Wireless mediums, such as RF, optical, or acoustical, provide finite resources for the purposes of remote sensing (such as radar) and data communications. Often, these two functions are at odds with one another and compete for these resources. Applications for wireless technology are growing rapidly, and RF convergence is already presenting itself as a requirement for both users as consumer and military system requirements evolve. The broad solution space to this complex problem encompasses cooperation or codesigning of systems with both sensing and communications functions. By jointly considering the systems during the design phase, rather than perpetuating a notion of mutual interference, both system’s performance can be improved. We provide a point of departure for future researchers that will be required to solve this problem by presenting the applications, topologies, levels of system integration, the current state of the art, and outlines of future information-centric systems.

Survey of RF Communications and Sensing Convergence Research

Joint radar and communication (JRC) technology has become important for civil and military applications for decades. This paper introduces the concepts, characteristics and advantages of JRC technology, presenting the typical applications that have benefited from JRC technology currently and in the future. This paper explores the state-of-the-art of JRC in the levels of coexistence, cooperation, co-design and collaboration. Compared to previous surveys, this paper reviews the entire trends that drive the development of radar sensing and wireless communication using JRC. Specifically, we explore an open research issue on radar and communication operating with mutual benefits based on collaboration, which represents the fourth stage of JRC evolution. This paper provides useful perspectives for future researches of JRC technology.

Joint radar and communication: A survey

We present a Xampling-based technology enabling interference-free operation of radar and communication systems over a common spectrum. Our system uses a recently developed cognitive radio (CRo) to sense the spectrum at low sampling and processing rates. The Xampling-based cognitive radar (CRr) then transmits and receives in the available disjoint narrow bands. Our main contribution is the unification and adaptation of two previous ideas—CRo and CRr—to address spectrum sharing. Hardware implementation shows robust performance at SNRs up to –5 dB.

Spectrum Sharing Radar: Coexistence via Xampling

Inferring information from a set of acquired data is the main objective of any signal processing (SP) method. The common problem of estimating the value of a vector of parameters from a set of noisy measurements is at the core of a plethora of scientific and technological advances in recent decades, including wireless communications, radar and sonar, biomedicine, image processing, and seismology.

Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental Findings and Applications

This paper presents a spectrum sharing technology enabling interference-free operation of a surveillance radar and communication transmissions over a common spectrum. A cognitive radio receiver senses the spectrum using low sampling and processing rates. The radar is a cognitive system that employs a Xampling-based receiver and transmits in several narrow bands. Our main contribution is the alliance of two previous ideas, CRo and cognitive radar (CRr), and their adaptation to solve the spectrum sharing problem.

A theoretical framework that embraces the competing objectives of cooperative radar-communication operations is proposed that engages the apparent trade-space in an optimal fashion. Specifically, minimization of a cooperative risk metric that extends the Neyman-Pearson criterion to include communication data rate is explored. Hence, establishing performance bounds for cooperative interaction. Ideal informed reception where simultaneous access to received data from both the radar and communication (comm.) system is shown to yield a structured covariance-based water-filling solution. Unlinked cooperation where codebooks, waveforms, and schedules are known by all, but received data is not relayed between radar and comm. conveys a complex tradespace dependent on the rate of information transmitted by the comm. system relative to the capacity of the radar-comm. data link.

Performance bounds on cooperative radar and communication systems operation

A system that extracts text from an image includes a capture device that captures the image having a low resolution. An image segmentation subsystem partitions the image into image segments. An image restoration subsystem generates a resolution-expanded image from the image segments and negates degradation effects of the low-resolution image by transforming the image segments from a first domain to a second domain and deconvolving the transformed image segments in the second domain to determine parameters of the low-resolution image. A text recognition subsystem transforms the restored image data into computer readable text data based on the determined parameters.

Devices and methods for restoring low-resolution text images

The Bayesian framework is versatile as it allows for incorporation of prior knowledge or experience in making inference. The case of no prior knowledge at all is likewise seamlessly supported. The Bayesian framework is naturally suited to many fields of science and engineering including the discipline of radar design, analysis, and function. This paper will highlight recent findings on (i) Bayesian bounds under model misspecification that include the impact of using incorrect prior information, and (ii) parameter estimation for a cooperative radar-communication system.

Bayesian framework and radar: On misspecified bounds and radar-communication cooperation

Purpose – In many problems involving decision‐making under uncertainty, the underlying probability model is unknown but partial information is available In some approaches to this problem, the available prior information is used to define an appropriate probability model for the system uncertainty through a probability density function When the prior information is available as a finite sequence of moments of the unknown probability density function (PDF) defining the appropriate probability model for the uncertain system, the maximum entropy (ME) method derives a PDF from an exponential family to define an approximate model This paper, aims to investigate some optimality properties of the ME estimatesDesign/methodology/approach – For n>m, when the exact model can be best approximated by one of an infinite number of unknown PDFs from an n parameter exponential family The upper bound of the divergence distance between any PDF from this family and the m parameter exponential family PDF defined by the M

Prabahan Basu

Papers

Performance bounds on cooperative radar and communication systems operation

Devices and methods for restoring low-resolution text images

Bayesian framework and radar: On misspecified bounds and radar-communication cooperation

On estimation error using maximum entropy density estimates

Signal and Image Restoration: Information-Theoretic Approaches