Modified Polar Representation (MPR) has shown its superiority in integration of near and far field localization for a stationary source. This paper extends the researches and applications of the MPR to moving source, where time difference of arrival (TDOA) and frequency difference of arrival (FDOA) exist due to the relative motion between sensors and source. Theoretical analysis through hybrid Bhattacharyya-Barankin (HBB) bound is provided for evaluating the performance tighter than the Cramér-Rao lower bound (CRLB). We propose a Maximum Likelihood Estimator (MLE) implemented by Gauss-Newton (GN) iteration based on the extended MPR to estimate the position and velocity of source and test the performance under the new MPR. Simulation results confirm that MPR can eliminate the thresholding effect of source position as the source moves far away.

Existing VLC backhaul solutions typically rely on rigid wired (e.g., Ethernet or power-line) or alignment-sensitive LOS links for inter-attocell connectivity, which renders the overall network vulnerable to backbone link failures. In this poster, we introduce a novel network architecture that exploits the inherent non-line-of-sight (NLOS) optical channels between adjacent attocells to enable inter-attocell communication. In particular, we design a chirp signal based on chirp spread spectrum (CSS) modulation to enhance the robustness of communication under low signal-to-noise ratio (SNR) conditions, along with a two-stage window alignment approach to achieve precise synchronization while reducing real-time decoding latency. The potential and feasibility of the newly suggested approach are demonstrated through implementations on both ESP32 and FPGA platforms.

Visible light communication (VLC), as one of the key technologies for new spectrum communication in 6G, has drawn much attention from both academia and industry. The femtocell-like deployment of VLC in indoor environments gives rise to the concept of optical attocells, where each light-emitting diode (LED) serves as an optical access point (AP), enabling illumination and communication simultaneously. However, the majority of existing optical attocell networks rely on wired backbone links (e.g., Ethernet or power-line connections) for inter-attocell connectivity, and this dependency renders the overall network vulnerable to backbone link failures. To this end, we introduce a novel network architecture that exploits the inherent non-line-of-sight (NLOS) optical channels between adjacent attocells to enable inter-attocell communication, enhancing network resilience and flexibility. In particular, we design a chirp signal based on chirp spread spectrum (CSS) modulation tailored for intensity-modulated VLC to improve the noise resilience in NLOS channels for reliable communication under low signalto-noise ratio (SNR) conditions. Moreover, we propose a twostage window alignment approach that integrates coarse-grained with fine-grained alignment to achieve precise synchronization while reducing real-time decoding latency. Finally, we build two prototypes of NLOS optical attocells based on different hardware platforms and conduct extensive field experiments using three modulation schemes, i.e., on-off keying (OOK), frequency-shift keying (FSK), and CSS, to evaluate their performance under variable parameters. Experimental results indicate that the CSS modulation scheme demonstrates superior robustness than the other two schemes, achieving bit error rates (BER) of 3.1×10-5 and 8.3×10-5 and packet reception rates (PRR) of 98.53% and 99.71% on two platforms, respectively, at a horizontal distance of 8 m between adjacent optical devices.

Localizing a three-dimensional (3D) source using linear arrays (LAs) is a promising new localization technology. Existing solutions are either designed for specific LA deployments, are computationally intensive, or rely on iterative methods that do not guarantee convergence. This paper presents a novel algebraic solution algorithm for 3D source localization using space angle (SA) measurements from LAs. We propose a new formulation of the SA measurement equation, which leads to a constrained weighted least squares (CWLS) problem. Solving it by Lagrangian multipliers, the optimal estimation is obtained with an error correction. The solution does not require specific arrangement and placement of LAs and effectively balances accuracy with computational efficiency. We analyze the performance and complexity of the proposed solution, demonstrating its ability to achieve the Cramér-Rao Lower Bound (CRLB) in the small error region under Gaussian noise with a low computational load. Simulations validate the analysis and confirm the superiority of the proposed solution compared to existing ones.

Three-dimensional (3-D) target localization using one-dimensional (1-D) space angle (SA) measurements from linear arrays has recently gained significant attention. Each 1-D SA measurement defines a conical surface, and the intersection of multiple such surfaces determines the target's location in 3-D space. However, state-of-the-art methods for solving the 1-D SA localization problem are often either suboptimal or computationally intensive. In this paper, we propose a novel iterative weighted least squares (IWLS) algorithm to address the problem. To provide deeper insights, we present a geometric interpretation of the iterative process, highlighting its physical significance. Furthermore, we analyze the computational complexity of the proposed algorithm and compare it with existing methods. Simulation results demonstrate that the proposed algorithm not only achieves higher estimation accuracy but also requires less computational time compared to state-of-the-art approaches.

As a main approach towards touch-free human-computer interaction,hand gesture recognition(HGR) has long been a research focus for both academia and industry. Meanwhile,visible light communication(VLC) has become increasingly popular with VLC-ready commercial products (e.g., Philips lamps) available on the market. These facts provoke us to ask: can we leverage a VLC-ready lamp to realizeintegrated sensing and communication(ISAC) by conducting both HGR and VLC simultaneously? To this end, we propose ReflexGest as our answer to this question. ReflexGest is implemented upon a table lamp for the sake of practicality; this VLC-ready lamp is equipped with a ring-shaped light-emitting diode (LED) array and a photodiode (PD, for light intensity sensing) originally aiming for up/down-link VLCs. Demanding hand gestures to be performed between the lamp and a table surface, ReflexGest exploits the variation of the reflection and their unique correlation with the corresponding hand gestures to achieve HGR. In particular, ReflexGest first handles the limited sensing ability of the PD by enhancing the LED lamp and thus diversifying the light emission patterns. Moreover, ReflexGest combats the reflection interference from varying table surfaces via an adversarial learning technique to distill only the features relevant to hand gestures. Our extensive evaluations demonstrate that ReflexGest is able to deliver accurate HGR under realistic VLC traffic.

Multidimensional scaling (MDS) is an attractive method for location-related applications due to its robustness against noise. This paper applies MDS to time difference of arrival (TDOA) localization in the modified polar representation (MPR) for integrating near-field and far-field localizations. The new MDS formulation yields a constrained optimization problem in terms of the source position. We then propose a computationally efficient and noise robust solution to solve this problem. The solution is closed-form and asymptotically unbiased. It can achieve better mean-square error (MSE) in the large noise region and possibly lower bias than the closed-form solutions from the literature, and also has attractive complexity.

Linear array-based three-dimensional (3-D) localization is a recently proposed technology. It uses a set of newly defined one-dimensional (1-D) angles, called space angle (SA), to locate the source. Integrating SA with time difference of arrival (TDOA) promises higher accuracy, and more attractive, provides the formulation leading to closedform solutions. Currently, studies of hybrid SA-TDOA localization leverage iteration to refine the performance to the Cramer-Rao lower' bound (CRLB), which suffers from the possible convergence issues. This paper advances this topic by proposing a two-stage weighted least squares (TSWLS) closed-form estimator, which avoids the risk of divergence and can attain the CRLB if the noise is mild. Analytical and numerical results show the performance ascendancy of the proposed method.

A recent research on a fixed point iteration (FPI) algorithm for elliptic localization has shown tremendous promise in terms of reduced implementation complexity than existing algorithms without sacrificing performance [1]. However, no theoretical analysis was provided in this study. In this article, we present a thorough theoretical analysis of the FPI algorithm’s convergence and performance. These analyses show that: 1) the optimum estimate of the maximum likelihood estimation (MLE) problem is found in a sphere; 2) the FPI estimator is unbiased and its covariance matrix achieves the Cramér-Rao lower bound (CRLB) matrix when the measurement noise is small enough.

With the increasing prevalence of IoT devices and smart systems in daily life, there is a growing demand for new modalities in Human-Computer Interaction (HCI) to improve accessibility, particularly for users who require hands-free and eyes-free interaction in contexts like VR environments, as well as for individuals with special needs or limited mobility. In this paper, we propose teeth gestures as an input modality for HCI. We find that teeth gestures, such as tapping, clenching, and sliding, are generated by various facial muscle movements that are often imperceptible to the naked eye but can be effectively captured using mm-wave radar. By capturing and analyzing the distinct patterns of these muscle movements, we propose a hands-free and eyes-free HCI solution based on three different gestures. Key challenges addressed in this paper include user range identification amidst background noise and other irrelevant facial movements. Results from 16 volunteers demonstrate the robustness of our approach, achieving 93% accuracy for up to a 2.5m range.

Platooning has been proven to dramatically increase traffic flow and reduce fuel consumption, and vehicle-to-vehicle (V2V) communication and sensing are requisite for platooning stability. However, most of the existing works only address V2V communication or sensing functions respectively, which is far away from meeting the 6G requirements in availability and synchronization for platooning applications. Inspired by the recent advanced integrated sensing and communication (ISAC), in this paper, we propose VehicleTalk, a lightweight V2V communication and sensing framework. Essentially, VehicleTalk reuses the head/tail LED lights of the vehicles to construct communication/sensing channels for achieving concurrent message exchange and status awareness between adjacent vehicles. In particular, VehicleTalk innovates in both message transmission and vehicle sensing to improve communication robustness and lower system latency for platooning. It leverages Raptor Codes to combat serious packet loss in the V2V network. We also engineer a fast risk detection algorithm by simply monitoring the strength change of the received optical signals from the head/tail lights to improve safety in extreme cases such as emergency brake and cutting-in. Finally, we build a prototype of VehicleTalk with low-cost Commercial Off-The-Shelf (COTS) devices to quickly verify its effectiveness, and the extensive experimental results demonstrate the promising performance of VehicleTalk.

Recent research has shown that using the 1-D signal arrival angles observed by linear arrays can locate a 3-D source in unique co-ordinates. Current methods to solve this localization problem are based on semidefinite programming (SDP) or gradient-based iteration, which are either computationally demanding or facing divergence or local convergence issues. This paper reformulates the maxi-mum likelihood (ML) estimation of the 3-D localization problem using the factor graph model, where an effective algorithm is designed through message passing. Although iterative, the proposed solution is more robust to measurement noise than the Gauss-Newton (GN) iterative solution, and the complexity is lower than the SDP solution without the need to introduce semidefinite relaxation error. Simulations validate the analytical performance and complexity, and con-firm the superiority on the convergence of the proposed solution.

Human-machine interaction (HMI) is much important in factories, and most HMI ways are contact which arise safety and health issues. To avoid such problems, contactless HMI ways such as in-air hand gesture recognition (HGR) via Wi-Fi or radar are widely studied by both academia and industry. However, these RF-based methods are not very appropriate for the industry because of the electromagnetic interference. As for visible light sensing, it is free of electromagnetic radiation and can reuse the existing devices, e.g., lamps on machines, hence utilizing visible light to realize HGR is a good solution for HMI in factories. The current visible-light-enabled HGR (VL-HGR) methods using deep learning algorithms are all supervised, which increases the cost of manual labeling and further hinders the industrial applications of VL-HGR. To this end, we propose SemiGest, a semi-supervised learning (SSL) method for VL-HGR, to facilitate the applications of VL-HGR in industry. The system prototype is built on a table lamp to mimic the lamp on a machine emitting lights at four distinct carrier frequencies, and the lights reflected by hands are collected by a receiver. SemiGest utilizes the variation and correlation of the lights to realize HGR with an SSL algorithm using only a small amount of labeled data and lots of unlabeled data. Furthermore, the SSL algorithm is designed not only for the visible light data but also can be generalized to other time-series data in the industry. To confirm the effectiveness and robustness of SemiGest, we perform various experiments to show the potential for practical implementation in the industry.

The signal emitted by an acoustic source may be propagating in an environment in which the speed is not known, such as in solid or ocean. Localization of such a source through observing the signal by a number of sensors requires joint estimation with the propagation speed. This work applies the nullspace projection approach to the pseudo-linear formulation for the localization problem to obtain a closed-form solution, which is refined by error-compensation to reach the final estimation. In contrast to the methods from the literature that are either suboptimal or computationally demanding, the proposed method is both statistically and computationally efficient, and is shown analytically to achieve the Cramér-Rao Lower Bound accuracy.

Optical camera communication (OCC) enabled by LED and embedded cameras has attracted extensive attention, thanks to its rich spectrum availability and ready deployability. However, the close interactions between OCC and the indoor spaces have created two major challenges. On one hand, the stripe pattern incurred by OCC may greatly damage the accuracy of image-based object recognition. On the other hand, the patterns inherent to indoor spaces can significantly degrade the decoding performance of reflected OCC. To this end, we propose CORE-Lens as a pipeline to make the mutual interference transparent to existing OR and OCC algorithms. Essentially, CORE-Lens treats the two challenges as two sides of a signal mixture issue: the signals transmitted by OCC get mixed with background images so well that their features become entangled. Consequently, CORE-Lens exploits the idea of disentangled representation learning to separate the mixed signals in the feature space: while the GAN-reconstructed clean background images are used to perform object recognition, OCC decoding is conducted on the residual of the original image after subtracting the reconstructed background. Our extensive experiments on evaluating the real-life performance of CORE-Lens evidently demonstrate its superiority over conventional approaches.

Abstract Phase retrieval (PR) of block‐sparse signals is a new branch of sparse PR that causes rising research, which focusses with methods owing a high successful rate. However, the recovery performances of existing methods for block sparsity are usually unfit for large‐scale problems with unacceptable compute complexity. We derive an algorithm for PR of block sparsity via variational Bayesian learning with expectation maximisation to mitigate this drawback. In the proposed algorithm, the block‐sparse structure is modelled by the hierarchical constructional priors with a novel adaptive coupled pattern, which provides a strong relationship between the neighbour blocks. Simulations indicate that the proposed algorithm outperforms the existing methods in success rate, noise‐robustness, and signal detection rate in large‐scale cases with acceptable computation complexity.

Locating a device is a basic element for many Internet of Things (IoT) applications. In particular, it often demands an algorithm having low complexity to limit the energy consumption and most important, sufficient robustness without knowing the device in the near-field for point localization or in the far-field for direction of arrival (DOA) estimation. This article proposes a new localization algorithm that can achieve the two purposes, with the theoretical analysis to validate the optimal accuracy and the real data experiment to support the promising performance. The first objective is achieved by a closed-form solution and the second is accomplished by using the modified polar representation (MPR) of the source position, based on a new formulation for the localization problem. While the MPR localization method has been introduced before, it is not sufficiently robust for IoT application to handle the large equal radius (LER) scenario or the presence of sensor position errors. The proposed algorithm uses a different MPR formulation, which is able to handle the LER scenario, sensor position errors, and has low computational complexity.

Time difference of arrival (TDOA) point positioning in the Cartesian coordinates is practical for a near-field source, and it will suffer from the thresholding effect when the source is in the far-field where only direction of arrival (DOA) can be obtained. Localization in the modified polar representation (MPR) is able to alleviate this problem, where point positioning and DOA estimation are unified into a single framework. The state-of-the-art literature only has an iterative realization of the maximum likelihood estimator (MLE) for this problem. This paper develops an algebraic closed-form positioning solution for MPR. The proposed algorithm avoids the initialization issue and is much more computationally efficient than the MLE with comparable accuracy. Simulation results validate the advocated performance.