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• The proposed scheme is the first work for ambiguity attack of SDM watermarking. • The implementation of the scheme only requires a small cost. • Our method does not need the original training data. • The proposed method is effective to various SDM watermarking. In recent years, the text-to-image diffusion models have achieved excellent performance. Among them, stable diffusion models (SDMs) have become one of the most widely used models because of their excellent performance. Scholars have proposed many model watermarking techniques to protect the copyright of the text-to-image diffusion models. In order to measure the security and potential risks of the existing text-to-image diffusion model watermarking techniques, an ambiguity attack against the text-to-image diffusion model watermarking is proposed for the first time in this paper. Specifically, we take the SDMs as an example, take advantage of the reversibility of the model watermarking and combine the ideas of adversarial examples and discrete prompt optimization to re-embed a forged watermark in the watermarked SDMs, thus confounding the watermark containing copyright information. A large number of experiments show that our ambiguity attack is effective and can make the original watermark lose its uniqueness without changing the watermarked text-to-image diffusion models. [ABSTRACT FROM AUTHOR]

The Wigner–Ville distribution (WD) and ambiguity function (AF) are serviceable tools for the global analysis of non-stationary signals. However, when dealing with time-varying signals that exhibit changing characteristics and require real-time signal processing, they have high limitations due to the existence of cross-items. In this paper, we propose the short-time Wigner–Ville distribution (STWD) as a novel approach that effectively analyzes time-varying signals with changing characteristics. First, the definition of STWD and short-time ambiguity function (STAF) and their properties are advanced. Next, three uncertainty principles that define the lower bound for the product of signal spread and its bandwidth are derived. Then, the discrete STWD (DSTWD) and the process of computerization are also proposed which can be used more widely in practice. Finally, the optimization of window functions based on Rényi entropy is discussed. Simulation experiments show that the STWD can be effectively applied for the analysis of local change characteristics and real-time processing of signals with valid and accurate results. • The short-time Wigner-Ville distribution (STWD) is proposed. • Three uncertainty principles of the STWD are derived. • Discrete STWD and window function optimization based on Rényi entropy are discussed. [ABSTRACT FROM AUTHOR]

For pulse Doppler radars, targets become undetectable for Doppler frequencies in the detection-blind range occupied by the clutter Doppler spectrum. To overcome this problem, different Pulse Repetition Frequencies (PRFs) are used to search for targets with Doppler ambiguity and estimate their velocity, which is required for target tracking. Owing to the different PRFs used for velocity estimation, many beam dwells are required for illuminating the targets, Doppler compensation cannot be realized in a short time. In this paper, we propose a novel velocity estimation method that uses multicarrier signals in a single dwell. First, we obtain separated subcarrier signals from the echo signal and multiply some separated subcarrier signals by other separated subcarrier signals in a conjugate manner, obtaining Frequency-Converted Subcarrier (FCS) signals. We derive the potential ambiguous and unambiguous Doppler frequencies for the separated subcarrier and FCS signals, respectively, using a slow-time Discrete Fourier Transformation (DFT). Subsequently, the actual Doppler ambiguity number is derived from the results. Finally, the unambiguous Doppler frequency and target velocity are inferred. The effectiveness and superiority of the proposed method are validated by performing simulation studies. • Extending the unambiguous Doppler range. • Simple implementation. • Solving Doppler ambiguity at a PRF. [ABSTRACT FROM AUTHOR]

The coexistence of clutter range ambiguity and range dependence in the airborne forward-looking radar may render the common space–time adaptive processing (STAP) ineffective. To cope with this challenge, a novel vertical element-pulse coding (EPC) radar is proposed in our paper. Note that both the elevation angles and coding factors determine the transmitted spatial frequency of the proposed vertical EPC radar. Therefore, the clutters corresponding to different ambiguous areas can be distinguished in the spatial frequency dimension based on this property. The element-pulse decoding and pre-STAP filtering are devised to separate and extract these clutters corresponding to different ambiguous areas. In the following, the clutter elimination is independently accomplished for each ambiguous area that has been separated. Consequently, the clutter range ambiguity as well as clutter range dependence are addressed simultaneously. The efficacy of the proposed approach is verified with simulation experiments. • A general signal model of vertical EPC array radar is established. • Clutter properties are analyzed in the vertical spatial frequency domain. • Range-ambiguous clutter is separated using element-pulse decoding and pre-filtering. • The design criteria of the element-pulse coding factor are derived. [ABSTRACT FROM AUTHOR]

Range ambiguity is a significant challenge in conventional pulse-Doppler radar systems with medium or high pulse repetition frequencies, greatly impacting radar detection and imaging capabilities. The classic approaches such as waveform diversity and compressed sensing are based on the isolation between pulses. However, these methods encounter issues like residual ambiguity and grid mismatch, resulting in poor range ambiguity suppression performance. In this paper, the multiple signal classification-alternating projection (MUSIC-AP) method is proposed to address these challenges and achieve high-resolution estimation. The proposed method converts the range-velocity estimate into a spectrum estimate, enabling the utilization of the MUSIC algorithm to solve the grid mismatch problem and improve echo reconstruction. Then, to mitigate the effect of ambiguous energy on reconstruction, the MUSIC reconstruction subprocess is embedded into the AP framework. Through iterative approximation, the method gradually suppresses ambiguous and noise energy, thereby further enhancing the echo reconstruction accuracy and range ambiguity suppression performance. Simulations demonstrate the effectiveness of the proposed method. Moreover, algorithm analysis is conducted, considering aspects such as resolution, robustness, and sparsity, which provides a basis for parameter selection and applicability conditions. [Display omitted] • The range-velocity estimation is transformed into spectrum estimation problem. • Unambiguous range-velocity high-resolution estimation via the MUSIC algorithm. • The MUSIC is embedded into AP outer frame to gradually suppress ambiguity and noise. [ABSTRACT FROM AUTHOR]

• Phase ambiguity problem limits the development of broadband direction finding. • Constructing virtual arrays with different aperture sizes can solve the problem. • Multi-layer convolutional operation can construct virtual arrays with different sizes. • Error backpropagation algorithm can optimize the combination of virtual arrays. As the electromagnetic environment becomes more complex, radio direction finding systems need to cover a wide frequency range. However, the phase ambiguity of high frequency limits the development of broadband direction finding technology. The existing methods in solving the phase ambiguity problem heavily rely on designing the precise actual sparse array structure by theoretical derivation, which is expensive and time-consuming. To solve this problem, we propose a broadband direction finding method based on constructing virtual sparse arrays. First, we perform the convolutional operation on the direction feature matrix to achieve the weighted summation of the original baselines, which generates numerous virtual array elements and virtual baselines with different directions. The virtual array elements are combined to form virtual sparse arrays with different aperture sizes. Second, we optimize the parameters through the error backpropagation algorithm and obtain the optimal combination of virtual sparse arrays, which eliminates the phase ambiguity. Meanwhile, the virtual sparse arrays can maintain the advantages of sparse arrays, which can accomplish high-precision broadband direction finding. The experimental results demonstrate that the proposed method can solve the phase ambiguity problem effectively. It has high accuracy and strong robustness in simulation environment. [ABSTRACT FROM AUTHOR]

• Combining the advantages of the phase-coded signal and LFM signal, a waveform with good Doppler tolerance and low sidelobe levels is designed. • The shape of the ambiguity function is designed directly without similarity constraints to obtain better degrees of freedom. • A fractional programming problem is constructed to minimize the ratio of the peak sidelobe level to the minimum mainlobe level in order to obtain good Doppler tolerance and low peak sidelobe levels. • Experimental results demonstrate that the designed waveform is applicable for high-speed moving target detection. Although the thumbtack-type ambiguity functions (AF) of the existing phase-coded signals are of good range-Doppler resolutions, they are not suitable for detection of high-speed targets due to their poor Doppler tolerance. In this paper, we address the design of waveform with good Doppler tolerance, which is expected to be of both diagonal-ridge-type AF and also local low peak sidelobe level (PSL). First we formulate the design model of Doppler tolerant waveform via minimizing the ratio of PSL to the minimum mainlobe level. Then, we solve the resulting fractional programming problem via quartic polynomial transfer and high-order polynomial optimization. Numerical results demonstrate the excellent Doppler tolerance and low PSL performance of our design. [ABSTRACT FROM AUTHOR]

• A generalized ambiguity function (GAF) signal model of FDA radar based on transmitting beamspace is established. • Based on the proposed FDA radar GAF signal model, its range resolution, angular resolution, and Doppler resolution are analyzed. • A method to improve the performance of FDA radar GAF by optimizing the transmitting beamspace is proposed, and its correctness is verified by simulation. • The transmitting beamspace optimization problem is modeled as a quasi-convex optimization problem, which can be efficiently solved using Matlab's CVX toolbox. The ambiguity function is a conventional method to evaluate the performance of radar waveform. Currently, the existing letter only evaluation the Frequency Diversity Array (FDA) radar generalized ambiguity function (GAF) that transmits coherent or orthogonal waveforms. No literature discusses the GAF performance of FDA radar transmission arbitrary waveforms. We implement a general GAF performance evaluation with the transmitting beamspace-based FDA radar. Then, we improve the performance of the GAF through beamspace optimization by semidefinite programming (SDP), essentially the waveform design of the FDA radar. The theoretical analysis and numerical calculation results show that the FDA radar can improve its range and angular resolution by optimizing the emission waveform. [ABSTRACT FROM AUTHOR]

• The proposed method can simultaneously correct the complex DFBs with a one-step process of matrix multiplication, thereby simplifying the corrections of different order DFBs. • The improved time-scaled transform is proposed to correct the linear RCM without searching the Doppler ambiguity number. • The proposed method achieves well-focused result without searching and estimating the target's unknown parameters. • The proposed method is simple to implement and computationally efficient. Maneuvering target presents a great threat for radar detection applications. The complex unknown motions between radar and maneuvering target seriously affect the performance of target coherent integration. Existing methods utilizing parameter search or estimation are limited by the high computational burden and the propagation errors of different parameter estimations. In this study, a fast nonparametric estimation method is proposed for coherent integration of maneuvering target. Firstly, the second-order Keystone transform is adopted to eliminate the quadratic range cell migration (RCM). Secondly, the modified range frequency reversal process is proposed to simultaneously remove the complex low- and high-order Doppler frequency broadenings (DFBs) by a one-step matrix multiplication operation. Subsequently, the residual RCM is corrected by the proposed improved time-scaled transform without searching for the Doppler ambiguity number, and then a well-focused result can be obtained. The proposed method is simple to implement and computationally efficient given that searching and estimation of parameters are avoided. It also addresses the high-order DFB and blind speed sidelobe. The processing results of simulated and real data are presented to verify the performance of the presented approach. [ABSTRACT FROM AUTHOR]

• A novel coupling KF problem of model uncertainty and non-Gaussian noise is solved. • A novel MEE criterion minimax Wasserstein distribution Kalman Filter is proposed. • The proposed algorithm is proven to be convergent. • The proposed algorithm is effectiveness verified in typical simulation scenarios. Recently, the divergence-based minimax approach was proposed and introduced to robust Kalman filtering under model uncertainty and Gaussian noise, and it significantly outperformed the standard Kalman filter. In this study, we consider a new Wasserstein-distribution based Kalman filter in which model uncertainty and non-Gaussian noise exist simultaneously. First, we construct a joint Gaussian distribution of states and observations in state space. Then, we set the equivalent estimator joint Gaussian distribution as a Wasserstein ambiguity set allowable neighborhood centered on the true distribution. Thus, the estimator distribution problem is transformed into a minimax problem, and the model uncertainty is tolerated accordingly. Subsequently, we introduce minimum error entropy to optimize the minimax problem based on Wasserstein ambiguity sets, so as to handle the influence of non-Gaussian noise. The minimax problem constrained by the minimum error entropy is transformed into a semi-positive definite convex optimization problem. By constructing two iterative sub-problems that are mutually conditional, the nonlinear semi-definite program finite convex optimization problem is solved. Finally, the minimum error entropy Wasserstein distribution Kalman filter algorithm is proposed. Additionally, the convergence of the proposed algorithm is clarified, and its effectiveness verified by comparing a series of algorithms in typical simulation scenarios. [ABSTRACT FROM AUTHOR]

In modern electronic warfare, artificial repeat jamming from hostile jammers and range-Doppler sidelobe interference from strong scatterers seriously affect target detection performance. Most of the existing anti-jamming methods are proposed for one of them. In this paper, we propose a pulse diversity phase coded (PDPC) waveform design scheme based on periodic ambiguity function (PAF) shaping. This method can minimize the jamming energy in the target detection area and has significant applications in the radar anti-jamming field. We first define the PDPC waveform and analyze its characteristics. On this basis, the principle of jamming suppression based on PDPC waveform PAF shaping is discussed. Further, a closed-loop confrontation method including jamming cognition and suppression is proposed. Simulation examples demonstrate that the proposed method can comprehensively call waveform optimization resources to suppress two kinds of jamming simultaneously and possess better jamming suppression performance. [ABSTRACT FROM AUTHOR]

• Critical survey of existing performance indices. • Introduce an index, called CorrIndex, whose reliability is proved theoretically. • Provides interpretable upper and lower bounds, with a low computational cost. • Possible applications include Blind Source Separation or Tensor Decompositions. Permutation and scaling ambiguities are relevant issues in tensor decomposition and source separation algorithms. Although these ambiguities are inevitable when working on real data sets, it is preferred to eliminate these uncertainties for evaluating algorithms on synthetic data sets. As shown in the paper, the existing performance indices for this purpose are either greedy and unreliable or computationally costly. In this paper, we propose a new performance index, called CorrIndex, whose reliability can be proved theoretically. Moreover, compared to previous performance indices, it has a low computational cost. Theoretical results and computer experiments demonstrate these advantages of CorrIndex compared to other indices. [ABSTRACT FROM AUTHOR]

• Nonlinear distortions are important issues in many communications systems. This work proposes original blind and semi-blind solutions for the identification of nonlinear, finite alphabet, multichannel systems. • The proposed solutions rely on the maximum likelihood approach and are based on the EM-algorithm. The blind one is initialized by a subspace method combined with an original ambiguity removal technique. They lead to a significant performance gain as compared to the state-of-the-art. • We provide theoretical performance bounds and identifiability results for the considered problem and show how our solutions can be extended to a large class of nonlinear models. • Our methods, not only outperform the existing solutions in the literature but also, allow to achieve channel estimation and data detection simultaneously. Nonlinear distortions are important issues in many communications systems. Therefore, this paper deals with the blind and semi-blind identification of nonlinear SIMO/MIMO channels. Quadratic and cubic nonlinearities are considered for the system model as well as a discussion on how the developed work can be extended to more general nonlinear models. The proposed blind solution is initialized by using a subspace approach, which is followed by an appropriate ambiguity removal method, then refined by a Maximum Likelihood (ML) based processing using the Expectation-Maximization (EM) algorithm. The proposed semi-bind solution, involving both data and pilots, is fully based on the EM algorithm. These solutions are supported by some identifiability results and performance bounds analysis related to the considered models (blind and semi-blind). Finally, simulation results essentially show that the proposed algorithms exhibit very attractive channel estimation performance, with interesting convergence speed for the EM-based iterative processing. [ABSTRACT FROM AUTHOR]