement is presented as well. [ABSTRACT FROM AUTHOR]

It is well known that the motion of a target induces range migration, especially for high-resolution synthetic aperture radar (SAR) systems. Ground moving target imaging necessitates the correction of the unknown range migration. To finely refocus a moving target, one must accurately obtain the motion parameters for compensating the target trajectory. However, in practice, these parameters usually cannot be precisely estimated. This paper proposes a new imaging approach for ground moving targets without a priori knowledge of their motion parameters. In the devised method, the azimuth compression function is constructed in range frequency domain, which can eliminate the coupling effect between range and azimuth. Theoretical analysis confirms that the methodology can precisely focus targets without interpolation procedure. The effectiveness of the proposed imaging technique is demonstrated by both simulated and real airborne SAR data. [ABSTRACT FROM AUTHOR]

Multibody structure from motion (SfM) is the extension of classical SfM to dynamic scenes with multiple rigidly moving objects. Recent research has unveiled some of the mathematical foundations of the problem, but a practical algorithm which can handle realistic sequences is still missing. In this paper, we discuss the requirements for such an algorithm, highlight theoretical issues and practical problems, and describe how a static structure-from-motion framework needs to be extended to handle real dynamic scenes. Theoretical issues include different situations in which the number of independently moving scene objects changes: Moving objects can enter or leave the field of view, merge into the static background (e.g., when a car is parked), or split off from the background and start moving independently. Practical issues arise due to small freely moving foreground objects with few and short feature tracks. We argue that all of these difficulties need to be handled online as structure-from-motion estimation progresses, and present an exemplary solution using the framework of probabilistic model-scoring. [ABSTRACT FROM AUTHOR]

This paper describes an ambiguity resolving approach for slant-range velocity estimation which utilizes the wideband characteristic of the transmitted signal (multiple wavelengths). Based on the wavelength dual-wavelength radar data. Then, two effective approaches are introduced to focus the moving target no matter the Doppler ambiguity exists or not. The slant-range velocity is estimated by the number of azimuth cell displacements between the two focused images. Both imaging methods have different properties and advantages. A performance analysis is made, and deleterious factors in practice are analyzed in detail. The effectiveness of the unambiguous slant-range velocity estimation approach is demonstrated with the use of simulated and real data. [ABSTRACT FROM AUTHOR]

In multiple-instance learning (MIL), an individual example is called an instance and a bag contains a single or multiple instances. The class labels available in the training set are associated with bags rather than instances. A bag is labeled positive if at least one of its instances is positive; otherwise, the bag is labeled negative. Since a positive bag may contain some negative instances in addition to one or more positive instances, the true labels for the instances in a positive bag may or may not be the same as the corresponding bag label and, consequently, the instance labels are inherently ambiguous. In this paper, we propose a very efficient and robust MIL method, called Multiple-Instance Learning via Disambiguation (MILD), for general MIL problems. First, we propose a novel disambiguation method to identify the true positive instances in the positive bags. Second, we propose two feature representation schemes, one for instance-level classification and the other for bag-level classification, to convert the MIL problem into a standard single-instance learning (SIL) problem that can be solved by well-known SIL algorithms, such as support vector machine. Third, an inductive semi-supervised learning method is proposed for MIL. We evaluate our methods extensively on several challenging MIL applications to demonstrate their promising efficiency, robustness, and accuracy. [ABSTRACT FROM AUTHOR]

Physical modeling of the Doppler centroid (DC) can be used to predict synthetic aperture radar (SAR) Doppler ambiguity when antenna attitude is controlled or measured precisely enough. It is shown that the same model proves useful even in the cases of higher attitude uncertainty, if it is combined with suitable adaptive techniques. In this paper, Doppler ambiguity resolution is formulated as a hypothesis testing problem over a domain of integer values that are directly related to the attitude uncertainty. A test statistic is derived from the entire SAR scene using data adaptive processing. A broad class of such adaptive algorithms is analyzed in a unified way, starting from the range-azimuth coupling in the frequency domain and multilook techniques. The analysis includes two well-known and two new multilook methods for Doppler ambiguity resolution. A suitable test statistic is proposed for each of these methods and its dependency on the scene spatial correlation is discussed. Experimental results confirm the robustness of the combined scheme. [ABSTRACT FROM AUTHOR]

The task of decentralized decision-making involves interaction of a set of local decision-makers, each of which operates under limited sensing capabilities and is thus subjected to ambiguity during the process of decision-making. In our prior work, we made a key observation that such ambiguities are of differing gradations and presented a framework for inferencing over varying ambiguity levels to arrive at local and global control decisions. We develop a similar framework for performing diagnosis in a decentralized setting. For each event-trace executed by a system being monitored, each local diagnoser issues its own diagnosis decision (failure or nonfailure or unsure), tagged with a certain ambiguity level (zero being the minimum). A global diagnosis decision is taken to be a "winning" local diagnosis decision, i.e., one with a minimum ambiguity level. The computation of an ambiguity level for a local decision requires an assessment of the self-ambiguity as well as the ambiguities of the others, and an inference based up on such knowledge. In order to characterize the class of systems for which any fault can be detected within a uniformly bounded number of steps (or "delay"), we introduce the notion of N-inference-diagnosability for Failures (also called N-inference F-diagnosability), where the index N represents the maximum ambiguity level of any winning local decision. We show that the codiagnosability introduced in [19] is the same as 0-inference F-diagnosability; the conditional F-codiagnosability introduced in [34], [35] is a type of 1-inference F-diagnosability; the class of higher-index inference F-diagnosable systems strictly subsumes the class of lower-index ones; and the class of inference F-diagnosable systems is strictly subsumed by the class of systems that are centrally F-diagnosable. Note to Practitioners--This paper studies the problem of decentralized failure diagnosis in discrete event systems. This is relevant for diagnosis of large scale distributed systems such as communication networks, manufacturing systems, and power systems. Multiple decision-makers observe the behaviors of an underlying system using their own set of sensors, and jointly diagnose the occurrence of a failure based on their observations of the system behavior together with an inference of the self-ambiguity and those of the others. Each diagnosis decision of a decision-maker is tagged with an ambiguity level,and an overall diagnosis decision is taken to be a diagnosis decision with a minimum ambiguity level. A notion of N-inference F-diagnosability is introduced as a condition under which each failure is diagnosed within a bounded number of steps (or "delay") of its occurrence by one of the decision-makers, and the ambiguity levels of all winning decisions are upper bounded by N. Properties of N-inference F-diagnosable system and specification pairs are studied and an algorithm for verifying the property of N-inference F-diagnosability is presented. [ABSTRACT FROM AUTHOR]

Abstract-A method for full-azimuth direction-of-arrival (DoA) estimation of multiple signals with a hexagonal array is proposed. The DoA estimation is performed in two steps. In the first, a set of estimate candidates is constructed by gathering the estimates that are obtained from applying the low-computational-cost Unitary-ESPRIT algorithm to several translational invariances designed into a hexagonal array. In the second step, the DoA estimates are successively selected from the estimate candidate set by using a selection function. The proposed method removes the north-or-south signal membership ambiguity and the limitation on the number of estimable sources, problems common to any ESPRIT-based algorithm used with one translational invariance. Therefore, up to M - 1 signal DoA estimations can be expected with an M-element hexagonal array in the full azimuth. The successive-selection approach is based on a selection function that uses an estimate of the signal's spatial correlation matrix to successively select the DoA estimates. For each DoA estimate selection, the already estimated signal components are removed from the correlation matrix. Such an approach is shown to actually allow the selection of suitable estimates from among a set of estimate candidates. Finally, the method's DoA estimation and resolution capabilities are demonstrated by computer simulation and comparison with the Cramér-Rao bound. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel carrier frequency offset (CEO) tracking algorithm for orthogonal frequency division multiplexing (OFDM) systems by exploiting scattered pilot carriers and virtual carriers embedded in the existing OFDM standards. Assuming that the channel remains constant during two consecutive OFDM blocks and perfect timing, a CFO tracking algorithm is proposed using the limited number of pilot carriers in each OFDM block. Identifiability of this pilot based algorithm is fully discussed under the noise free environment, and a constellation rotation strategy is proposed to eliminate the c-ambiguity for arbitrary constellations. A weighted algorithm is then proposed by considering both scattered pilots and virtual carriers. We find that, the pilots increase the performance accuracy of the algorithm, while the virtual carriers reduce the chance of CEO outlier. Therefore, the proposed tracking algorithni is able to achieve full range CFO estimation, can be used before channel estimation, and could provide improved performance compared to existing algorithms. The asymptotic mean square error (MSE) of the proposed algorithm is derived and simulation results agree with the theoretical analysis. [ABSTRACT FROM AUTHOR]

This paper presents a monitor positioning algorithm to determine the optimal number of power quality monitors and to decide the locations for a given distribution system. From graph theory, the system topology is formulated into a coverage matrix. The weighting factors of each component are newly defined from the guidelines of monitor positioning suggested. Weighting factors represent the degree of importance in the system topology. Ambiguity indices are suggested to evaluate the monitor locations in terms of topological ambiguity. The optimal number and locations are determined from the optimization routine utilizing the weighting factor and the ambiguity indices. The proposed approach can realize the expert's knowledge on monitor positioning into a sophisticated automatic computing algorithm. [ABSTRACT FROM AUTHOR]

In theory, the pose of a calibrated camera can be uniquely determined from a minimum of four coplanar but noncollinear points. In practice, there are many applications of camera pose tracking from planar targets and there is also a number of recent pose estimation algorithms which perform this task in real-time, but all of these algorithms suffer from pose ambiguities. This paper investigates the pose ambiguity for planar targets viewed by a perspective camera. We show that pose ambiguities-two distinct local minima of the according error function-exist even for cases with wide angle lenses and close range targets. We give a comprehensive interpretation of the two minima and derive an analytical solution that locates the second minimum. Based on this solution, we develop a new algorithm for unique and robust pose estimation from a planar target. In the experimental evaluation, this algorithm outperforms four state-of-the-art pose estimation algorithms. [ABSTRACT FROM AUTHOR]

Many generic position-estimation algorithms are vulnerable to ambiguity introduced by nonunique landmarks. Also, the available high-dimensional image data is not fully used when these techniques are extended to vision-based localization. This paper presents the landmark matching, triangulation, reconstruction, and comparison (LTRC) global localization algorithm, which is reasonably immune to ambiguous landmark matches. It extracts natural landmarks for the (rough) matching stage before generating the list of possible position estimates through triangulation. Reconstruction and comparison then rank the possible estimates. The LTRC algorithm has been implemented using an interpreted language, onto a robot equipped with a panoramic vision system. Empirical data shows remarkable improvement in accuracy when compared with the established random sample consensus method. LTRC is also robust against inaccurate map data. [ABSTRACT FROM AUTHOR]

This paper studies the error propagation effect that is caused by certain ambiguities in joint data detection-channel tracking algorithms for transmission diversity schemes. Here, we use a space-time (ST) receiver based on the maximum a posteriori (MAP) method that takes into account the channel estimation error assuming the unknown channel to have a given complex multi-variate Gaussian probability density function (pdf) (i.e., a Ricean channel). The decision criterion that is expressed in quadratic form represents either a linear detector or a noncoherent-nonlinear detector in extreme cases. Then, the channel pdf for the next iteration is updated by estimates of the second-order statistics of the channel coefficients, and a very simple decision-directed adaptive algorithm is derived for adaptive channel estimation. The adaptive algorithm can efficiently track a fast Rayleigh fading channel and, as a result, achieves robust performance. However, the occurrence of two types of ambiguities initiated in deep fades result in error propagation. Some remedies called space-time ambiguity remedies (STARS) are proposed to prevent error propagation. A new time-varying space-time coding (TVST) scheme is suggested as a bandwidth-efficient method to combat the permutation ambiguity impairment. This coding scheme, in conjunction with a differential detector, can resolve the ambiguity problem. [ABSTRACT FROM AUTHOR]