Showing papers in "IEEE Transactions on Signal Processing in 2022"

Cramér-Rao Bound Optimization for Joint Radar-Communication Beamforming

Abstract: In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks.

KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Abstract: State estimation of dynamical systems in real-time is a fundamental task in signal processing. For systems that are well-represented by a fully known linear Gaussian state space (SS) model, the celebrated Kalman filter (KF) is a low complexity optimal solution. However, both linearity of the underlying SS model and accurate knowledge of it are often not encountered in practice. Here, we present KalmanNet, a real-time state estimator that learns from data to carry out Kalman filtering under non-linear dynamics with partial information. By incorporating the structural SS model with a dedicated recurrent neural network module in the flow of the KF, we retain data efficiency and interpretability of the classic algorithm while implicitly learning complex dynamics from data. We demonstrate numerically that KalmanNet overcomes non-linearities and model mismatch, outperforming classic filtering methods operating with both mismatched and accurate domain knowledge.

Robust Aggregation for Federated Learning

Abstract: We present a novel approach to federated learning that endows its aggregation process with greater robustness to potential poisoning of local data or model parameters of participating devices. The proposed approach, Robust Federated Aggregation (RFA), relies on the aggregation of updates using the geometric median, which can be computed efficiently using a Weiszfeld-type algorithm. RFA is agnostic to the level of corruption and aggregates model updates without revealing each device’s individual contribution. We establish the convergence of the robust federated learning algorithm for the stochastic learning of additive models with least squares. We also offer two variants of RFA: a faster one with one-step robust aggregation, and another one with on-device personalization. We present experimental results with additive models and deep networks for three tasks in computer vision and natural language processing. The experiments show that RFA is competitive with the classical aggregation when the level of corruption is low, while demonstrating greater robustness under high corruption.

Foundations of MIMO Radar Detection Aided by Reconfigurable Intelligent Surfaces

Abstract: A reconfigurable intelligent surface (RIS) is a nearly-passive flat layer made of inexpensive elements that can add a tunable phase shift to the impinging electromagnetic wave and are controlled by a low-power electronic circuit. This paper considers the fundamental problem of target detection in a RIS-aided multiple-input multiple-output (MIMO) radar. At first, a general signal model is introduced, which includes the possibility of using up to two RISs (one close to the radar transmitter and one close to the radar receiver) and subsumes both a monostatic and a bistatic radar configuration with or without a line-of-sight view of the prospective target. Upon resorting to a generalized likelihood ratio test (GLRT), the design of the phase shifts introduced by the RIS elements is formulated as the maximization of the probability of detection in the location under inspection for a fixed probability of false alarm, and suitable optimization algorithms are proposed. The performance analysis shows the benefits granted by the presence of the RISs and shed light on the interplay among the key system parameters, such as the radar-RIS distance, the RIS size, and the location of the prospective target. A major finding is that the RISs should be better deployed in the near-field of the radar arrays at both the transmit and the receive side. The paper is concluded by discussing some open problems and foreseen applications.

Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems

Abstract: Reconfigurable intelligent surface (RIS) is a promising device that can reconfigure the electromagnetic propagation environment through adjustment of the phase shifts of its reflecting elements. However, channel estimation in RIS-aided multiuser multiple-input single-output (MU-MISO) wireless communication systems is challenging due to the passive nature of the RIS and the large number of reflecting elements that can lead to high channel estimation overhead. To address this issue, we propose a novel cascaded channel estimation strategy with low pilot overhead by exploiting the sparsity and the correlation of multiuser cascaded channels in millimeter-wave MISO systems. Based on the fact that the physical positions of the BS, the RIS and users do not appreciably change over multiple consecutive channel coherence blocks, we first estimate the full channel state information (CSI) including all the angle and gain information in the first coherence block, and then only re-estimate the channel gains in the remaining coherence blocks with much lower pilot overhead. In the first coherence block, we propose a two-phase channel estimation method, in which the cascaded channel of one typical user is estimated in Phase I based on the linear correlation among cascaded paths, while the cascaded channels of other users are estimated in Phase II by utilizing the reparameterized CSI of the common base station (BS)-RIS channel obtained in Phase I. The minimum pilot overhead is much less than the existing works. Simulation results show that the performance of the proposed method outperforms the existing methods in terms of the estimation accuracy when using the same amount of pilot overhead.

Improving Sum-Rate of Cell-Free Massive MIMO With Expanded Compute-and-Forward

Abstract: Cell-free massive multiple-input multiple-output (MIMO) employs a large number of distributed access points (APs) to serve a small number of user equipments (UEs) via the same time/frequency resource. Due to the strong macro diversity gain, cell-free massive MIMO can considerably improve the achievable sum-rate compared to conventional cellular massive MIMO. However, the performance of cell-free massive MIMO is upper limited by inter-user interference (IUI) when employing simple maximum ratio combining (MRC) at receivers. To harness IUI, the expanded compute-and-forward (ECF) framework is adopted. In particular, we propose power control algorithms for the parallel computation and successive computation in the ECF framework, respectively, to exploit the performance gain and then improve the system performance. Furthermore, we propose an AP selection scheme and the application of different decoding orders for the successive computation. Finally, numerical results demonstrate that ECF frameworks outperform the conventional CF and MRC frameworks in terms of achievable sum-rate.

Joint Radar and Communications for Frequency-Hopped MIMO Systems

Abstract: Contention in the frequency spectrum has seen the emergence of Dual-Function Radar Communications (DFRC) systems, enabling frequency-hopped (FH) multiple-input-multiple-output (MIMO) waveforms to carry communication symbols. While a variety of novel signaling strategies have been developed to facilitate the communication function, their implementation in slow-time results in the achievable data rate being limited by the radar pulse repetition interval. We develop a generalized framework for performing information embedding in DFRC systems by exploiting the fast-time structure of the transmitted radar waveform. By defining a unified formulation, we show that a variety of existing signaling strategies can be accommodated, including FH index modulation, FH permutation, quadrature amplitude modulation (QAM), M-arry PSK (MPSK) modulation and/or frequency carrier index modulation. In addition, we use this framework to propose hybrid modulation strategies constructed using combinations of the aforementioned schemes to produce significant improvements in the achievable data rate over the individual signaling schemes. Simulation results demonstrate that the hybrid schemes can deliver significantly higher bit rates with only small increases in the required $E_b/N_0$. They also show that, in terms of the impact on the radar operation, the frequency hopping code selection has the highest range sidelobes whereas the PSK schemes suffer from significant spectral leakage. Finally, we also give a discussion of the issues and open problems that remain unaddressed.

Multi-Spectrally Constrained Transceiver Design Against Signal-Dependent Interference

Abstract: This paper focuses on the joint synthesis of constant envelope transmit signal and receive filter aimed at optimizing radar performance in signal-dependent interference and spectrally contested-congested environments. To ensure the desired Quality of Service (QoS) at each communication system, a precise control of the interference energy injected by the radar in each licensed/shared bandwidth is imposed. Besides, along with an upper bound to the maximum transmitted energy, constant envelope (with either arbitrary or discrete phases) and similarity constraints are forced to ensure compatibility with amplifiers operating in saturation regime and bestow relevant waveform features, respectively. To handle the resulting NP-hard design problems, new iterative procedures (with ensured convergence properties) are devised to account for continuous and discrete phase constraints, capitalizing on the Coordinate Descent (CD) framework. Two heuristic procedures are also proposed to perform valuable initializations. Numerical results are provided to assess the effectiveness of the conceived algorithms in comparison with the existing methods.

An Optimal Estimation Framework of Multi-Agent Systems With Random Transport Protocol

Abstract: The state estimation problem of heterogeneous multi-agent systems with random transport protocol is investigated in this paper. Due to the dependency of the agent dynamics and the random sparse structure induced by the random transport protocol, the optimal state estimation design becomes complex and challenging. An optimal state estimator is successfully designed by the Hadamard product and gradient method. Based on the analysis of the matrix functions, a sufficient condition is established to guarantee that the average estimate error covariance is limited. Finally, a numerical example and a smart grid model are utilized to demonstrate the effectiveness of the deigned estimator.

Complex Parameter Rao, Wald, Gradient, and Durbin Tests for Multichannel Signal Detection

Abstract: In the problem of multichannel signal detection, when it comes to the detector design criteria apart from the generalized likelihood ratio test, the traditional method is to cascade the real and imaginary parts of the parameters, and then substitute them into the real parameter statistics. This method is not succinct, and sometimes may be cumbersome and difficult to handle. Recently, a complex parameter Rao test was introduced by Kay and Zhu without the need of cascading the real and imaginary parts of the complex parameters when there is no nuisance parameter. Inspired by this work, we move a further step toward the complex parameter statistics of the Rao, Wald, gradient, and Durbin tests both with and without nuisance parameters, and derive the relationships between their real and complex parameter statistics. Moreover, for a special Fisher information matrix which often holds in practice, we derive a series of simple forms of the complex parameter statistics for the above four criteria, and discuss their application conditions in linear multivariate complex circular Gaussian distribution. Finally, several application examples are given to confirm the proposed schemes.

Mixed Near-Field and Far-Field Localization and Array Calibration With Partly Calibrated Arrays

Abstract: The problem of passive localization of mixed near-field (NF) and far-field (FF) source signals in the presence of array gain-phase uncertainties is addressed. A new algorithm is aimed to use partly calibrated nonuniform linear arrays (NLAs), in which only three sensors have been fully-calibrated. Most of the existing algorithms deal with this problem by exploiting uniform linear arrays (ULAs). Moreover, they assume a simplified source-array model, in which the propagation magnitude scaling is completely neglected and the spatial phase difference is approximated by Taylor’s polynomial. As an opposite, the proposed algorithm is employed to accommodate a more general situation: the exact spatial geometries and nonuniform linear arrays. In the proposed algorithm, three cumulant matrices are firstly defined to construct two matrix pencils. Unambiguous range and angle parameter estimates of the NF sources are then obtained from the generalized eigenvalues of the two defined matrix pencils. After that, these estimates are utilized to calibrate array gain-phase errors. Finally, a spectrum-MUSIC like approach is applied to accomplish the angle estimation for the FF sources. The new algorithm is shown to be readily simple and effective and will be verified both mathematically and numerically.

A Hybrid Approach to Optimal TOA-Sensor Placement With Fixed Shared Sensors for Simultaneous Multi-Target Localization

Abstract: This paper focuses on optimal time-of-arrival (TOA) sensor placement for multiple target localization simultaneously. In previous work, different solutions only using non-shared sensors to localize multiple targets have been developed. Those methods localize different targets one-by-one or use a large number of mobile sensors with many limitations, such as low effectiveness and high network complexity. In this paper, firstly, a novel optimization model for multi-target localization incorporating shared sensors is formulated. Secondly, the systematic theoretical results of the optimal sensor placement are derived and concluded using the A-optimality criterion, i.e., minimizing the trace of the inverse Fisher information matrix (FIM), based on rigorous geometrical derivations. The reachable optimal trace of Cramér-Rao lower bound (CRLB) is also derived. It can provide optimal conditions for many cases and even closed form solutions for some special cases. Thirdly, a novel numerical optimization algorithm to quickly find and calculate the (sub-)optimal placement and achievable lower bound is explored, when the model becomes complicated with more practical constraints. Then, a hybrid method for solving the most general situation, integrating both the analytical and numerical solutions, is proposed. Finally, the correctness and effectiveness of the proposed theoretical and mathematical methods are demonstrated by several simulation examples.

Integrated Waveform Design for MIMO Radar and Communication via Spatio-Spectral Modulation

Abstract: This paper considers the integrated waveform design to simultaneously achieve a desired radar beampattern and multi-users communication for a dual-function Multiple-Input Multiple-Output (MIMO) system. To this end, a spatio-spectral modulation strategy via shaping the spatial waveform Energy Spectral Density (ESD) in directions of communication is proposed for the communication function, while beampattern Integrated Sidelobe Level (ISL) is minimized to enhance radar detectability. Meanwhile, Peak-to-Average Ratio (PAR) and power restrictions to comply with the current hardware technique and the mainlobe width constraint to cohere the beampattern main energy on the spatial region of interest are forced, respectively. Exploiting an equivalent reformulation of the original non-convex optimization problem, a Sequential Block Enhancement (SBE) framework that alternately updates each waveform in each emitting antenna is developed to monotonically decrease ISL. Each block involves the Dinkelbach’s procedure, sequential convex approximation and Alternating Direction Method of Multipliers (ADMM) to obtain single waveform, while the analytic proof with the converged block being a Karush-Kuhn-Tucker (KKT) point is provided. Finally, numerical results highlight the effectiveness of both the proposed dual function scheme and the waveform synthesis technique in comparison with some counterparts.

Unlimited Sampling From Theory to Practice: Fourier-Prony Recovery and Prototype ADC

Abstract: Following the Unlimited Sampling strategy to alleviate the omnipresent dynamic range barrier, we study the problem of recovering a bandlimited signal from point-wise modulo samples, aiming to connect theoretical guarantees with hardware implementation considerations. Our starting point is a class of non-idealities that we observe in prototyping an unlimited sampling based analog-to-digital converter. To address these non-idealities, we provide a new Fourier domain recovery algorithm. Our approach is validated both in theory and via extensive experiments on our prototype analog-to-digital converter, providing the first demonstration of unlimited sampling for data arising from real hardware, both for the current and previous approaches. Advantages of our algorithm include that it is agnostic to the modulo threshold and it can handle arbitrary folding times. We expect that the end-to-end realization studied in this paper will pave the path for exploring the unlimited sampling methodology in a number of real world applications.

Integrated Waveform Design for MIMO Radar and Communication via Spatio-Spectral Modulation

Abstract: This paper considers the integrated waveform design to simultaneously achieve a desired radar beampattern and multi-users communication for a dual-function Multiple-Input Multiple-Output (MIMO) system. To this end, a spatio-spectral modulation strategy via shaping the spatial waveform Energy Spectral Density (ESD) in directions of communication is proposed for the communication function, while beampattern Integrated Sidelobe Level (ISL) is minimized to enhance radar detectability. Meanwhile, Peak-to-Average Ratio (PAR) and power restrictions to comply with the current hardware technique and the mainlobe width constraint to cohere the beampattern main energy on the spatial region of interest are forced, respectively. Exploiting an equivalent reformulation of the original non-convex optimization problem, a Sequential Block Enhancement (SBE) framework that alternately updates each waveform in each emitting antenna is developed to monotonically decrease ISL. Each block involves the Dinkelbach’s procedure, sequential convex approximation and Alternating Direction Method of Multipliers (ADMM) to obtain single waveform, while the analytic proof with the converged block being a Karush-Kuhn-Tucker (KKT) point is provided. Finally, numerical results highlight the effectiveness of both the proposed dual function scheme and the waveform synthesis technique in comparison with some counterparts.

Direction-of-Arrival Estimation for Large Antenna Arrays With Hybrid Analog and Digital Architectures

Abstract: The large antenna arrays with hybrid analog and digital (HAD) architectures can provide a large aperture with low cost and hardware complexity, resulting in enhanced direction-of-arrival (DOA) estimation and reduced power consumption. This paper investigates the trade-off between DOA estimation and power consumption in large antenna arrays with HAD architectures. Particularly, the DOA estimation problem of fully-connected, sub-connected (SC), and switches-based (SE) hybrid architectures is formulated into a unified expression, with the compression matrix in a time-varying form. Based on this model, we derive a dynamic maximum likelihood (D-ML) estimator that is suitable for both HAD and conventional fully digital (FD) structures, and the closed-form expression of Cramér-Rao bound (CRB) to evaluate the performance limit of the D-ML estimator for different HAD structures. The theoretical CRB analysis in the single-source case reveals that, the SC structure has the ability to achieve approximately the same performance as the FD structures at DOAs around zero, but suffers from the inherent angle ambiguity because of the antenna grouping. In addition, we propose a dynamic SC (D-SC) structure that is proved to eliminate the angle ambiguity with time-varying phase shifters, and a switch optimization (SWO) algorithm to minimize the CRB of SE structures. Finally, we introduce a new metric, DOA efficiency, to measure the trade-off between the DOA estimation performance and power consumption of different structures. Simulation results verify our theoretical analysis and the superiority of the proposed D-SC structure and the SWO algorithm.

Computational Array Signal Processing via Modulo Non-Linearities

Abstract: Conventionalliterature on array signal processing (ASP) is based on the “capture first, process later” philosophy and to this end, signal processing algorithms are typically decoupled from the hardware. This poses fundamental limitations because if the sensors result in information loss, the algorithms may no longer be able to achieve their guaranteed performance. In this paper, our goal is to overcome the barrier of information loss via sensor saturation and clipping. This is a significant problem in application areas including physiological monitoring and extra-terrestrial exploration where the amplitudes may be unknown or larger than the dynamic range of the sensor. To overcome this fundamental bottleneck, we propose “computational arrays” which are based on a co-design approach so that a collaboration between the sensor array hardware and algorithms can be harnessed. Our work is inspired by the recently introduced unlimited sensing framework. In this context, our computational arrays encode the high-dynamic-range information by folding the signal amplitudes, thus introducing a new form of information loss in terms of the modulo measurements. On the decoding front, we develop mathematically guaranteed recovery algorithms for spatio-temporal array signal processing tasks that include DoA estimation, beamforming and signal reconstruction. Numerical examples corroborate the applicability of our approach and pave a path for the development of novel computational arrays for ASP.

Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray

Abstract: In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array structure and coprime frequency offsets, a two-dimensional space-frequency virtual difference coarray corresponding to uniform array and uniform frequency offset is considered to increase the number of degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz covariance matrix is computationally prohibitive. To solve this problem, we propose an interpolation algorithm based on decoupled atomic norm minimization (DANM), which converts the coarray signal to a simple matrix form. On this basis, a relaxation-based optimization problem is formulated to achieve joint DoA-range estimation with enhanced DoFs. The reconstructed coarray signal enables application of existing subspace-based spectral estimation methods. The proposed DANM problem is further reformulated as an equivalent rank-minimization problem which is solved by cyclic rank minimization. This approach avoids the approximation errors introduced in nuclear norm-based approach, thereby achieving superior root-mean-square error which is closer to the Cramer-Rao bound. The effectiveness of proposed method is confirmed by theoretical analyses and numerical simulations.

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Abstract: The conventional direction of arrival (DOA) estimation methods are performed with multiple receiving channels. In this paper, a changeling DOA estimation problem is addressed in a different scenario with only one full-functional receiving channel. A new unmanned aerial vehicle (UAV) swarm system using multiple lifted reconfigurable intelligent surface (RIS) is proposed for the DOA estimation. The UAV movement degrades the DOA estimation performance significantly, and the existing atomic norm minimization (ANM) methods cannot be used in the scenario with array perturbation. Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation. Then, a customized semi-definite programming (SDP) method is derived to solve the atomic norm-based method, where different from the traditional SDP method, an additional transforming matrix is formulated. Moreover, a gradient descent method is applied to refine the estimated DOA and the position perturbation further. Simulation results show that the proposed method achieves much better DOA estimation performance in the RIS-aided UAV swarm system with only one receiving channel than various benchmark schemes.

MIMO Multifunction RF Systems: Detection Performance and Waveform Design

Abstract: This paper studies the detection performance of a multiple-input-multiple-output (MIMO) multifunction radio frequency (MFRF) system, which simultaneously supports radar, communication, and jamming. We show that the detection performance of the MIMO MFRF system improves as the transmit signal-to-interference-plus-noise-ratio (SINR) increases. To analyze the achievable SINR of the system, we formulate an SINR maximization problem under the communication and jamming functionality constraint as well as a transmit energy constraint. We derive a closed-form solution of this optimization problem for energy-constrained waveforms and present a detailed analysis of the achievable SINR. Moreover, we analyze the SINR for systems transmitting constant-modulus waveforms, which are often used in practice. We propose an efficient constant-modulus waveform design algorithm to maximize the SINR. Numerical results demonstrate the capability of a MIMO array to provide multiple functions, and also show the tradeoff between radar detection and the communication/jamming functionality.

Intelligent Reflecting Surface Enabled Sensing: Cramér-Rao Bound Optimization

Abstract: This article investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is deployed to assist an access point (AP) to sense a target at its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. We consider two types of target models, namely the point and extended targets, for which the AP aims to estimate the targets direction-of-arrival (DoA) and the target response matrix with respect to the IRS, respectively, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cramér-Rao bound (CRB) on the estimation error. Towards this end, we first obtain the CRB expressions in closed form. It is shown that for the point target, the CRB for estimating the DoA depends on both the transmit and reflective beamformers; while for the extended target, the CRB for estimating the target response matrix only depends on the transmit beamformers. Next, we optimize the joint beamforming design to minimize the CRB for the point target via alternating optimization, semi-definite relaxation, and successive convex approximation. We also obtain the optimal transmit beamforming solution in closed form to minimize the CRB for the extended target. Numerical results show that for both cases, the proposed designs based on CRB minimization achieve improved sensing performances than other traditional schemes.

Outlier-Resistant Filtering With Dead-Zone-Like Censoring Under Try-Once-Discard Protocol

Abstract: In this paper, a novel outlier-resistant filtering problem is concerned for a class of networked systems with dead-zone-like censoring under the weighted try-once-discard protocol (WTODP). To describe the phenomenon of dead-zone-like censoring, the sensor output is characterized by the Tobit model in which the censored region is restrained by specified left- and right-censoring thresholds. The WTODP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. In the case of the measurement outliers, a saturation function is employed in the Tobit Kalman filter structure to constrain the innovations contaminated by the measurement outliers, thereby maintaining satisfactory filtering performance. By resorting to the approach of the matrix inequality, an upper bound is first obtained on the filtering error covariance where the gain matrix of the Tobit Kalman filter is carefully designed to minimize the obtained upper bound. Moreover, the exponential boundedness of the filtering error is analyzed in the mean square sense. Finally, the effectiveness of the proposed outlier-resistant filtering algorithm is verified by three practical examples.

Learned Factor Graphs for Inference From Stationary Time Sequences

Abstract: The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed one. A broad family of model-based algorithms have been derived to carry out inference at controllable complexity using recursive computations over the factor graph representing the underlying distribution. An alternative model-agnostic approach utilizes machine learning (ML) methods. Here we propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences. In the proposed approach, neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence, rather than the complete inference task. By exploiting stationary properties of this distribution, the resulting approach can be applied to sequences of varying temporal duration. Learned factor graphs can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems. We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data, and can be applied to sequences of different lengths. Our experimental results demonstrate the ability of the proposed learned factor graphs to learn from small training sets to carry out accurate inference for sleep stage detection using the Sleep-EDF dataset, as well as for symbol detection in digital communications with unknown channels.

An Outlier-Robust Kalman Filter With Adaptive Selection of Elliptically Contoured Distributions

Abstract: In this paper, elliptically contoured (EC) distributions are used to model outlier-contaminated measurement noises. Exploiting a heuristic approach to introduce an unknown parameter, we present an analytical update form of the joint posterior probability density function of the state vector and auxiliary random variable, from which a novel robust EC distributions-based Kalman filtering framework is first derived. To illustrate the effectiveness of the proposed framework, the convergence, robustness, optimality and computational complexity analyses of the proposed method are then given. In addition, to cope with complex noise environments, the interaction multiple model is employed to achieve the adaptive selection of EC distributions such that well-behaved estimation performance can be obtained for different noise cases. Simulation results demonstrate the validity and superiority of the proposed algorithm.

A Secure Communication Scheme of Three-Variable Chaotic Coupling Synchronization Based on DNA Chemical Reaction Networks

Abstract: Three-variable chaotic synchronization has been implemented based on DNA chemical reaction networks (CRNs) in previous works. However, it is still difficult to realize the encryption and decryption of continuous signals. A scheme of secure communication is proposed based on chaotic coupling synchronization by DNA CRNs in this paper. Firstly, the CRNs of two three-variable chaotic systems are constructed using bimolecular representation. Secondly, according to the design principle of the coupling term and the theory of stability, the CRNs of the coupling term are designed to realize the synchronization of two chaotic systems. Finally, the oblique wave, square wave, and staircase wave signals are added into chaotic systems to realize encryption and decryption. The dynamic behavior of a three-variable chaotic system is verified by Visual DSD and MATLAB software. The synchronization proof and the experimental results of encryption and decryption are given. The results indicate that the proposed secure communication scheme is effective. This paper provides an application method for studying secure communication in the DNA field.

Joint Detection Threshold Optimization and Illumination Time Allocation Strategy for Cognitive Tracking in a Networked Radar System

Abstract: In this paper, a joint detection threshold optimization and illumination time allocation (JDTOITA) strategy was developed for multi-target tracking in an asynchronous networked radar system under cluttered background. The basis of this strategy is to facilitate detection and tracking using the prior target information in the tracking recursive cycle. The information reduction factor in the Bayesian detection framework is derived, optimized, and incorporated in the posterior Cramer-Rao lower bound (PCRLB), which is then utilized to serve as the optimization metric. Due to the asynchronous data and cluttered environment, the objective function needs to be recursively deduced and is nonlinear and nonconvex. We propose an efficient solver integrating the convex relaxation with the local search technique for this problem solving. Simulation results demonstrate the superiority of the JDTOITA strategy compared with the benchmarks with no optimization or optimization of either the illumination time allocation (ITA) or detection threshold alone. The results also imply that the target reflectivity and sampling interval of local radars are two important factors that influence the resource optimization.

Joint DoA-Range Estimation Using Space-Frequency Virtual Difference Coarray

Abstract: In this paper, we address the problem of joint direction-of-arrival (DoA) and range estimation using frequency diverse coprime array (FDCA). By incorporating the coprime array structure and coprime frequency offsets, a two-dimensional space-frequency virtual difference coarray corresponding to uniform array and uniform frequency offset is considered to increase the number of degrees-of-freedom (DoFs). However, the reconstruction of the doubly-Toeplitz covariance matrix is computationally prohibitive. To solve this problem, we propose an interpolation algorithm based on decoupled atomic norm minimization (DANM), which converts the coarray signal to a simple matrix form. On this basis, a relaxation-based optimization problem is formulated to achieve joint DoA-range estimation with enhanced DoFs. The reconstructed coarray signal enables application of existing subspace-based spectral estimation methods. The proposed DANM problem is further reformulated as an equivalent rank-minimization problem which is solved by cyclic rank minimization. This approach avoids the approximation errors introduced in nuclear norm-based approach, thereby achieving superior root-mean-square error which is closer to the Cramér-Rao bound. The effectiveness of the proposed method is confirmed by theoretical analyses and numerical simulations.

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Abstract: The conventional direction of arrival (DOA) estimation methods are performed with multiple receiving channels. In this paper, a changeling DOA estimation problem is addressed in a different scenario with only one full-functional receiving channel. A new unmanned aerial vehicle (UAV) swarm system using multiple lifted reconfigurable intelligent surface (RIS) is proposed for the DOA estimation. The UAV movement degrades the DOA estimation performance significantly, and the existing atomic norm minimization (ANM) methods cannot be used in the scenario with array perturbation. Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation. Then, a customized semi-definite programming (SDP) method is derived to solve the atomic norm-based method, where different from the traditional SDP method, an additional transforming matrix is formulated. Moreover, a gradient descent method is applied to refine the estimated DOA and the position perturbation further. Simulation results show that the proposed method achieves much better DOA estimation performance in the RIS-aided UAV swarm system with only one receiving channel than various benchmark schemes.

Multivariate Fast Iterative Filtering for the Decomposition of Nonstationary Signals

Abstract: In this work, we present a new technique for the decomposition of multivariate data, which we call Multivariate Fast Iterative Filtering (MvFIF) algorithm. We study its properties, proving rigorously that it converges in finite time when applied to the decomposition of any kind of multivariate signal. We test MvFIF performance using a wide variety of artificial and real multivariate signals, showing its ability to: separate multivariate modulated oscillations; align frequencies along different channels; produce a quasi–dyadic filterbank when decomposing white Gaussian noise; decompose the signal in a quasi–orthogonal set of components; being robust to noise perturbation, even when the number of channels is increased considerably. Finally, we compare it and its performance with the main methods developed so far in the literature, proving that MvFIF produces, without any a priori assumption on the signal under investigation and in a fast and reliable manner, a uniquely defined decomposition of any multivariate signal.

A Novel Wireless Communication Paradigm for Intelligent Reflecting Surface Based Symbiotic Radio Systems

Abstract: This paper investigates a novel intelligent reflecting surface (IRS)-based symbiotic radio (SR) system architecture consisting of a transmitter, an IRS, and an information receiver (IR). The primary transmitter communicates with the IR and at the same time assists the IRS in forwarding information to the IR. Based on the IRS’s symbol period, we distinguish two scenarios, namely, commensal SR (CSR) and parasitic SR (PSR), where two different techniques for decoding the IRS signals at the IR are employed. We formulate bit error rate (BER) minimization problems for both scenarios by jointly optimizing the active beamformer at the base station and the phase shifts at the IRS, subject to a minimum primary rate requirement. Specifically, for the CSR scenario, a penalty-based algorithm is proposed to obtain a high-quality solution, where semi-closed-form solutions for the active beamformer and the IRS phase shifts are derived based on Lagrange duality and Majorization-Minimization methods, respectively. For the PSR scenario, we apply a bisection search-based method, successive convex approximation, and difference of convex programming to develop a computationally efficient algorithm, which converges to a locally optimal solution. Simulation results demonstrate the effectiveness of the proposed algorithms and show that the proposed SR techniques are able to achieve a lower BER than benchmark schemes.