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In this paper, we present a new nonlinear filter for high-dimensional state estimation, which we have named the cubature Kalman filter (CKF). The heart of the CKF is a spherical-radial cubature rule, which makes it possible to numerically compute multivariate moment integrals encountered in the nonlinear Bayesian filter. Specifically, we derive a third-degree spherical-radial cubature rule that provides a set of cubature points scaling linearly with the state-vector dimension. The CKF may therefore provide a systematic solution for high-dimensional nonlinear filtering problems. The paper also includes the derivation of a square-root version of the CKF for improved numerical stability. The CKF is tested experimentally in two nonlinear state estimation problems. In the first problem, the proposed cubature rule is used to compute the second-order statistics of a nonlinearly transformed Gaussian random variable. The second problem addresses the use of the CKF for tracking a maneuvering aircraft. The results of both experiments demonstrate the improved performance of the CKF over conventional nonlinear filters. Index Terms--Bayesian filters, cubature rules, Gaussian quadrature rules, invariant theory, Kalman filter, nonlinear filtering.

A square-root extension of the quadrature Kalman filter using matrix triangularizations is discussed. Excellent numerical characteristics are observed in the square-root quadrature Kalman filter.

We present a novel wideband multiple-input, multiple-output (MIMO) channel model, which we refer to as the structured model. The structured model is based on the eigenvalue decomposition (EVD) of the wideband channel correlation matrix. It does not assume the scatterers at the receiver fade independently of those at the transmitter. It also models correlation between delay bins in the power delay profile (PDP). With preliminary data gathered using McMaster's wideband MIMO Software Defined Radio (the WMSDR) in fixed outdoor locations, and Brigham Young University's (BYU) wideband channel sounder in fixed indoor locations, we show good agreement between modeled and measured data. The two platforms used and the environments in which the data were collected were very different. The proposed model performed equally well with both data sets, demonstrating its robustness. Index Terms--Antenna arrays, channel capacity, channel modeling, measured channel data, multiple-input multiple-output (MIMO), wideband.

Modeling wireless channels is essential to wireless communication systems. An autoregressive (AR) process of order one for wireless channel has long been assumed, but without a rigorous mathematical/physical basis. In this paper, we derive a first-order stochastic AR model for a flat stationary wireless channel, which comes from stochastic differential equation (SDE) theory concerning the nature of multipath fading channels. The resulting AR model describes more of the origin of multipath fading channels than previous AR models, and it can efficiently model and generate Rayleigh-distributed stationary fading channels. The Markovian property of the AR model is inherited through the SDE approach. Index Terms--Autoregressive (AR) process, multipath channels, Rayleigh, stochastic differential equation (SDE).

Recurrent neural networks (RNNs) have been successfully applied to communications channel equalization because of their modeling capability for nonlinear dynamic systems. Major problems of gradient-descent learning techniques commonly employed to train RNNs are slow convergence rates and long training sequences required for satisfactory performance. This paper presents decision-feedback equalizers using an RNN trained with Kalman filtering algorithms. The main features of the proposed recurrent neural equalizers, using the extended Kalman filter (EKF) and unscented Kalman filter (UKF), are fast convergence and good performance using relatively short training symbols. Experimental results for various time-varying channels are presented to evaluate the performance of the proposed approaches over a conventional recurrent neural equalizer. Index Terms--Channel equalization, extended Kalman filter (EKF), recurrent neural network (RNN), time-varying channel, unscented Kalman filter (UKF).

Stochastic correlative learning, as the basis for a broadly defined class of statistical learning algorithms, collectively known as the algorithm of pattern extraction (ALOPEX) family is addressed. Three different experiments are presented in support of the ALOPEX algorithms using synthetic and real-life data on binocular fusion of stereo images, on-line prediction, and system identification.

In earlier papers [1]-[5], we described a novel multitransmit, multireceive (MTMR) antenna system for wireless communications. This new system, turbo Bell-Labs layered space-time (T-BLAST) architecture, combines the benefits of layered space-time coding concepts and turbo principles in the multitransmit, multireceive antenna system design. In particular, the random layered space-time codes designed by using a set of block convolutional codes and random space-time interleavers and the space-time turbo-like decoding operation allow T-BLAST to realize the benefits of MTMR systems in a computationally feasible manner. The goal of this paper is to present experimental results of T-BLAST based on real-life data collected using the Bell-Labs experimental multiple antenna system with eight transmit and five and six receive antennas. The experimental results show the practical virtues of T-BLAST. Index Terms--Bell-Labs layered space-time (BLAST) architecture, interference cancellation, space-time codes, turbo processing.

As an essential part of the emerging Internet of Things, connected and autonomous vehicles (CAVs) have the potential to reshape future transportation systems and change the commute style in people’s everyday life. Among many vehicular on-board devices, radar system and vehicle-to-vehicle (V2V) communication system are two important pillars for the realization of CAVs. In this paper, the concept of coordinated CRC (C-CRC) is proposed to serve as a cognitive mediator for bridging vehicular radar and communication systems. By establishing a mutual-assistance relationship, C-CRC provides a new safety mechanism that allows one system to learn from and react to the risks that the other system has encountered. Simulation results have shown that the proposed method has desirable performance in face of motion perturbation and/or jamming attack under various scenarios. [ABSTRACT FROM AUTHOR]

The future of intelligent transportation system (ITS) is expected to be composed of connected and autonomous vehicles (CAVs), the development of which will have great impact on people's everyday life. Unfortunately, this progress will be accompanied by all kinds of potential threats and attacks rising in CAV network. As a legacy from traditional wireless networks, jamming attack is still one of the major and serious threats to vehicle-to-vehicle (V2V) communications. In this paper, we investigate the anti-jamming V2V communication in CAV networks through power control in conjunction with channel selection. Bringing into play a brain-inspired research tool called cognitive dynamic system (CDS), the general structure of cognitive risk control (CRC) is well-tailored to analyze and address the jamming problem. Specifically, power control is carried out first using reinforcement learning, the result of which is then examined by a module called task-switch control. Based on the risk assessment, a multi-armed bandit (MAB) problem is formulated to perform the channel-selection process when necessary. Through continuous perception-action cycles (PACs), the feature of predictive adaptation is realized for the legitimate vehicle in its behavioral interactions with the jammer. Simulation results have shown that the proposed method has desirable performance in terms of several evaluation metrics. [ABSTRACT FROM AUTHOR]

Cognitive dynamic system (CDS) is a structured engineering model and research tool inspired by certain features of the human brain. As a special function of CDS, cognitive risk control (CRC) actualizes the concept of predictive adaptation to bring risk under control when encountered with unexpected uncertainty. In this paper, the first experimental demonstration of CRC is presented in the practical application of vehicular radar systems, and an algorithm for transmit-waveform selection in cognitive vehicular radar (CVR) based on CRC is proposed. During each perception–action cycle, the perceptor of CVR processes new environmental inputs and provides the processed information to the executive through feedback channel for the selection of cognitive action. With the mechanism of task-switch control being functional all the time, the CVR will switch to a more capable operation mode in the face of unexpected disturbances or adverse events. In such cases, a new subsystem of executive is brought into play, in which the risk-sensitive cognitive action is finally selected and applied to the environment. Simulation results have shown the robustness and effectiveness of the proposed CVR system, which can make the next-generation vehicular radars more intelligent and play an important role in future self-driving cars. [ABSTRACT FROM AUTHOR]

The idea of cognitive dynamic system (CDS) was first described in a Point of View article published ten years ago [1]. As simple as it was then, it is remarkable how far that early work on the CDS has progressed to make a big difference to engineering. The objective of this article is to highlight the recently pioneered elements of the CDS for how to bring risk under control for physical systems in the presence of uncertainties. [ABSTRACT FROM AUTHOR]

Much has been said about observability in system theory and control; however, it has been recently that observability in complex networks has seriously attracted the attention of researchers. This paper examines the state-of-the-art and discusses some issues raised due to complexity and stochasticity. These unresolved issues call for a new practical methodology. For stochastic systems, a degree of observability may be defined and the observability problem is not a binary (i.e., yes-no) question any more. Here, we propose to employ a goal-seeking system to play a supervisory role in the network with the objective of improving the degree of observability. Towards this goal, the supervisor dynamically optimizes the observation process by reconfiguring the sensory parts in the network. A cognitive dynamic system (CDS) is proposed as a proper choice for the supervisory system. In this framework, the network itself is viewed as the environment with which the CDS interacts. Computer experiments confirm the potential of the proposed approach for addressing some of the issues raised in networks due to complexity and stochasticity. [ABSTRACT FROM AUTHOR]

This article proposes to integrate two entities, the Internet of Things and a cognitive dynamic system (CDS), and studies a smart home scenario as the application of interest. As a people-centric IoT, the smart home aims to enhance the intelligence level of the living environment and improve the quality of human life. Cognition, the distinct principles of which are perception?action cycle, memory, attention, intelligence, and language, can play a key role to pave the way for building truly smart homes. With cognition as its foundation, the engineering paradigm of CDS provides step-by-step guidelines for systematic development of smart homes. Hence, CDS can significantly contribute to the interactive IoT ecosystem. [ABSTRACT FROM AUTHOR]

This paper addresses a brand new way of thinking that distinguishes itself by integrating two entities, namely, dynamic spectrum management on the one hand and cognitive dynamic system (CDS) on the other. To be more precise, the CDS performs the role of a cognitive engine centered at the heart of wireless communications, wherein licensed bands rooted in network providers responsible for primary users and unlicensed bands rooted in other networks responsible for secondary users. Thus, these two worlds embody primary users and secondary users, which collectively function within a single complex wireless communication network empowered by the CDS for the first time ever. [ABSTRACT FROM PUBLISHER]