Multi-view indoor radar perception has drawn attention due to its cost-effectiveness and low privacy risks. Existing methods often rely on implicit cross-view radar feature association, such as proposal pairing in RFMask or query-to-feature cross-attention in RETR, which can lead to ambiguous feature matches and degraded detection in complex indoor scenes. To address these limitations, we propose REXO (multi-view Radar object dEtection with 3D bounding boX diffusiOn), which lifts the 2D bounding box (BBox) diffusion process of DiffusionDet into the 3D radar space. REXO utilizes these noisy 3D BBoxes to guide an explicit cross-view radar feature association, enhancing the cross-view radar-conditioned denoising process. By accounting for prior knowledge that the person is in contact with the ground, REXO reduces the number of diffusion parameters by determining them from this prior. Evaluated on two open indoor radar datasets, our approach surpasses state-of-the-art methods by a margin of +4.22 AP on the HIBER dataset and +11.02 AP on the MMVR dataset. Our implementation is available at https://github.com/merlresearch/radar-bbox-diffusion.

Emerging IoT applications are transitioning from battery-powered to grid-powered nodes. DRP, a contention-based data dissemination protocol, was developed for these applications. Traditional contention-based protocols resolve collisions through control packet exchanges, significantly reducing goodput. DRP mitigates this issue by employing a distributed delay timer mechanism that assigns transmission-start delays based on the average link quality between a sender and its children, prioritizing highly connected nodes for early transmission. However, our in-field experiments reveal that DRP is unable to accommodate real-world link quality fluctuations, leading to overlapping transmissions from multiple senders. This overlap triggers CSMA’s random back-off delays, ultimately degrading the goodput performance. To address these shortcomings, we first conduct a theoretical analysis that characterizes the design requirements induced by real-world link quality fluctuations and DRP’s passive acknowledgments. Guided by this analysis, we design EDRP, which integrates two novel components: (i) Link-Quality Aware CSMA (LQ-CSMA) and (ii) a Machine Learning-based Block Size Selection (ML-BSS) algorithm for rateless codes. LQ-CSMA dynamically restricts the back-off delay range based on real-time link quality estimates, ensuring that nodes with stronger connectivity experience shorter delays. ML-BSS algorithm predicts future link quality conditions and optimally adjusts the block size for rateless coding, reducing overhead and enhancing goodput. In-field evaluations of EDRP demonstrate an average goodput improvement of 39.43% than the competing protocols.

This paper addresses the integration of millimeter-wave (mmWave) Wi-Fi communication and sensing during data transmission intervals (DTIs). We leverage prior knowledge from codebook beam training conducted during preceding beacon transmission intervals (BTIs) and association beamforming training (A-BFT) intervals to design a transceiver array response that meets both requirements on downlink communication SNR and targeted sensing area. By formulating it as a first-order array response optimization with constraints on power, codebook, communication SNR, and limited RF chains, this paper introduces a two-stage solution. First, we introduce a two-way communication-sensing matching pursuit to determine a set of codewords that prioritize the communication SNR constraint. Then, using the selected codewords, we employ an alternating minimization over an auxiliary phase term and beamforming weights to further minimize an array-response distance loss. Numerical results validate the effectiveness of the proposed DMG beamforming design over baseline methods.

In this paper, we propose an algorithm for the removal of ghost targets based on angle estimation termed GREST (Ghost targets Removal using ESTAR). In the proposed GREST, the tentative targets reports, including the azimuth angles, is obtained through the conventional 3-D coherent integrations and signal detection at first. Then two-way angles, defined as a combination of direction-of-arrival (DOA) and direction-of-departure (DOD), are estimated using the ESTAR (Estimation of Two-way Angle by MIMO Radar), which was proposed by the authors recently. Subsequently, the ghost targets are removed based on the estimated angles by comparing DOD and DOA to finalize the target reports. The ESTAR method represents a novel approach to angle estimation that yields unambiguous results, even in the case of MIMO radars with transmit sparse arrays, which are commonly utilized in the standard millimeter-wave radars. Based on these angles, it is possible to distinguish between direct and multipath propagation, thereby enabling the removal of ghost targets. In this paper, we will present the detail of the proposed GREST and demonstrate its effectiveness in removing ghost targets using experimental data acquired in a situation where multipath propagation is occurring.

The Radar dEtection TRansformer (RETR) has recently been introduced to fuse multi-view millimeter-wave radar heatmaps by leveraging the detection transformer architecture and a geometric learning framework for indoor radar perception. A notable feature of RETR is its tunable positional encoding (TPE), which allows for adjusting the significance of depth positional embedding across multiple views to promote depth-prioritized feature association. However, the TPE ratio is predetermined, rather than being optimized during the training process. In this paper, we propose a differentiable positional encoding (DiPE) scheme for RETR by automatically adjusting the TPE ratio during the training for enhanced performance and avoiding exhaustive grid value search. DiPE can be applied along with either pre-fixed (e.g., sinusoidal) or learnable positional embeddings, achieved by multiplying dual differentiable masks over the depth and angular positional embedding vectors. Comprehensive evaluations on the open MMVR dataset demonstrate that the proposed DiPE not only simplifies the determination of the TPE ratio but also enhances the overall detection performance.

As the number of wireless devices supporting multiple communication interfaces increases, the connection redundancy is being considered for efficient bandwidth utilization and QoS improvement. Accordingly, network technologies must adapt to emerging multi-interface devices to improve network performance. Multipath TCP (MPTCP) is default multipath transport protocol desired for networks with multi-interface devices and has achieved success in computer networks. However, it has not been well studied for wireless networks, especially for carrier sense multiple access (CSMA) based wireless networks, which present great challenges to round trip time (RTT) computation and multipath scheduling. This paper introduces MPTCP techniques for heterogeneous WiFi and 5G networks. We first model a proposed 5-state congestion control algorithm and WiFi CSMA function. We then present an innovative RTT computation method and a novel loss-ware multipath scheduling mechanism. We evaluated the proposed MPTCP techniques under varying network configurations. Our MPTCP can significantly outperform conventional MPTCP.

Radar-based indoor 3D human pose estimation typically relied on fine-grained 3D keypoint labels, which are costly to obtain especially in complex indoor settings involving clutter, occlusions, or multiple people. In this paper, we propose \textbf{RAPTR} (RAdar Pose esTimation using tRansformer) under weak supervision, using only 3D BBox and 2D keypoint labels which are considerably easier and more scalable to collect. Our RAPTR is characterized by a two-stage pose decoder architecture with a pseudo-3D deformable attention to enhance (pose/joint) queries with multi-view radar features: a pose decoder estimates initial 3D poses with a 3D template loss designed to utilize the 3D BBox labels and mitigate depth ambiguities; and a joint decoder refines the initial poses with 2D keypoint labels and a 3D gravity loss. Evaluated on two indoor radar datasets, RAPTR outperforms existing methods, reducing joint position error by $34.3$\% on HIBER and $76.9$\% on MMVR. Our implementation is available at \url{https://github.com/merlresearch/radar-pose-transformer}.

Wi-Fi channel measurements across different bands, e.g., sub-7-GHz and 60-GHz bands, are asynchronous due to the uncoordinated nature of distinct standards protocols, e.g., 802.11ac/ax/be and 802.11ad/ay. Multi-band Wi-Fi fusion has been considered before on a frame-to-frame basis for simple classification tasks, which does not require fine-time-scale alignment. In contrast, this paper considers asynchronous sequence-to-sequence fusion between sub-7-GHz channel state information (CSI) and 60-GHz beam signal-to-noise-ratio (SNR)s for more challenging tasks, such as continuous coordinate estimation. To handle the timing disparity between asynchronous multi-band Wi-Fi channel measurements, this paper proposes a multi-band neural dynamic fusion (NDF) framework. This framework uses separate encoders to embed the multi-band Wi-Fi measurement sequences to separate initial latent conditions. Using a continuous-time ordinary differential equation (ODE) modeling, these initial latent conditions are propagated to the respective latent states of the multi-band channel measurements at the same time instances for a latent alignment and a post-ODE fusion, and at their original time instances for measurement reconstruction. We derive a customized loss function based on the variational evidence lower bound (ELBO) that balances between the multi-band measurement reconstruction and continuous coordinate estimation. We evaluate the NDF framework using an in-house multi-band Wi-Fi testbed and demonstrate substantial performance improvements over a comprehensive list of single-band and multi-band baseline methods.

Conventional radar feature extraction faces limitations due to low spatial resolution, noise, multipath reflection, the presence of ghost targets, and motion blur. Such limitations can be exacerbated by nonlinear object motion, particularly from an ego-centric viewpoint. It becomes evident that to address these challenges, the key lies in exploiting temporal feature relation over an extended horizon and enforcing spatial motion consistency for effective association. To this end, this paper proposes SIRA (Scalable Inter-frame Relation and Association) with two designs. First, inspired by Swin Transformer, we introduce extended temporal relation, generalizing the existing temporal relation layer from two consecutive frames to multiple inter-frames with temporally regrouped window attention for scalability. Second, we propose motion consistency track with the concept of a pseudo-tracklet generated from observational data for better trajectory prediction and subsequent object association. Our approach achieves 58.11 [email protected] for oriented object detection and 47.79 MOTA for multiple object tracking on the Radiate dataset, surpassing previous state-of-the-art by a margin of +4.11 [email protected] and +9.94 MOTA, respectively.

This paper considers object detection with millimeter-wave (mmWave) Wi-Fi beam training frames, e.g., beacon frames, in a monostatic passive directional multi-gigabit (DMG) sensing configuration. We derive an explicit signal model that accounts for the preamble, frame-to-frame antenna gains, and clutter. Given the signal model, we develop a hypothesis testing-based object detection that directly leverages symbol-level preamble waveforms and explores the Kronecker structure between the range steering vector and the Doppler steering vector weighted by the antenna gain. Numerical results confirm the effectiveness of the proposed detector and evaluate the impact of frame-to-frame antenna gains due to the beam scanning.

Indoor monitoring systems are in high demand to protect vulnerable people, especially when they are alone at home, in nursing homes, hospitals, etc. Although surveillance systems in public spaces use cameras and microphones to find incidents, indoor monitoring in personal spaces needs to protect privacy. Such systems thus need to understand scenes without relying on direct sensing information, e.g., from audio-visual sensors, instead using indirect sensing information that is difficult to interpret by humans and may be insufficient to understand ongoing events precisely. To mitigate this drawback, this paper proposes a new indoor monitoring approach that attempts to realize scene understanding using only indirect sensors by transferring the learned inductive bias of a multimodal fusion model trained using direct and indirect sensing information to a model that uses only indirect information during inference. We collected direct (audio-visual) and indirect (infrared and Wi-Fi) sensing information of indoor human actions in daily life and manually annotated event captions. We build models that can generate event captions from various combinations of indirect and direct sensor data, and show that our transfer learning approach leads to significant improvements in caption quality when only indirect information is used at inference time.

In contrast to existing multi-band Wi-Fi fusion in a frame-to-frame basis for simple classification, this paper considers asynchronous sequence-to-sequence fusion between sub-7GHz channel state information (CSI) and 60GHz beam SNR for more challenging downstream tasks such as continuous regression. To handle the timing disparity between the two channel measurements, we extend our recently proposed dual-decoder neural dynamic (DDND) framework with latent ordinary differential equations (ODEs), align the distinct latent dynamic states at the same time instances, and introduce a post-ODE fusion framework. The resulting neural dynamic fusion (NDF) framework is trained in an end-to-end fashion with a modified variational autoencoder loss function. Evaluation over a newly collected in-house multi-band Wi-Fi dataset shows the advantage of the proposed NDF method over frame-based and DDND methods.

Due to the noise and low spatial resolution in automotive radar data, exploring temporal relations of learnable features over consecutive 2 radar frames has shown performance gain on downstream tasks (e.g., object detection and tracking) in our previous study [1]. In this paper, we further enhance radar perception by significantly extending the time horizon of temporal relations. To this end, we propose a scalable connective temporal radar (SCTR) method that consists of 1) a standard temporal relation layer (TRL), 2) a connective TRL with shifted window attention, and 3) a window merging operation, to facilitate feature connectivity between radar frames over an extended time interval. Our complexity analysis and comprehensive evaluation of the Radiate dataset demonstrate that the SCTR achieves a great tradeoff between the complexity and downstream detection performance.

With the advent of 5G and beyond communication technologies, the consumer IoT devices are evolving from current generation to next generation. Next generation IoT devices can support multiple communication interfaces and perform more functions. Accordingly, IoT network technologies must adapt to the emerging multi-link devices to improve network performance. Multipath TCP (MPTCP) is desired for networks with multi-link devices and has achieved success in computer networks. However, MPTCP has not been well studied for wireless networks. To that end, this paper presents MPTCP techniques for heterogeneous wireless IoT networks consisting of IEEE 802.15.4 nodes and 5G nodes. We propose a path builder, an adaptive congestion controller and an innovative path scheduler. We evaluated our MPTCP techniques under varying network configurations. Compared with conventional MPTCP, the proposed MPTCP can significantly reduce the number of packet transmissions, shorten packet delivery time, improve network throughput and packet delivery rate.

Indoor radar perception has seen rising interest due to affordable costs driven by emerging automotive imaging radar developments and the benefits of reduced privacy concerns and reliability under hazardous conditions (e.g., fire and smoke). However, existing radar perception pipelines fail to account for distinctive characteristics of the multi-view radar setting. In this paper, we propose Radar dEtection TRansformer (RETR), an extension of the popular DETR architecture, tailored for multi-view radar perception. RETR inherits the advantages of DETR, eliminating the need for hand-crafted components for object detection and segmentation in the image plane. More importantly, RETR incorporates carefully designed modifications such as 1) depth-prioritized feature similarity via a tunable positional encoding (TPE); 2) a tri-plane loss from both radar and camera coordinates; and 3) a learnable radar-to-camera transformation via reparameterization, to account for the unique multi-view radar setting. Evaluated on two indoor radar perception datasets, our approach outperforms existing state-of-the-art methods by a margin of 15.38+ AP for object detection and 11.91+ IoU for instance segmentation, respectively. Our implementation is available at https://github.com/merlresearch/radar-detection-transformer.

Sub-1 GHz Wireless Communications of LPWAN (Low Power Wide Area Network) are attracting attention in IoT applications. In addition to battery-powered devices, the number of grid-powered and solar-powered sensor devices using LPWAN are also rapidly increasing for various IoT applications. We aim to improve reliability and efficiency of IEEE 802.15.4 CSMA/CA mechanism in the network consisting of devices without power constraint. We propose Suspendable Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) algorithms for IEEE 802.15.4 to mitigate packet loss by channel access failure while maintaining compatibility with conventional IEEE 802.15.4 CSMA/CA. We have performed extensive simulations to valid the Suspendable CSMA/CA mechanism. Simulation results show that the proposed Suspendable CSMA/CA improves Packet Delivery Rate (PDR) by 9.7 points (89.9 % to 99.6 %) compared to the conventional IEEE 802.15.4g CSMA/CA and therefore, can lead higher spectrum efficiency for IoT applications operate in the limited Sub-l G Hz wireless bandwidth. The proposed Suspendable CSMA/CA mechanism has been also approved and adopted for the next IEEE 802.15.4 amendment by IEEE 802.15 Working Group.

We consider the problem of simultaneously learning a convex penalty function and its proximity operator for image reconstruction from incomplete measurements. Our goal is to apply Accelerated Proximal Gradient Method (APGM) using a learned proximity operator in place of the true proximity operator of the learned penalty function. Starting from a Gaussian image denoiser, we learn an associated penalty function and its proximity operator. The learned penalty function offers provable reconstruction guarantees, whereas access to its proximity operator presents the opportunity to achieve APGM convergence rates, which are faster than those of subgradient descent approaches.

This paper considers spatial-domain detector design for mutual interference mitigation among automotive MIMO-FMCW radars. This detector design is based on our previously derived interference signal model that fully accounts for the time-frequency incoherence and the slow-time code incoherence between the victim and interfering radars. Compared with our previous spatial-domain detector in [1], the proposed detector further exploits the structural property of both transmit and receive steering vectors of the interference for stronger interference mitigation. Preliminary numerical results confirm the performance of our proposed detector and show advantages over baseline detectors.

We leverage standards-compliant beam training measurements from commercial-of-the-shelf (COTS) 802.11ad/ay devices for localization of a moving object. Two technical challenges need to be addressed: (1) the beam training measurements are intermittent due to beam scanning overhead control and contention-based channel-time allocation, and (2) how to exploit underlying object dynamics to assist the localization. To this end, we formulate the trajectory estimation as a sequence regression problem. We propose a dual-decoder neural dynamic learning framework to simultaneously reconstruct Wi-Fi beam training measurements at irregular time instances and learn the unknown dynamics over the latent space in a continuous-time fashion by enforcing strong supervision at both the coordinate and measurement levels. The proposed method was evaluated on an in-house mmWave Wi-Fi dataset and compared with a range of baseline methods, including traditional machine learning methods and recurrent neural networks.

The software distribution in advanced IoT networks is inevitable. However, distributing software in multi-hop wireless networks consumes enormous communication bandwidth and can also suffer from reliability challenge. This paper proposes an innovative dynamic relay point (DRP) protocol to reduce the number of software packet transmissions. It also introduces a network condition based rateless coding scheme to improve the packet transmission reliability. The NS3 simulator is employed for performance evaluation. The proposed DRP protocol outperforms multi-point relay (MPR) baseline by reducing the software packet transmissions and improving the effective throughput.

We consider the object recognition problem in autonomous driving using automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective and robust in all- weather conditions for perception in autonomous driving. However, radar signals suffer from low angular resolution and precision in recognizing surrounding objects. To enhance the capacity of automotive radar, in this work, we exploit the temporal information from successive ego-centric bird-eye-view radar image frames for radar object recognition. We leverage the consistency of an object's existence and attributes (size, orientation, etc.), and propose a temporal relational layer to explicitly model the relations between objects within successive radar images. In both object detection and multiple object tracking, we show the superiority of our method compared to several baseline approaches.

This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localization as a multi-modal sequence regression problem, a multi-stream recurrent fusion method is proposed to combine the current hidden state of each modality in the context of recurrent neural networks while accounting for the modality uncertainty which is directly learned from its own immediate past states. The proposed method was evaluated on the large-scale SPAWC2021 multi-modal localization dataset and compared with a wide range of baseline methods including the trilateration method, traditional fingerprinting methods, and convolution network-based methods.

Beyond data communications, commercial-off-the-shelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment. In particular, spatial beam attributes that are inherently available in the 60-GHz IEEE 802.11ad/ay standards have shown to be effective in terms of overhead and channel measurement granularity for these indoor sensing tasks. In this paper, we investigate transfer learning to mitigate domain shift in human monitoring tasks when Wi-Fi settings and environments change over time. As a proof-of-concept study, we consider quantum neural networks (QNN) as well as classical deep neural networks (DNN) for the future quantum-ready society. The effectiveness of both DNN and QNN is validated by an in-house experiment for human pose recognition, achieving greater than 90% accuracy with a limited data size.

Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross- domain regression model. Specifically, a discrepant bi-regressor architecture is developed to maximize the difference of bi-regressor to discover uncertain target instances far from the source distribution, and then an adversarial training mechanism is adopted between feature extractor and dual regressors to produce domain-invariant representations. To further bridge the large domain gap, a domain- specific augmentation module is designed to synthesize two source-similar and target-similar inter- mediate domains to gradually eliminate the original domain mismatch. The empirical studies on two cross-domain regressive benchmarks illustrate the power of our method on solving the domain adaptive regression (DAR) problem.

With the explosive proliferation of wireless devices, our lives are improved by various applications supported by heterogeneous wireless technologies, such as WiFi and ZigBee. However, the coexistence of WiFi and ZigBee also results in the degradation of the network performance, which cannot be avoided if the WiFi devices are even unaware of the ambient ZigBee devices. To better accommodate the heterogeneous wireless devices, this paper presents X-Disco, the first cross-technology neighbor discovery mechanism, for a WiFi device to detect ZigBee neighbors, without modification to hardware or firmware. With the help of the recently proposed cross-technology communication, X-Disco enables a commodity WiFi device to trigger responses, containing ZigBee neighbor information, from the ambient ZigBee coordinators (including routers). Through exploring the WiFi PHY-layer information accessible by WiFi driver, X-Disco decodes the responded ZigBee messages and obtains the ZigBee neighbor information. To improve X-Disco's reliability, we also propose ZigBee neighbor validation and interruption mitigation to exclude hidden node terminals and mitigate the interference caused by the ambient WiFi traffic respectively. The evaluation of X-Disco is performed on the commodity devices (TP-Link WDR 4300 WiFi router, TelosB motes) and USRP B210. The results demonstrate X-Disco successfully detects nine ZigBee neighbors within 70ms in the office.

Complementary to the fine-grained channel state information (CSI) and coarse-grained received signal strength indicator (RSSI) measurements, the mid-grained spatial beam attributes [i.e., beam SNR (bSNR)] during the millimeter-wave (mmWave) beam training phase were recently repurposed for Wi-Fi sensing applications, such as human activity recognition and indoor localization. This article proposes a multiband Wi-Fi sensing framework to fuse features from both CSI from 5-GHz bands and the mid-grained bSNR at 60 GHz with feature granularity matching (GM) that pairs feature maps from the CSI and bSNR at different granularity levels with learnable weights. To address the issue of limited labeled training data, we propose to pretrain an autoencoder-based multiband Wi-Fi fusion network in an unsupervised fashion. For specific sensing tasks, separate sensing heads can be attached to the pretrained fusion network with fine-tuning. The proposed framework is thoroughly validated for three sensing applications using in-house experimental data sets: 1) pose recognition; 2) occupancy sensing; and 3) indoor localization. Comparison to a list of baseline methods demonstrates the effectiveness of GM. An ablation study is performed as a function of the amount of labeled data, the latent space dimension, and learning rates.

Recent advances in solving imaging inverse problems have witnessed the combination of deep learning models with classical image models for better signal representation. One such approach, DeepRED, combines the deep image prior (DIP) with the regularization by denoising (RED) framework to boost the performance of image deblurring and super resolution tasks. In this paper, we formulate DeepRED as a consensus equilibrium problem and set up a fixed-point algorithm for solving the equilibrium equations. We also derive sufficient conditions that the DIP generative prior should satisfy to ensure that the corresponding fixed-point operator is non-expansive. We then demonstrate that the fixed-point algorithm that solves the CE equations results in improved image reconstruction quality in a deblurring setting compared to state-of-the-art methods.

This paper introduces a B-spline chained ellipses model representation for extended object tracking (EOT) using high-resolution automotive radar measurements. With offline automotive radar training datasets, the proposed model parameters are learned using the expectation-maximization (EM) algorithm. Then the probabilistic multi-hypothesis tracking (PMHT) along with the unscented transform (UT) is proposed to deal with the nonlinear forward-warping coordinate transformation, the measurement-to-ellipsis association, and the state update step. Numerical validation is provided to verify the effectiveness of the proposed EOT framework with automotive radar measurements.

In addition to coarse-grained received signal strength indicator (RSSI) measurements and fine-grained channel state information (CSI), a mid-grained channel measurement - spatial beam signal-to-noise ratios (SNRs) - that are inherently available during the millimeter wave (mmWave) beam training as defined in mmWave fifth-generation (5G) and IEEE 802.11ad/ay standards, were recently utilized for fingerprinting-based indoor localization. In this paper, we extend the beam SNR fingerprinting-based indoor localization to more challenging scenarios in non-line-of-sight (NLOS) propagation. Particularly, multi-channel beam covariance matrix (BCM) images are used as the fingerprinting signature and fed into a beam covariance learning (BCL) network to identify the position and estimate the coordinate. Using our in-house testbed with commercial off-the-shelf (COTS) 60-GHz WiFi routers, real-world mmWave BCMs are fingerprinted in several NLOS locations-of-interest in an enclosed L-shape conference room. Given a fingerprinting grid-size of 30 cm, preliminary performance evaluation shows the position classification accuracy can be above 90% using classical classification methods and a coordinate estimation error around 11 cm with the BCL approach.

This paper considers object detection in the case of imperfect waveform separation, in the context of automotive radars with a slow-time MIMO-FMCW signaling scheme. We develop an explicit signal model that accounts for waveform separation residuals and propose a Kronecker subspace-based object detector in the framework of generalized likelihood ratio test (GLRT). Our exact theoretical analysis under both hypotheses shows that the proposed detector holds the desired property of constant false alarm rate (CFAR). Numerical simulations validate our proposed object detection scheme.

Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements. The effectiveness of the proposed algorithm is verified in simulations.

Millimeter-wave (mmWave) communications is an emerging technology expected to bring unprecedented data rates and throughput. WiFi operating at unlicensed 60 GHz range is envisioned to become an ubiquitous technology and the IEEE 802.11ad standard is an initial attempt in that direction. Al-though spatial and temporal resolution of mmWave signals make them suitable for location estimation, a variety of hardware-related issues and commonly encountered difficulties in extract-ing channel measurements from commercial chipsets, challenge opportunistic use of commercial mmWave WiFi chips for indoor localization. We propose in this paper an indoor localization method that fingerprints transmit beam indices that a pair of WiFi transceivers employ to establish a mmWave link, as well as the resulting received signal strength (RSS). In particular, we develop an algorithm that learns possible probabilistic models from the fingerprint data and leverages them to perform indoor localization in the online stage. The proposed algorithm is experimentally evaluated using commercial 60 GHz WiFi routers in an office space area and localization error of around 30 cm is demonstrated.

In this paper, we propose a variational Bayesian inference approach for a low-complexity symbol detection for massive MIMO systems with symbol- dependent transmit-side impairments. This study is motivated by observations that realworld communication transceivers are often affected by the hardware impairments, such as non-linearities of power amplifiers, I/Q imbalance, phase drifts due to non-ideal oscillators, and carrier frequency offsets. Particularly, symbol-dependent perturbations are fully accounted into the designed hierarchical signal model as unknown model parameters. The developed variational Bayesian symbol detector is able to learn the unknown perturbations in an iterative fashion. Numerical evaluation confirms the effectiveness of the proposed approach.

Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements from the MAC layer. In this paper, we propose to use an intermediate channel measurement - spatial beam signal-to-noise ratios (SNRs) that are inherently available during the beam training phase as defined in the IEEE 802.11ad standard - to construct the feature space for location-and-orientation-dependent fingerprinting database. We build a 60GHz experimental platform consisting of three access points and one client using commercial-off-the-shelf routers and collect realworld beam SNR measurements in an office environment during regular office hours. Both position/orientation classification and coordinate estimation are considered using classic machine learning approaches. Comprehensive performance evaluation using real-world beam SNRs demonstrates that the classification accuracy is 99.8% if the location is only interested, while the accuracy is 98.6% for simultaneous position-and-orientations classification. Direct coordinate estimation gives an average root-mean-square error of 17.52 cm and 95% of all coordinate estimates are less than 26.90 cm away from corresponding true locations. This concept directly applies to other mmWave band (e.g., 5G) devices where beam training is also required.

This paper studies parameter estimation of a coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, a newly introduced model motivated by industrial applications. Particularly, we analytically evaluate the estimation performance (or performance loss) via the misspecified Cramér-Rao bound (CRB) when system designers choose existing efficient estimation algorithms designed for an independent (decoupled) mixture model due to hardware limits. Our analysis provides an analytical tool to conveniently evaluate performance loss if the implemented system ignores the coupling effect. The achievability of the misspecified CRB is verified by numerical examples.

State-of-the-art base stations can be equipped with a massively large number of antenna elements, often several hundreds of elements, thanks to the rapid advancement of wideband radio-frequency (RF) analog circuits and compact antenna design techniques. With massive antenna systems, a relatively large number of users can be served at the same time by means of analog and digital beamforming and spatial multiplexing. We investigate such a large-scale multi-user multiple-input multiple-output (MU-MIMO) wireless system employing an orthogonal frequency- division multiplexing (OFDM)-based downlink transmission scheme. The use of OFDM causes a high peak-to-average power ratio (PAPR), which usually calls for expensive and power-inefficient RF components at the base station. In this paper, we propose a nullspace vector perturbation (VP) which integrates both nonlinear lattice and linear subspace precoding approaches. By exploiting high degrees of freedom available in massive MU-MIMO OFDM systems, the signal PAPR can be significantly reduced with the proposed method. We also introduce a Gaussian process (GP) regression approach to be robust against the imperfect channel knowledge, which is required for the VP operation, in time-varying fading channels. Our analysis of outage capacity reveals that the proposed VP with GP regression offers a significant improvement in sum-rate spectral efficiency while reducing the PAPR.

A growing number of applications may benefit from embedding receiver with the capability to separate collided packets directly on a physical layer. For example, in IoT's random access channel scenario, a number of IoT devices are placed in the same cell, occasionally transmit messages, and are assigned non-orthogonal channel resources, giving rise to packet collisions. While channel access techniques for sharing a common channel are usually employed in most multi-user communication systems, they cause channel underutilization and increased latency. This paper considers a scenario where multiple users asynchronously transmit packets over a shared channel and are not subject to a random access mechanism, or the mechanism itself fails to prevent packet collisions. Assuming that each packet consists of a common preamble and information bearing payload, and experiences random delay, frequency offset, phase noise variation and block flat fading channel, we propose a packet separation algorithm which recovers collided packets, estimates their parameters and detects corresponding payload symbols. The performance of the proposed method is validated using simulations and benchmarked against bounds.

In this paper, we propose a Bayesian inference approach to extract the binary reflectance pattern of samples from compressed measurements in the terahertz (THz) frequency band. Compared with existing compressed THz imaging methods relying on the sparsity of the reflectance pattern, the proposed Bayesian approach exploits the non-negative binary nature of the reflectance without any assumption on its spatial pattern information and enables a pixel-wise iterative inference approach for fast signal recovery. Numerical evaluation confirms the effectiveness of the proposed approach.

We consider the problem of channel estimation for millimeter wave (mmWave) systems, where, to minimize the hardware complexity and power consumption, an analog transmit beamforming and receive combining structure with only one radio frequency chain at the base station and mobile station is employed. Most existing works for mmWave channel estimation exploit sparse scattering characteristics of the channel. In addition to sparsity, mmWave channels may exhibit angular spreads over the angle of arrival, angle of departure, and elevation domains. In this paper, we show that angular spreads give rise to a useful low-rank structure that, along with the sparsity, can be simultaneously utilized to reduce the sample complexity, i.e., the number of samples needed to successfully recover the mmWave channel. Specifically, to effectively leverage the joint sparse and low-rank structure, we develop a two-stage compressed sensing method for mmWave channel estimation, where the sparse and low-rank properties are respectively utilized in two consecutive stages, namely, a matrix completion stage and a sparse recovery stage. Our theoretical analysis reveals that the proposed two-stage scheme can achieve a lower sample complexity than a conventional compressed sensing method that exploits only the sparse structure of the mmWave channel. Simulation results are provided to corroborate our theoretical results and to show the superiority of the proposed two-stage method.

In millimeter wave (mmWave) communication, analog and hybrid beamforming systems are proposed to reduce the number of RF chains when a large antenna array is utilized to achieve a high beamforming gain. In this paper, a new transmission scheme is first proposed by leveraging the hardware architecture of analog and hybrid beamforming to improve the spectral efficiency. The new scheme is called spatial scattering modulation (SSM), since it exploits the spatial scattering dimension to modulate information bits. And then, an adaptive transmission strategy (ATS) which chooses the best transmission scheme under instantaneous channel state information, is proposed to improve the performance. Link-level simulation results demonstrate the superiority of the proposed ATS in all the simulated signal-to-noise (SNR) values.

In this paper, channel estimation in millimeter wave (mmWave) communication systems is considered. In contrast to prevailing mmWave channel estimation methods exploiting the sparsity nature of the channel, we move one step further by exploiting the joint AoD-AoA angular spread. By formulating the channel estimation as a block-sparse signal recovery with an underlying two-dimensional cluster feature, we propose a two-dimensional sparse Bayesian learning method without a priori knowledge of two-dimensional angular spread patterns. It essentially couples the channel path power at one angular direction with its two-dimensional AoD-AoA neighboring directions. Compared with existing sparse mmWave channel estimation methods, the proposed method is numerically verified to reduce the training overhead and channel estimation error.

This paper considers speed estimation for contactless electromagnetic (EM) encoder system with a moving read-head and spatially periodic placed reflectors. We first introduce a new signal model to capture reflected signals from these spatially periodic placed reflectors. Then an instantaneous phase-based speed estimator is proposed by using the phase unwrapping technique followed by a nonlinear least square method for motion-related parameters. The proposed speed estimator is verified by 1) Monte-Carlo simulations to confirm its statistical efficiency as the numerical mean squared errors approach to corresponding Cramér-Rao bounds and 2) a semi-analytical dataset by accounting for real system specificaltions such as the antenna beampattern, 3-dB beamwidth, and noise.

This paper considers parameter estimation of a hybrid sinusoidal frequency modulated (FM) and polynomial phase signal (PPS) from a finite number of samples. We first show limitations of an existing method, the high-order ambiguity function (HAF), and then propose a new method by adopting the high-order phase function which was originally designed for the pure PPS. The proposed method estimates parameters of interest from peak locations in the time-frequency rate domain, which are less perturbed by the noise than peak values used by the HAF-based method. Numerical evaluation shows the proposed method can handle the hybrid FM-PPS signal with low sinusoidal frequency and improve estimation accuracy in terms of mean squared error for several orders of magnitude.

This letter introduces a new coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, motivated by real-world applications, for example, contactless linear encoders. Specifically, the coupling is introduced to express the sinusoidal FM frequency as a function of the PPS-related parameters. Given the coupling mixture, it generalizes two existing models: the pure PPS model and the independent mixture model. Performance bounds of parameter estimation for the coupled mixture model are established in terms of the Cramér-Rao bound (CRB). Unlike the pure PPS case, the derived CRB shows its dependence on the PPS-related and sinusoidal FM-related parameters due to the coupling mixture. On the other hand, the derived CRBs for the PPS-related parameters are lower than their counterparts of the independent mixture model, as the sinusoidal FM frequency provides additional information on the PPS parameters.

This paper considers adaptive signal detection in stochastic homogeneous environments where the disturbance covariance matrix of both test and training signals, R, is assumed to be a random matrix with a priori knowledge of R. Unlike existing detectors assuming a known hyperparameter associated with R, a knowledge-aided detector with the capability of automatic weighting is considered by accounting for the uncertainty of the prior knowledge. Specifically, the generalized likelihood ratio test (GLRT) is utilized to develop the test statistic, along with the maximum marginal likelihood (MML) estimation of the hyperparameter. The proposed KA-MML-GLRT detector is evaluated by numerical simulations and the results show improved detection performance over conventional and knowledge-aided detectors, especially in the case of limited training signals and inaccurate prior knowledge.

In this paper, a knowledge-aided parametric adaptive matched filter (KA-PAMF) is proposed that utilizing both observations (including the test and training signals) and a priori knowledge of the spatial co-variance matrix. Unlike existing KA-PAMF methods, the proposed KA-PAMF is able to automatically adjust the combining weight of a priori covariance matrix, thus gaining enhanced robustness against uncertainty in the prior knowledge. Meanwhile, the proposed KA-PAMF is significantly more efficient than its KA non-parametric counterparts when the amount of training signals is limited. One distinct feature of the proposed KA-PAMF is the inclusion of both the test and training signals for automatic determination of the combining weights for the prior spatial covariance matrix and observations. Numerical results are presented to demonstrate the effectiveness of the proposed KA-PAMF, especially in the limited training scenarios.

The estimation of slowness (the reciprocal of velocity) and attenuation dispersion of borehole acoustic and surface seismic data is key to a variety of applications. The emerging broadband approach for dispersion extraction of multiple modes has shown significant advantages over traditional narrowband approaches. In this letter, Cramér–Rao bounds (CRBs) are established to characterize the best achievable performance of any unbiased broadband estimator of the dispersion. One noteworthy observation from the derived CRBs is that the same estimation accuracy can be achieved between the angular wavenumber and the attenuation and, between the group slowness and the attenuation rate. The broadband CRB is also shown to include the narrowband CRB as a special case and it quantifies the performance benefit of the broadband approach.

This paper considers a parametric approach for adaptive multichannel signal detection, where the disturbance is modeled by a multichannel auto-regressive (AR) process. Motivated by the fact that a symmetric antenna geometry usually yields a persymmetric structure on the covariance matrix of disturbance, a new persymmetric AR (PAR) modeling for the disturbance is proposed and, accordingly, a persymmetric parametric adaptive matched filter (Per-PAMF) is developed. The developed Per-PAMF, while allowing a simple implementation like the traditional PAMF, extends the PAMF by developing the maximum likelihood (ML) estimation of unknown nuisance (disturbance-related) parameters under the persymmetric constraint. Numerical results show that the Per-PAMF provides significantly better detection performance than the conventional PAMF and other non-parametric detectors when the number of training signals is limited.

This study considers the problem of detecting a multi-channel signal of range-spread targets in a homogeneous environment, where the disturbances in both test signal and training signals share the same covariance matrix. To this end, a generalised parametric Rao (GP-Rao) test is developed by modelling the disturbance as a multi-channel auto-regressive process. The GP-Rao test uses less training data and is computationally more efficient, when compared with conventional covariance matrix-based solutions. The theoretical detection performance of the GP-Rao test is characterised in terms of the asymptotic distribution under both hypotheses. Numerical results indicate that the proposed GP-Rao test attains asymptotically the constant false alarm rate property. Numerical results show that the GP-Rao test achieves better detection performance and uses significantly less training signals than the covariance matrix-based approach.

In this letter, we consider the detection of a multichannel signal with an unknown amplitude in colored noise, when there is a covariance mismatch between the null and alternative hypotheses. Specifically, the covariance mismatch is caused by a target-induced subspace interference that is present only under the alternative hypothesis. According to the signal model, we propose a detector involving the following steps. The observation is first projected to the orthogonal complement of the signal to be detected, followed by a second projection to the interference subspace. Then, the energy of the doubly projected signal (residual) is computed. If the residual energy is small, the proposed detector reduces to the standard matched filter (MF), which ignores the subspace interference; otherwise, a modified test statistic is employed for additional interference cancellation. Simulation results are presented to demonstrate the effectiveness of the proposed detector.

This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals,${\bf R}$, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal,${\bf R}_{0}$, is assumed to be equal to$\lambda{\bf R}$, i.e.,${\bf R}_{0}=\lambda{\bf R}$. On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor$\lambda$between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.

This work proposes a low-complexity space-time adaptive processing (STAP) algorithm for sensing applications built on a moving platform in the presence of strong clutters. The proposed algorithm achieves low-complexity computation via two steps. First, it utilizes improved fast approximated power iteration methods to compress the data into a much smaller subspace. To further reduce the computational complexity, a progressive singular value decomposition (SVD) approach is employed to update the inverse of the covariance matrix of the compressed data. As a result, the proposed low-complexity STAP algorithm can achieve order-of-magnitude computational complexity reduction as compared to conventional STAP algorithms. Simulation results are shown to confirm the validity of the proposed algorithm.

This paper considers the problem of knowledge-aided space-time adaptive processing (STAP) in nonhomogeneous environments, where the covariance matrices of the training and test signals are assumed random and different from each other. A Bayesian detector is proposed by incorporating somea prioriknowledge of the disturbance covariance matrices, and exploring their inherent block-Toeplitz structure. Specifically, the block-Toeplitz structure of the covariance matrix allows us to model the training signals as a multichannel auto-regressive (AR) process. The resulting detector is referred to as the Bayesian parametric adaptive matched filter (B-PAMF) which, compared with nonparametric Bayesian detectors, entails a lower training requirement and alleviates the computational complexity. Numerical results show that the proposed B-PAMF detector outperforms the standard PAMF test in nonhomogeneous environments.

For a high-order polynomial-phase signal (PPS), instantaneous frequency rate (IFR), which is defined as the second derivative of the phase, is estimated by using an estimator with only a second-order nonlinearity. Compared to high-order phase function (HPF), the proposed IFR estimator presents improved performance including smaller mean-squared error (MSE) and lower SNR threshold. Statistical analysis via a multivariate first-order perturbation analysis is derived for the estimate bias and MSE. Numerical results verify our analytical results.

Instantaneous frequency rate (IFR) estimation for high-order polynomial phase signals (PPSs) is considered. Specifically, an IFR estimator with only a second-order nonlinearity is proposed. The asymptotic mean-squared error (MSE) of the proposed IFR estimator is obtained via a multivariate first-order perturbation analysis. Our results show that the proposed estimator yields a smaller MSE and a lower signal-to-noise ratio (SNR) threshold than a popular IFR estimator involving higher nonlinearity. The proposed IFR estimator is also extended to estimate the phase parameters of a PPS. Numerical studies are presented to illustrate the performance of the proposed estimator.

In this paper, an extended algorithm for parameter estimation of quadratic FM signal is derived by exploring the time diversity in the cubic phase (CP) function. The performance of the proposed algorithm is analyzed in terms of estimate bias and variance, and compared with other methods. Although the proposed algorithm employs a fourth-order nonlinearity which results in higher threshold SNR, it provides a number of advantages, such as low mean-square error (MSE) for the estimates at high SNR and simple extension for multicomponent signals. Extension to cubic FM signals is also discussed. The theoretical analysis is verified by the simulation results.

The cubic phase function (CPF) is recently proposed to estimate the instantaneous frequency rate (IFR) for the polynomial phase signals (PPS) in a Gaussian noise environment. However, for an impulse noise environment, the performance of the standard CPF degrades significantly. In addition, the resulting noise in the CPF is a mixture of the Gaussian and impulse noise even for a Gaussian input noise. Hence, a modified robust CPF algorithm based on the alpha-trimmed form of L-estimation is proposed in this paper. Extension to the robust higher-order phase function (HPF) is also derived. Simulation results demonstrate that the robust CPF outperforms the standard CPF in impulse noise and is also valid to estimate the IFR in Gaussian noise