A class of computationally efficient DOA estimators under the Partial Relaxation (PR) framework has recently been proposed. Conceptually different from conventional DOA estimation methods in the literature, the estimators under the PR framework rely on the non-complete relaxation of the array manifold while performing a spectral-search in the field of view. This particular type of relaxation essentially

Wireless communication systems are inherently vulnerable to intentional jamming. In this paper, two classes of such jammers are considered: those with partial and full knowledge. While the first class accounts for those jammers that know the encoding and decoding function, the latter accounts for those that are further aware of the actual transmitted message. Of particular interest are so-called denial-of-service

In the context of supervised learning, meta learning uses features, metadata and other information to learn about the difficulty, behavior, or composition of the problem. Using this knowledge can be useful to contextualize classifier results or allow for targeted decisions about future data sampling. In this paper, we are specifically interested in learning the Bayes error rate (BER) based on a labeled

Compared with traditional learning criteria, such as minimum mean square error (MMSE), the minimum error entropy (MEE) criterion has received increasing attention in the domains of nonlinear and non-Gaussian signal processing and machine learning. Since the MEE criterion is shift-invariant, one has to add a bias to achieve zero-mean error over training datasets. Thus, a modification of the MEE called

In this paper we study the matrix completion problem: Suppose $X \in {\mathbb {R}}^{n_r \times n_c}$ is unknown except for a known upper bound $r$ on its rank. By measuring a small number $m \ll n_r n_c$ of elements of $X$ , is it possible to recover $X$ exactly with noise-free measurements, or approximately with noisy measurements? Existing solutions to these problems involve sampling the elements

We derive robust linear filtering and experimental design for systems governed by stochastic differential equations (SDEs) under model uncertainty. Given a model of signal and observation processes, an optimal linear filter is found by solving the Wiener-Hopf equation; with model uncertainty, it is desirable to derive a corresponding robust filter. This article assumes that the physical process is

Nonnegative Matrix Factorization (NMF) is valuable in many applications of blind source separation, signal processing and machine learning. A number of algorithms that can infer nonnegative latent factors have been developed, but most of these assume a specific noise kernel. This is insufficient to deal with complex noise in real scenarios. In this paper, we present a hierarchical Dirichlet process

Graphs have become a popular toolbox for capturing similarity relationships among data vectors in a variety of networks. Diffusion processes are used to model the data using the graph topology. A novel approach is put forth that exploits data similarity and diffusion models, in a graph, to improve upon the reconstruction mean-square error (MSE) performance of principal component analysis. The tasks

Direct localization systems can outperform two-step localization ones by jointly processing all the measurements observed at base stations. However, such systems encounter large communication and computational load when the signal dimension is high, e.g., with large-scale antenna arrays. In this paper, we establish a general framework for beamspace direct localization, which consists of beamspace design

Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i.e., a radio map )—from limited samples taken sparsely over the region. Classic cartography methods are mostly concerned with recovering the aggregate radio frequency (RF) information while ignoring the constituents of the radio map—but fine-grained emitter-level RF information

In this paper, we propose a cost-aware cascading bandits model, a new variant of multi-armed bandits with cascading feedback, by considering the random cost of pulling arms. In each step, the learning agent chooses an ordered list of items and examines them sequentially, until certain stopping condition is satisfied. Our objective is then to maximize the expected net reward in each step, i.e., the

This paper considers the multi-group multicast beamforming optimization problem, for which the optimal solution has been unknown due to the non-convex and NP-hard nature of the problem. By utilizing the successive convex approximation numerical method and Lagrangian duality, we obtain the optimal multicast beamforming solution structure for both the quality-of-service (QoS) problem and the max-min

Wideband communication is often expected to deal with a very wide spectrum, thus receivers for the wideband communication systems often need to mitigate interferers to reduce the distortion caused by the amplifier nonlinearity and noise. Recently, the random modulation mixes a signal with different pseudorandom sequences using multiple branches of channels before sampling. In previous work, we introduced

We propose two new least mean squares (LMS)-based algorithms for adaptive estimation of graph signals that improve the convergence speed of the LMS algorithm while preserving its low computational complexity. The first algorithm, named extended least mean squares (ELMS), extends the LMS algorithm by virtue of reusing the signal vectors of previous iterations alongside the signal available at the current

Learning discriminative features is difficult for deep learning-based target recognition in synthetic aperture radar (SAR) images with small training samples. To achieve a better feature learning, this study proposes a new deep network, a compact convolutional autoencoder (CCAE) for SAR target recognition. CCAE minimises the reconstruction loss and the distance between intra-class samples simultaneously

Initial relative parameters are of great importance as inputs of the subsequent filter process based on the stereovision system. The relative dynamic model is approximated to estimate the initial relative state. Firstly, the error distribution property of feature point captured using cameras can be approximately regarded to be zero-mean Gaussian distribution when the measurement error of pixel is assumed

The random matrix approach for extended object tracking (EOT) is appealing. This approach assumes that the measurements are linear in the kinematic state and measurement noise. In many practical applications, however, this linear condition cannot always be satisfied. First, this study derives a linearised measurement model. The model first employs a decorrelated unbiased technique to convert the non-linear

This study presents the concept, design, and operation of a low-cost and lightweight multiple-input multiple-output (MIMO) radar system, dedicated to the characterisation of complex volumetric environments such as snow, forest, sand etc. using synthetic aperture radar tomographic focusing. The system consists of six transmitting and six receiving antennas, operated in a multi-static configuration,

Parametric calibration of antenna arrays uses mathematical models of the array manifold with adjustable parameters. In this study, the author shows that the standard model used in that array signal processing literature may not be capable of accurate calibration because of the mismatch between the assumed manifold and the true electromagnetic manifold. To improve calibration quality, the author uses

On account of the different parameters of delay-locked loop, multipath effect and code bandwidth, the Global Navigation Satellite System (GNSS) pseudorange noise of each frequency is different. An unequal-weighted three-carrier ambiguity resolution (TCAR) algorithm for long-baseline static triple-frequency ambiguity resolution is proposed. The algorithm is conducted in cascaded steps. When calculating

Among the existing binary offset carrier (BOC) signal receiving technologies, the dual binary phase-shift keying tracking (DBT), originally designed for alternate BOC (AltBOC) modulated Galileo E5 signal and then extended to standard BOC signals, has its unique characteristics. It incorporates both single-band and dual-band processing schemes into a unified and simple structure to meet both requirements

The authors consider the problem of direction-of-arrival (DOA) estimation for the uniform noise (UN) or non-UN (NUN) in a passive radar. The radar waveform to be estimated is modelled as a deterministic and unknown process. Under this condition, the well-known expectation maximisation (EM) algorithm can be utilised for the estimation. In the conventional EM algorithm, the DOA is updated in every iteration

This study proposes a ground-based threat probability model based on a Markov chain to evaluate multiple ground threats faced by the platform during flight. On this basis, a new threat evaluation and jamming allocation system is constructed by combining the model with jamming resource allocation methods. In the threat evaluation phase, the interaction relationship between the platform and radar is

In some tracking applications, target states are subjected to a trajectory shape constraint, where only the trajectory shape is known a priori. By incorporating the trajectory shape constraint, the tracking performance can be improved. However, the existing state estimation methods with trajectory shape constraints are either of high computational load or of poor robustness due to the nature of the

The statistical characteristic of ocean ambient noise plays an important role in developing underwater signal processors. Considering that the noise in shallow water shows the impulsive nature, non-Gaussian noise models are usually applied to model the ocean ambient noise. In this study, the ocean ambient noise is modelled by using the Middleton Class B model, which can be decomposed into Gaussian

Radar cross-section (RCS) of an object is a complex function of various geometric variables, frequency and angles of incidence. In this work, an artificial intelligence solution is provided to predict the non-deterministic characteristics of RCS using the supervised machine learning algorithm that involves Gaussian process (GP) regression. A parametrised aircraft model is used to generate training

The linear time-varying frequency errors (LTFE) caused by the mismatch of transmitter and receiver oscillators can defocus the imaging result of distributed inverse synthetic aperture radar (ISAR) seriously. The LTFE calibration method based on the entropy minimization principle is sensitive to signal-to-noise ratio (SNR), and its performance is degraded significantly under low SNR conditions. In addition

Most research regarding passive radar exploiting ‘illuminators of opportunity’, such as FM radio, has focused on detecting and tracking targets. This study explores adding automatic target recognition (ATR) capabilities to such systems. The ATR algorithms described here use the radar cross-section (RCS) of potential targets, collected over a short period of time. The received signal model accounts

Using the frequency-division duplex long-term evolution (LTE)-based multistatic passive radar system for unmanned aerial vehicle (UAV) detection is considered. The typical outdoor terrestrial LTE eNodeBs and the broadband direct air-to-ground communications network eNodeBs are used to cover the UAVs at the low altitude and at the medium-high altitude, respectively. The bistatic range measurements are

The ionosphere causes serious phase error in P-band inverse synthetic aperture radar (ISAR) systems, which makes it difficult to obtain a high-quality image. Recently, the convolutional neural network (CNN) has gained much attention in signal processing, and it can automatically extract features to realise an image super-resolution reconstruction. As a popular CNN-based network, U-net can work with

The generalised Pareto distribution is an efficient model to characterise the high-resolution sea clutter. Robust and precise estimation of the model's parameters is a precondition of effective target detection in sea clutter. It is known that the explicit bipercentile (BiP) estimators are efficient in computation and robust to outliers. In this study, it is proved that the explicit BiP estimators

Air-to-air target tracking is a challenging task for practical military and civilian applications. In this study, an unbiased converted measurement Kalman filter in Earth-centred Earth-fixed (ECEF) coordinates (E-UCMKF) is proposed for air-to-air target tracking, which takes account for noisy measurements from the airborne radar and the inertial navigation system/global navigation satellite system

The least-mean-square (LMS) is promising in a system whose signal statistics are time-varying, and it is preferred in adaptive digital predistortion techniques. In a nonlinear memory system, it is found that the high nonlinearity, the finite sampling rate and the finite number of digits will introduce colored noise that is dependent on signals. In this paper, the long-term stability of the conventional

We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new, simple method for model-free online change-point detection that relies only on fast and light recursive statistics, inspired by the classical Exponential Weighted Moving Average algorithm (EWMA). The proposed idea is to

Signal separation and extraction are important tasks for devices recording audio signals in real environments which, aside from the desired sources, often contain several interfering sources such as background noise or concurrent speakers. Blind Source Separation (BSS) provides a powerful approach to address such problems. However, BSS algorithms typically treat all sources equally and do not resolve

Speech dereverberation has been an important component of effective far-field voice interfaces in many applications. Algorithms based on multichannel linear prediction (MCLP) have been shown to be especially effective for blind speech dereverberation and numerous variants have been introduced in the literature. Most of these approaches can be derived from a common framework, where the MCLP problem

Given a matrix $\bf A$ with numerical rank $k$ , the two-sided orthogonal decomposition (TSOD) computes a factorization ${\bf A} = {\bf UDV}^T$ , where ${\bf U}$ and ${\bf V}$ are orthogonal, and ${\bf D}$ is (upper/lower) triangular. TSOD is rank-revealing as the middle factor ${\bf D}$ reveals the rank of $\bf A$ . The computation of TSOD, however, is demanding. In this paper, we present an algorithm

Estimation of the mean of a stochastic variable observed in noise with positive support is considered. It is well known from the literature that order statistics gives one order of magnitude lower estimation variance compared to the best linear unbiased estimator (BLUE). We provide a systematic survey of some common distributions with positive support, and provide derivations of minimum variance unbiased

Efficient mitigation of power amplifier (PA) nonlinear distortion in multi-user hybrid precoding based broadband mmWave systems is an open research problem. In this article, we carry out detailed signal and distortion modeling in broadband multi-user hybrid MIMO systems, with a bank of nonlinear PAs in each subarray, while also take the inevitable crosstalk between the antenna/PA branches into account

This paper deals with the NP-hard quadratic optimization problem under similarity and constant modulus constraints. A computationally efficient iterative algorithm based on the Alternating Direction Penalty Method (ADPM) framework is proposed for continuous and discrete phase cases. In each iteration, it converts the considered problem into two subproblems with closed-form solutions via an introduced

Subspace-based methods suffer from the rank loss of the noise free data covariance matrix in the context of direction of arrival (DOA) estimation of coherent sources. The well-known spatial smoothing techniques are then widely employed to create a rank restored data covariance matrix. However, conventional spatial smoothing techniques, such as the spatial smoothing pre-processing (SSP), modified spatial

Generalized eigenvalue decomposition has many advantages when it is applied in modern signal processing. Compared with other methods, neural network model-based algorithms provide an efficient way to solve such problems online. Generalized feature extraction algorithms based on neural network models have been described in the literature. However, the majority of the existing algorithms can only extract

The min-max problem, also known as the saddle point problem, is a class of optimization problems which minimizes and maximizes two subsets of variables simultaneously. This class of problems can be used to formulate a wide range of signal processing and communication (SPCOM) problems. Despite its popularity, most existing theory for this class has been mainly developed for problems with certain special

The problem of wideband spectrum sensing/sampling in the sub-Nyquist domain is solved in this paper using sparse (low-density) binary-valued measurement matrices. Key objectives are (i) to achieve an efficient compression ratio, and (ii) improve the signal reconstruction performance. We propose a novel RF front-end with parallel branches that we have called Low-Density Wideband Converter (LDWC). We

Decentralized optimization methods often entail information exchange between neighbors. In many circumstances, due to the limited communication bandwidth, the exchanged information has to be quantized. In this paper, we investigate the impact of quantization on the performance of decentralized stochastic optimization. We consider a multi-agent network, in which each node is associated with a stochastic

We present a new method for estimating signal model parameters using the Cumulative Distribution Transform (CDT). Our approach minimizes the Wasserstein distance between measured and model signals. We derive some useful properties of the CDT and show that the resulting estimation problem, while nonlinear in the original signal domain, becomes a linear least squares problem in the transform domain.

This paper presents new pseudolinear estimation algorithms for angle-of-arrival (AOA) target motion analysis when the sensor locations are not precisely known. Sensor location errors can cause significant performance degradation for the existing pseudolinear estimator (PLE) and its variants. In particular, we analytically show that, in the presence of sensor location errors, the well-known weighted

Considering a mixed signal composed of various audio sources and recorded with a single microphone, we consider in this paper the blind audio source separation problem which consists in isolating and extracting each of the sources. To perform this task, nonnegative matrix factorization (NMF) based on the Kullback-Leibler and Itakura-Saito $\beta$ -divergences is a standard and state-of-the-art technique

In this paper, we show that, in vector-to-vector regression utilizing deep neural networks (DNNs), a generalized loss of mean absolute error (MAE) between the predicted and expected feature vectors is upper bounded by the sum of an approximation error, an estimation error, and an optimization error. Leveraging upon error decomposition techniques in statistical learning theory and non-convex optimization

Dual-function radar communication (DFRC) systems implement both sensing and communication using the same hardware. Such schemes are often more efficient in terms of size, power, and cost, over using distinct radar and communication systems. Since these functionalities share resources such as spectrum, power, and antennas, DFRC methods typically entail some degradation in both radar and communication

This paper proposes an improved most likely heteroscedastic Gaussian process (MLHGP) algorithm to handle a kind of nonlinear regression problems involving input-dependent noise. The improved MLHGP follows the same learning scheme as the current algorithm by use of two Gaussian processes (GPs), with the first GP for recovering the unknown function and the second GP for modeling the input-dependent noise

In this study, the weighted integral method for identifying differential equation models is extended to a discrete-time system with a difference equation (DE) model and a finite-length sampled data sequence, and obtain a frequency-domain algorithm for short-time signal analysis and frequency estimation. The derivation consists of three steps. 1) Provide the DE (autoregressive model) with unknown coefficients

A new technique for locating a moving source radiating a wide-band almost-cyclostationary signal is proposed. For this purpose, the signals received on two possibly moving sensors are modeled as jointly spectrally correlated, a new nonstationarity model that allows one to describe the Doppler effect accounting for a time-scale or time-stretch factor in the complex envelopes of the received signals

In this article, we consider groups of agents in a network that select actions in order to satisfy a set of constraints that vary arbitrarily over time and minimize a time varying function of which they have only local observations. The selection of actions, also called a strategy, is causal and decentralized, i.e., the dynamical system that determines the actions of a given agent depends only on the

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We consider the problem of estimating the angles of arrival (AOAs) of multiple sources from a single snapshot obtained by a set of non-coherent sub-arrays, i.e., while the antenna elements in each sub-array are coherent, each sub-array observes a different unknown phase offset. Previous relevant works are based on eigendecomposition of the sample covariance, which requires a large number of snapshots

This paper proposes and analyzes a communication-efficient distributed optimization framework for general nonconvex nonsmooth signal processing and machine learning problems under an asynchronous protocol. At each iteration, worker machines compute gradients of a known empirical loss function using their own local data, and a master machine solves a related minimization problem to update the current

Filter bank-based multicarrier (FBMC) systems have attracted increasing attention recently in view of their many advantages over the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation. However, their more advanced structure (resulting in, for example, self interference) complicates signal processing tasks at the receiver, including synchronization, channel

Spreading the information over all coefficients of a representation is a desirable property in many applications such as digital communication or machine learning. This so-called antisparse representation can be obtained by solving a convex program involving an $\ell _\infty$ -norm penalty combined with a quadratic discrepancy. In this paper, we propose a new methodology, dubbed safe squeezing, to