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In this paper, we present a novel non-contact and privacy preserving approach on recovering physiological measurements like heart rate when multiple subjects are slowly walking in the scene using a ultra-wideband (UWB) multi-frequency radar system. We employ joint probabilistic data association (JPDA) algorithm to separate detection from multiple moving objects. Additionally, we exploit a recently developed multi-channel variational mode decomposition (MVMD) algorithm to extract heartbeat information from the range aligned samples. Our contribution lies in demonstration the possibility of a novel signal processing approach on heartbeat detection while multiple human targets are moving on the order of a few meters with a relatively simple setup while prior works requires a more complex setting and only considers small body movement on centimeter levels.

The maximum value of an antenna array’s sidelobe beampattern, or radiation pattern in the power domain, is an important parameter determining its performance. In this paper, when array antenna elements have random phase centers, we approximate the maximum sidelobe value, or peak sidelobe level, as a Gumbel distribution using the extreme value theory (EVT). Before the EVT result, an expression for the beampattern distribution at each angle in the array field of view is found. From this expression, the pointwise convergence of the difference between the beampattern and exponential distributions in the limit of a large number of antennas is obtained. Using the exponential distribution approximation, EVT is applied with samples of the beampattern. A bound is given for the difference between the beampattern sample maximum and its true maximum in the sidelobe region.

We develop and present a novel minimum estimation error variance waveform design method. The method optimizes the spectral shape of a unimodular radar waveform such that the performance of a joint radar-communications system that shares spectrum is maximized. We also develop the novel spectral water-filling successive interference cancelation data rate, which employs the continuous spectral water-filling algorithm to obtain the optimal communications power spectrum. We perform a numerical study to compare the performance of the new technique with the previously derived spectral mask shaping method. The global estimation rate and the data rate capture the radar and the communications performance, respectively.

Limited spectral resources and increasing demand for diverse RF applications limit the capabilities of modern RF systems. Recent results in the field of RF Convergence demonstrate that these limitations may be overcome by adopting co-operative and co-design techniques. We designed the Communications and High-Precision Positioning (CHP2) system to provide positioning, navigation, and timing (PNT) and communications services in congested environments with limited spectral access. This system leverages modern two-way ranging (TWR) techniques to provide sub-centimeter ranging precision to flying ad hoc networks (FANETs) in GPS denied environments with only 10 MHz bandwidth. In this study, we extend the CHP2 TWR algorithm to improve robustness against carrier-phase range ambiguities, commonly referred to as “cycle slips”. We propose a resolution method in conjunction with joint distributed coherence and time-of-flight (ToF) tracking to mitigate these ambiguities, with a structure similar to a time-sensitive Viterbi trellis. The proposed “trellis trimming” method measures the likelihood of multiple ranging hypotheses and eliminates paths of least likeliness using Bayesian filters. We demonstrate that this method reduces the probability of incorrectly choosing range ambiguities and significantly improves ranging performance in low-SNR regimes where cycle slips are common.

Future radio systems must rely on co-existence and co-operation techniques to adapt to limited spectral resources and increasing demand. Motivated by the promise of RF Convergence co-design techniques, we designed and implemented the Communications and High-Precision Positioning (CHP2) system for flying ad hoc networks (FANETs). CHP2 simultaneously synchronizes distributed users, performs carrier-phase-accurate localization and enables network communications using limited bandwidth. We previously demonstrated rapid (

With rapid advances in flexible, software-defined computational architectures and radio front-end technologies, we have an opportunity to improve the efficiency of spectral use, reinforce system reliability, and enable new system capabilities. We deprecate the traditional “stove-piped” approach towards spectral management in favor of networks of RF, mmWave, and terahertz systems that jointly execute multiple radio functions, including communications; radar; positioning, navigation, and timing (PNT); spectral situational awareness; and opportunistic spectrum access. Such multi-function systems are enabled by a class of waveform, processing, and co-design techniques which we label spectral convergence. In this paper, we review the set of challenges obstructing the integration of such devices into the same spectral region. We briefly survey the current status of the field, outline a taxonomy of solution classes, and recommend paths towards fruitful research. We discuss existing and novel approaches towards enabling multifunction systems in the context of contemporary applications such as automotive networks and satellite meshes. We address the challenges associated with distributed resource optimization in networks of multi-function systems. We apply these concepts to a simple automotive network example and demonstrate the potential of spectral convergence solutions in a basic simulation environment

Spectral congestion limits the opportunities and performance of radio frequency (RF) systems. Spectral isolation sufficiently mitigates this congestion for a small number of users but does not offer a scalable solution once the entire spectrum is occupied. Dynamic resource management supports higher user densities by constantly renegotiating spectral access depending on need and opportunity. This approach promises efficient spectral access but is predicated on cooperation between different types of RF systems, which is a significant paradigm shift for many legacy technologies. Intelligent transportation systems (ITS) rely on several different types of RF services such as radar, communications, and positioning, navigation, and timing (PNT). RF Convergence demonstrates that many of these systems can be executed simultaneously using efficient cooperation strategies, which improves performance and limits spectral access. In this study, we demonstrate a simultaneous positioning, navigation, timing, and communications system that cooperatively executes multiple RF services. We define a "constant-information ranging" strategy that maintains constant information learned about an incoherent moving target by modulating the revisit interval to minimize the number of interactions. This significantly reduces spectral congestion and offers a control mechanism to dynamically manage spectral access. We validate the constant-information ranging algorithm in a simulation environment where we observe a 91% reduction in spectral access for a particular flight path while maintaining a 3 cm precision in ranging.

In this paper, we develop a decision theoretic approach for radar waveform design to maximize the joint radar communications performance in spectral coexistence. Specifically, we develop an adaptive waveform design approach by posing the design problem as a partially observable Markov decision process (POMDP), which leads to a hard optimization problem. We extend an approximate dynamic programming approach called nominal belief-state optimization to solve the waveform design problem. We perform a numerical study to compare the performance of the proposed POMDP approach with the commonly used myopic approaches.

Remote vital sign monitoring at home with commercially viable hardware extends care from hospital wards to personal settings without overwhelming health care providers and resources. In this paper, we explore usage of Azure Kinect (Microsoft) to monitor respiration and cardiac activity of a subject. We propose a fusion sensing technique that ensures immunity to ambient lighting conditions and movements of the subject, two key limitations while employing cameras or radars respectively. The functionality of this process is enabled by the phenomenon of Photoplethysmography (PPG) and availability of commercial sensors that can leverage it. We investigate robustness of the proposed technique by conducting experiments in variable lighting conditions while the subject is in motion. A comparison is drawn to an RGB camera and the results are validated using a pulse oximeter, a contact PPG sensor.

In-band, full-duplex (IBFD) radio frequency (RF) users generate self-interference that obstructs receiver operations. This interference may be mitigated using several techniques, including antenna isolation, circulators, digital mitigation, and analog mitigation at the carrier frequency. We apply an analog mitigation technique that uses a sparse tap-delay model to reduce power and computational complexity. We demonstrate that optimizing the weights of the resulting equalizer achieves sufficient self-interference mitigation, even under realistic hardware limitations. The performance is limited by i) how many delay taps are chosen, ii) how closely this discrete model matches the actual channel, iii) the dynamic range of the weighting coefficients, and iv) the timing precision of the system. We compare the achievable performance of this technique under different combinations of these limitations. We propose a sparse, constrained optimization solution that offers sufficient self-interference mitigation and significantly reduces the power consumption.

The Communications and High-Precision Positioning (CHP2) system enables secure communications and positioning services between networks of cooperative RF users. CHP2 features flexible physical and data link layers that may be tuned to prioritize communications or positioning precision to support traffic and resource management for different network conditions. We apply reinforcement learning techniques to optimize positioning performance and network throughput for a simple CHP2 network configuration. In this example, a central arbiter manages a grid of ground users tracking aerial users moving around the network. This arbiter dynamically optimizes each ground user to maximize network throughput while maintaining a minimum positioning precision on each aerial user. We demonstrate that this arbiter successfully manages the ground user resources to track the aerial targets with a minimal number of interactions. This reduces the required resources to maintain the network, which reduces the spectral congestion and power consumption for sparse networks or supports more users in congested networks.

Unmanned aerial systems (UASs) have recently enabled novel applications such as passenger transport and package delivery, but are increasingly vulnerable to cyberattack and therefore difficult to certify. These applications require robust positioning, communciations, and time synchronization services. Legacy systems such as GPS provide these capabilities extremely well, but are sensitive to spoofing and hijacking cyberattacks. In a previous study, we developed a local GPS alternative that provides highly secure communications, positioning, and timing synchronization services to networks of cooperative RF users. In this study, we consider this Communications and High-Precision Positioning (CHP2) system in the context of safety-critical transport applications and urban air mobility. We demonstrate rapid (< 100 ms) and precise (< 1cm) positioning capabilities in over-the-air experiments using flexible ground stations and UAS platforms using limited bandwidth (10 MhZ). We compare several position estimation algorithms in simulation and experimental test environments to identify suitable approaches for different applications. We discuss how the integrated communications link secures the system from spoofing and hijacking cyberattacks. We assert that the these capabilities make CHP2 a suitable candidate to provide both communications, navigation, and surveillance (CNS) and alternative positioning, navigation, and timing (APNT) services for safety-critical transport applications on a variety of vehicular platforms.

Image sensors with programmable region-of-interest (ROI) readout are a new sensing technology important for energyefficient embedded computer vision. In particular, ROIs can subsample the number of pixels being readout while performing single object tracking in a video. In this paper, we develop adaptive sampling algorithms which perform joint object tracking and predictive video subsampling. We utilize an object detection consisting of either mean shift tracking or a neural network, coupled with a Kalman filter for prediction. We show that our algorithms achieve mean average precision of 0.70 or higher on a dataset of 20 videos in software. Further, we implement hardware acceleration of mean shift tracking with Kalman filter adaptive subsampling on an FPGA. Hardware results show a 23 × improvement in clock cycles and latency as compared to baseline methods and achieves 38FPS real-time performance. This research points to a new domain of hardware-software co-design for adaptive video subsampling in embedded computer vision.

Modern RF environments are becoming increasingly congested. This limits the opportunities and capabilities of modern RF systems, obstructing the development and proliferation of new technologies. Novel vehicular RF technologies promise a new era of transportation capabilities, but legacy design techniques cannot adapt to the current spectral congestion. We summarize recent RF Convergence results and discuss how they mitigate spectral congestion in modern vehicular applications. We propose a joint radar, communications, positioning, navigation, and timing (JRCPNT) system architecture as a suitable candidate for future automotive applications. We present relevant performance bounds and initial experimental results to demonstrate the potential performance enhancements of such multiple-function RF systems. We define multiple-channel, multiple-user receiver (MCMUR) techniques that enable this architecture and discuss how these components cooperate to enable these performance enhancements. We summarize initial experimental results to demonstrate the viability of such multiple-function architectures in the context of urban air mobility (UAM) and other automotive applications.

The estimation of the visual stimulus-based reaction time (RT) using subtle and complex information from the brain signals is still a challenge, as the behavioral response during perceptual decision making varies inordinately across trials. Several investigations have tried to formulate the estimation based on electroencephalogram (EEG) signals. However, these studies are subject-specific and limited to regression-based analysis. In this paper, for the first time to our knowledge, a generalized model is introduced to estimate RT using single-trial EEG features for a simple visual reaction task, considering both regression and classification-based approaches. With the regression-based approach, we could predict RT with a root mean square error of 111.2 ms and a correlation coefficient of 0.74. A binary and a 3-class classifier model were trained, based on the magnitude of RT, for the classification approach. Accuracy of 79% and 72% were achieved for the binary and the 3-class classification, respectively. Limiting our study to only high and low RT groups, the model classified the two groups with an accuracy of 95%. Relevant EEG channels were evaluated to localize the part of the brain significantly responsible for RT estimation, followed by the isolation of important features.Clinical relevance- Electroencephalogram (EEG) signals can be used in Brain-computer interfaces (BCIs), enabling people with neuromuscular disorders like brainstem stroke, amyotrophic lateral sclerosis, and spinal cord injury to communicate with assistive devices. However, advancements regarding EEG signal analysis and interpretation are far from adequate, and this study is a step forward.

Ultra Wide Band (UWB) radars are effective in detecting subtle fluctuations of illuminated target. This attribute makes them attractive for uncovering respiration and circulation patterns from chest movements. Despite the acute range resolution offered by Doppler radars, contribution by cardiovascular pumping is quite feeble and is overshadowed by breathing. It is observed that, when chest movements were monitored using UWB pulse Doppler radars, cardiac motion is buried at least 30 dB under first harmonic of respiration. In this paper, we propose and investigate novel Arctangent demodulation method for direct-RF receivers. The proposed method offers an improvement of signal-to-noise ratio by 8–15 dB of cardiopulmonary motion signal when compared to existing algorithms. In addition, it enables adaptive suppression of respiratory artifacts using trend filtering which vastly enhances robustness in real-time non-invasive heart rate monitoring applications.

We develop and present a radar waveform design method that optimizes the spectral shape of the radar waveform so that joint performance of a cooperative radar communications system is maximized. The continuous water-filling (WF) spectral-mask shaping method presented in this paper is based on the previously derived spectral-mask shaping technique. However, the method presented in this paper is modified to utilize the continuous spectral water-filling algorithm to improve communications performance. We also introduce additional practical system constraints on the autocorrelation peak side-lobe-to-main-lobe ratio and radar waveform spectral leakage. Finally, we perform a numerical study to compare the performance of the continuous WF spectral-mask-shaping method with the previously derived method. The global estimation rate, which also accounts for non-local estimation errors, and the data rate capture radar and communications performance respectively.

In this paper, we present a new observation when processing backscattered radar signals from a sleeping subject on a normal mattress. A breathing motion magnification effect is observed because of mattress surface displacement due to human respiratory activity. This undesirable motion artifact causes existing methods for accurate heart-rate estimation to fail. We therefore propose a novel active motion cancellation technique to deal with this problem by intelligently selecting slow-time series from multiple ranges and examining the corresponding phase difference. Finally, we validate our approach through experimental study.

UWB radars can detect minute surface displacements therefore, have been exploited for vibrometry applications. In this paper, we demonstrate via experimentation that it is feasible to remotely sense sound and recover acoustic signals from vibrating sources using radars. Early research in this domain only demonstrated single sound source recovery using narrowband millimeter wave radars in direct line-of-sight scenarios. Instead, we illustrate using X band UWB radars that, multiple sources separated in ranges are observed and their signals isolated and recovered. Additionally, we leverage the see-through ability of microwave signals to extend this technology to surveillance of targets obstructed by barriers. We further drive this notion of blind surveillance by reconstructing audio from a passive object which is merely in proximity of the sound source using clever radar and audio processing techniques.

A multiple-channel multiple-user receiver (MCMUR) exploits both spatial and temporal degrees of freedom to separate radar and communications signals coexisting jointly in the same frequency band, and to cancel interference waveforms. In this paper, we present the simultaneous track and search MCMUR, a novel mode of operation for the multi-antenna joint receiver. We discuss this novel operation of the receiver and present theoretical bounds on the detection and estimation performance of the simultaneous track and search MCMUR. We present a theoretically assessed receiver operating characteristic (ROC) analysis of the MCMUR herein to better understand the performance gains of the MCMUR at false alarm rates that are more informative of an actual radar systems' performance. We also measure the detection and estimation performance of the joint system through the detection and estimation information rates respectively.

Limited spectral access motivates radio technologies that are capable of performing multiple tasks simultaneously and efficiently cooperating with existing systems. We develop a joint positioning-communications system for a vehicular ad hoc network that simultaneously synchronizes distributed users, performs carrier-phase-accurate localization, and enables network communications. In this paper, we focus on two-way ranging methods for jointly estimating propagation delay and clock offset between the participating nodes in the network. We propose a family of novel one-shot methods to achieve distributed coherence and demonstrate that, for given conditions, such estimators reduce to a system of linear equations, hence, optimal. Here, we study the precision and computational complexity of two of these estimators using simulated flight paths and discuss their region of applicability.

Though it is often taken as a truism that communication contributes to organizational productivity, there are surprisingly few empirical studies documenting a relationship between observable interaction and productivity. This is because comprehensive, direct observation of communication in organizational settings is notoriously difficult. In this paper, we report a method for extracting network and speech characteristics data from audio recordings of participants talking with each other in real time. We use this method to analyze communication and productivity data from seventy-nine employees working within a software engineering organization who had their speech recorded during working hours for a period of approximately 3 years. From the speech data, we infer when any two individuals are talking to each other and use this information to construct a communication graph for the organization for each week. We use the spectral and temporal characteristics of the produced speech and the structure of the resultant communication graphs to predict the productivity of the group, as measured by the number of lines of code produced. The results indicate that the most important speech and network features for predicting productivity include those that measure the number of unique people interacting within the organization, the frequency of interactions, and the topology of the communication network.

We derive bounds on mutual information for arbitrary estimation problems in additive noise, modeled using Gaussian mixtures. Previous work exploiting the I-minimum-mean-squared-error (MMSE) formula to formulate a bridge between bounds on the MMSE for Gaussian mixture model estimation problems and bounds on the mutual information are generalized to allow arbitrary noise modeling. A novel upper bound on estimation information is also developed for the general estimation case. In addition, limits are analyzed to develop bounds on arbitrary entropy, asymptotic behavior of all bounds, and bound errors with some results bridged back to the MMSE domain.

In this paper, we introduce a radar information metric, the estimation rate, that allows the radar user to be considered in a multiple-access channel enabling performance bounds for joint radar-communications coexistence to be derived. Traditionally, the two systems were isolated in one or multiple dimensions. We categorize new attempts at spectrum-space-time convergence as either coexistence, cooperation, or co-design. The meaning and interpretation of the estimation rate and what it means to alter it are discussed. Additionally, we introduce and elaborate on the concept of “not all bits are equal,” which states that communications rate bits and estimation rate bits do not have equal value. Finally, results for joint radar-communications information bounds and their accompanying weighted spectral efficiency measures are presented.

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.

A methodology is presented to reduce the amount of coupled power for co-located multiple-input-multiple-output (MIMO) radar waveforms via convex optimization. Specifically, a set of predefined waveforms is desired to be implemented on a given uniform linear array (ULA) structure, where the coupling behavior for the array is given via the array scattering matrix. The predefined waveforms are assumed to have coupled power greater than the desired −10 dB power return loss threshold on all or some of the antenna elements throughout the MIMO transmission. This assumption is typically true for MIMO emissions that place a significant amount of energy near endfire when the array is impedance matched at bore-sight. Here, we consider two instantiations of Doppler-division multiple access (DDMA) MIMO waveforms: a monotonically increasing Doppler frequency assignment across the ULA, and a random frequency assignment. The DDMA waveforms are altered to limit the energy coupling between the antenna elements while minimizing the change (or maximizing the similarity) to the original set of waveforms. The altered set of waveforms is generated via minimization of a Quadratically-Constrained Quadratic Programming (QCQP) convex optimization problem formulation. The resulting emissions follow intuition where the emission near boresight is largely unchanged from the original while the maximum change happens near endfire.

Lattices provide useful structure for distributed coding of correlated sources. A common lattice encoder construction is to first round an observed sequence to a `fine&rsquo, lattice with dither, then produce the result&rsquo, s modulo to a `coarse&rsquo, lattice as the encoding. However, such encodings may be jointly-dependent. A class of upper bounds is established on the conditional entropy-rates of such encodings when sources are correlated and Gaussian and the lattices involved are a from an asymptotically-well-behaved sequence. These upper bounds guarantee existence of a joint&ndash, compression stage which can increase encoder efficiency. The bounds exploit the property that the amount of possible values for one encoding collapses when conditioned on other sufficiently informative encodings. The bounds are applied to the scenario of communicating through a many-help-one network in the presence of strong correlated Gaussian interferers, and such a joint&ndash, compression stage is seen to compensate for some of the inefficiency in certain simple encoder designs.

Modern radio systems must adapt to limited spectral access by reducing spectrum demand and increasing operational efficiency. We design and implement a hybrid communications, navigation, and surveillance (CNS) radio system, which simultaneously performs relative positioning and network communications in a distributed network of base-stations and unmanned aerial systems (UASs). Positioning and communications tasks are performed simultaneously with a single, co-use waveform, which efficiently utilizes a limited bandwidth allocation and limits spectrum demand of new entrants. The communications task enables applications such as distributed knowledge base, air traffic management (ATM), and distributed timing synchronization, while the positioning task enables applications such as collision avoidance and automated landing. The positioning task employs a novel time-of-flight (ToF) estimation algorithm that produces high precision (σ < 5 cm) position estimates with limited bandwidth (10 MHz). The communications task provides an encrypted data link between network nodes which enables phase-accurate timing synchronization and secures the positioning system against cyberattacks such as spoofing. Multi-antenna platforms additionally enable orientation estimation and multiple-input, multiple output (MIMO) communications. We implement this system on a consumer-off-the-shelf (COTS) experimental testbed to demonstrate the functionality of the system and verify theoretical performance limits. The experimental results demonstrate that this technology is a viable alternative positioning, navigation, and timing (APNT) system which can support increasingly dense networks and numerous applications.

Heterogeneous system-on-chips (SoC) can increase the energy-efficiency of domain-specific computation by orders of magnitude compared to scalar processors. High-performance systems can be generated procedurally through example-driven inference of a domain of computation to facilitate the design of domain-specific SoCs. This paper focuses on the domain of signal processing as it plays a recurring and important role in automation. The expertise required to build processors well-suited to a specific computation domain, rather than a single application or general computation, is inferred through the statistical analysis of computation, hardware, and their affinity for each other. This paper highlights the development of an ontological inference engine to achieve this goal.

Presents a collection of slide covering the following topics: communications and radar spectrum sharing.

In this paper, we propose a novel concept of Radar Cardiography (RCG). We demonstrate the ability to achieve precise heartbeat measurement wirelessly using advanced radar signal processing technique. We show that the reconstructed heartbeat waveform matches the reference electrocardiography (ECG) signal. We propose a new heartbeat monitoring method, in which we isolate cardiovascular activity from strong respiratory-inference in the 3-Dimensional (3-D) spaces: azimuth, elevation, and depth. With significant pulse signal-to-noise-ratio (SNR) improvement compared to the conventional radar approach, heart-rate variability (HRV) analysis using radar is therefore possible.

In this paper, we developed a new multiple-subject heart-rates monitoring algorithm through a wall using a wireless sensor. We proposed an adaptive heart-rate estimation algorithm to enable continuous heart-rate monitoring. Finally, we demonstrated the proposed theory through experimental demonstration.

One solution to addressing the spectral congestion problem is to co-design communications and remote sensing systems to cooperate, such that each system benefits from the presence of one another. In this work, we present a novel joint sensing and communications multiple access system architecture that allows for simultaneous decoding of communications information and radar target tracking and parameter estimation within the same space, time, and frequency continuum. We demonstrate the feasibility of this co-design architecture by implementing it on a network of software defined radios (SDRs). To characterize the system, we consider a scenario where one transceiver transmits an emulated radar return waveform, while a second transceiver simultaneously transmits a communications waveform. A third transceiver receives and processes the emulated radar return and communications waveforms jointly. The radar transmitter and joint sensing and communications receiver acts as a quasi-monostatic tracking radar. We characterize the joint performance of the system using the communications and estimation rate metrics. The results show that the system achieves high throughput and excellent radar tracking performance for the given experiment parameters.

In the automotive sector, both radars and wireless communication are susceptible to interference. However, combining the radar and communication systems, i.e., radio frequency (RF) communications and sensing convergence, has the potential to mitigate interference in both systems. This article analyses the mutual interference of spectrally coexistent frequency modulated continuous wave (FMCW) radar and communication systems in terms of occurrence probability and impact, and introduces RadChat, a distributed networking protocol for mitigation of interference among FMCW based automotive radars, including self-interference, using radar communications. The results show that RadChat can significantly reduce radar mutual interference in single-hop vehicular networks in less than 80 ms., 14 pages, 17 figures, 1 table, published in IEEE Transactions on Intelligent Transportation Systems

In this chapter, we develop a set of tools to study the co-design ofjoint communication and radar systems. For the sake of discussion, given a fixed set of resources, one can determine an n-dimensional space of performance metrics such that the best performance of the system can be described by a manifold in this metric space. By changing the parameters the overall system employs, one can therefore move along this surface to different operating points as indicated in Figure 19.1. Thus, an intelligent system can dynamically modify the parameters of a network of joint radar and communication nodes in response to multiple goals that, along with the environment, can likewise be dynamic. Further, in highly dynamic scenarios, distributed (as opposed to centralized) optimization may be required. In fact, dynamic joint radar and communication systems may actually provide a more appropriate example for use of the adjective “cognitive” than other systems that often employ this monicker.

Limited spectral access motivates technologies that adapt to diminishing resources and increasingly cluttered environments. We design and implement a joint positioning-communications system that enables simultaneous positioning of, and communications between, nodes in a distributed network of base-stations and unmanned aerial systems (UASs). This system design achieves high precision position estimates with fewer spectral resources than similar technologies, while simultaneously enabling orientation estimation and network communications. These capabilities are enabled by developing advanced time-of-arrival (ToA) estimators and a phase-accurate distributed coherence algorithm.

The growing population of radar and communications systems poses a challenge to spectral coexistence. In this preliminary study, we investigate the utility of reinforcement learning in facilitating cooperative radar and communications coexistence. We also highlight subtleties and challenges associated with employing reinforcement learning in this context. We simulate a small joint radar-communications network whose members communicate while tracking a moving target’s range. A network management system must learn how to distribute allotted bandwidth to jointly maximize communications and radar performance. Considering several constraints and assumptions in the simulation, the agent sensibly chooses actions that accomplish the required performance behavior.

In this paper, we propose a new signal processing method for continuous heart-rate (HR) monitoring using ultra-wide band (UWB) impulse radar. Since the radar sensor is able to directly sample the received signal at RF satisfying the Nyquist sampling rate, the vital information is preserved in the received signal. We show that mathematically the vital signs can be extracted directly from the raw RF signal and thus the down conversion to the complex baseband is optional. Finally, we demonstrate the HR monitoring performance from the proposed direct RF signal processing technique by comparing against the existing methods using the complex baseband signal.

In this work we consider the problem of quantifying the detection latency of weak signals-a signal whose energy is much lower than the noise floor. We define detection latency as the total number of samples needed to correctly detect the weak signal. We characterize the latency by considering a single-input, single-output (SISO) wireless communications scenario where the receiver is tasked with detecting the transition from a channel probing waveform to a 500 kHz signaling tone. We consider constant modulus linear frequency modulated (LFM) chirp and the GPS L2C (M) pseudorandom noise waveforms for channel probing. We also introduce the notion of hypothesis tracking built on the sequential detection framework to reduce detection latency at the receiver. We compare the performance between two hypothesis tracking methods.

We investigate methods of co-existence between radar and communications systems. Each system typically considers the other system a source of interference. Consequently, the traditional solution is to isolate the two systems spectrally or spatially. By considering a cooperative radar and communications signaling scheme, we derive achievable bounds on performance for a receiver that observes communications and radar return in the same frequency allocation. We assume the radar and communications operations to be a single joint system. Bounds on performance of the joint system are measured in terms of data information rate for communications and a novel radar estimation information rate for the radar.

In this paper, we investigate the joint multiple-input multiple-output (MIMO) radar and communications system convergence problem and develop a joint radar-communications performance bound using the concept of estimation rate for radar system and data information rate for communications system. At the radar receiver, we propose a Successive Interference Cancellation (SIC) strategy to estimate radar parameters and decode communications signal simultaneously. While, at the MIMO communications transmitter, we propose a spectrum sharing scheme and show that the optimal communications performance can be achieved through a spectral and spatial waterfilling algorithm. In the end, we show that the Multiple-Access (MAC) performance bound outperforms the result from the spectrally isolated MIMO radar-communications system.

Limited spectral access motivates radar and communications technologies that can cooperate effectively. We briefly discuss the recent advancements in the fields of joint radar-communications and RF convergence to motivate a discussion of joint radar-communications network topologies. We demonstrate that MIMO spatial diversity and multi-static sensing modalities can be leveraged by a radar subsystem to massively increase effective coverage area while simultaneously providing the standard communications performance improvement. We also demonstrate that certain topologies can support a massive increase in communications user density without adversely affecting radar performance.

In this paper, we discuss and demonstrate the possibility of detecting multiple heartbeat of stationary subjects at equal distance away from a single input and single output UWB radar. To that end, we propose a novel concept to extract the heartbeat information from its harmonics to deal with the spurious peaks of respiration signal and intermodulations between respiration and heartbeat signals. We provide a numerical analysis to justify the proposed algorithm. We compare the ability of suppressing the interferencing harmonics between the proposed algorithm and other detection algorithms. Finally, we provide an experimental example and show that accurate heart rates detection from two subjects at equal distance can be achieved using the proposed method by comparing against two types of references: camera based system and chest heart rate sensor.