Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System

The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared to existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.Comment: 8 pages, 7 figure

BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning

Understanding the global optimality in deep learning (DL) has been attracting more and more attention recently. Conventional DL solvers, however, have not been developed intentionally to seek for such global optimality. In this paper we propose a novel approximation algorithm, BPGrad, towards optimizing deep models globally via branch and pruning. Our BPGrad algorithm is based on the assumption of Lipschitz continuity in DL, and as a result it can adaptively determine the step size for current gradient given the history of previous updates, wherein theoretically no smaller steps can achieve the global optimality. We prove that, by repeating such branch-and-pruning procedure, we can locate the global optimality within finite iterations. Empirically an efficient solver based on BPGrad for DL is proposed as well, and it outperforms conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the tasks of object recognition, detection, and segmentation

Unsupervised Deep Feature Transfer for Low Resolution Image Classification

In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. We use pre-trained convenet to extract features for both high- and low-resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. A SVM classifier is learned directly using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution images, and transfers the distinguishing features from a well-structured source domain (high resolution features space) to a not well-organized target domain (low resolution features space). Extensive experiments on VOC2007 test set show that the proposed method achieves significant improvements over the baseline of using feature extraction.Comment: 4 pages, accepted to ICCV19 Workshop and Challenge on Real-World Recognition from Low-Quality Images and Video

STARS Enabled Integrated Sensing and Communications

A simultaneously transmitting and reflecting intelligent surface (STARS) enabled integrated sensing and communications (ISAC) framework is proposed, where the whole space is divided by STARS into a sensing space and a communication space. A novel sensing-at-STARS structure, where dedicated sensors are installed at the STARS, is proposed to address the significant path loss and clutter interference for sensing. The Cramer-Rao bound (CRB) of the 2-dimension (2D) direction-of-arrivals (DOAs) estimation of the sensing target is derived, which is then minimized subject to the minimum communication requirement. A novel approach is proposed to transform the complicated CRB minimization problem into a trackable modified Fisher information matrix (FIM) optimization problem. Both independent and coupled phase-shift models of STARS are investigated: 1) For the independent phase-shift model, to address the coupling of ISAC waveform and STARS coefficient in the modified FIM, an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) framework is conceived; 2) For the coupled phase-shift model, based on the PDD framework, a low complexity alternating optimization algorithm is proposed to tackle coupled phase-shift constants by alternatively optimizing amplitude and phase-shift coefficients in closed-form. Finally, the numerical results demonstrate that: 1) STARS significantly outperforms the conventional RIS in CRB under the communication constraints; 2) The coupled phase-shift model achieves comparable performance to the independent one for low communication requirements or sufficient STARS elements; 3) It is more efficient to increase the number of passive elements of STARS rather than the active elements of the sensor; 4) High sensing accuracy can be achieved by STARS using the practical 2D maximum likelihood estimator compared with the conventional RIS.Comment: 30 pages, 8 figure

Near-Field Integrated Sensing and Communications

A near-field integrated sensing and communications (ISAC) framework is proposed, which introduces an additional distance dimension for both sensing and communications compared to the conventional far-field system. In particular, the Cramer-Rao bound for the near-field joint distance and angle sensing is derived, which is minimized subject to the minimum communication rate requirement of each user. Both fully digital antennas and hybrid digital and analog antennas are investigated. For fully digital antennas, a globally optimal solution of the ISAC waveform is obtained via semidefinite relaxation. For hybrid antennas, a high-quality solution is obtained through two-stage optimization. Numerical results demonstrate the performance gain introduced by the additional distance dimension of the near-field ISAC over the far-field ISAC.Comment: 5 pages, 4 figure

Beamfocusing Optimization for Near-Field Wideband Multi-User Communications

A near-field wideband communication system is studied, wherein a base station (BS) employs an extremely large-scale antenna array (ELAA) to serve multiple users situated within its near-field region. To facilitate the near-field beamfocusing and mitigate the wideband beam split, true-time delayer (TTD)-based hybrid beamforming architectures are employed at the BS. Apart from the fully-connected TTD-based architecture, a new sub-connected TTD-based architecture is proposed for enhancing energy efficiency. Three wideband beamfocusing optimization approaches are proposed to maximize spectral efficiency for both architectures. 1) Fully-digital approximation (FDA) approach: In this approach, the TTD-based hybrid beamformers are optimized to approximate the optimal fully-digital beamformers using block coordinate descent. 2) Penalty-based FDA approach: In this approach, the penalty method is leveraged in the FDA approach to guarantee the convergence to a stationary point of the spectral maximization problem. 3) Heuristic two-stage (HTS) approach: In this approach, the closed-form TTD-based analog beamformers are first designed based on the outcomes of near-field beam training and the piecewise-near-field approximation. Subsequently, the low-dimensional digital beamformer is optimized using knowledge of the low-dimensional equivalent channels, resulting in reduced computational complexity and channel estimation complexity. Our numerical results unveil that 1) the proposed approaches effectively eliminate the near-field beam split effect, and 2) compared to the fully-connected architecture, the proposed sub-connected architecture exhibits higher energy efficiency and imposes fewer hardware limitations on TTDs and system bandwidth.Comment: 30 pages, 11 figure

TTD Configurations for Near-Field Beamforming: Parallel, Serial, or Hybrid?

True-time delayers (TTDs) are popular components for hybrid beamforming architectures to combat the spatial-wideband effect in wideband near-field communications. A serial and a hybrid serial-parallel TTD configuration are investigated for hybrid beamforming architectures. Compared to the conventional parallel configuration, the serial configuration exhibits a cumulative time delay through multiple TTDs, which potentially alleviates the maximum delay requirements on the TTDs. However, independent control of individual TTDs becomes impossible in the serial configuration. In this context, a hybrid TTD configuration is proposed as a compromise solution. Furthermore, a power equalization approach is proposed to address the cumulative insertion loss of the serial and hybrid TTD configurations. Moreover, the wideband near-field beamforming design for different configurations is studied for maximizing the spectral efficiency in both single-user and multiple-user systems. 1) For single-user systems, a closed-form solution for the beamforming design is derived. The preferred user locations and the required maximum time delay of each TTD configuration are characterized. 2) For multi-user systems, a penalty-based iterative algorithm is developed to obtain a stationary point of the spectral efficiency maximization problem for each TTD configuration. In addition, a mixed-forward-and-backward (MFB) implementation is proposed to enhance the performance of the serial configuration. Our numerical results confirm the effectiveness of the proposed designs and unveil that i) compared to the conventional parallel configuration, both the serial and hybrid configurations can significantly reduce the maximum time delays required for the TTDs and ii) the hybrid configuration excels in single-user systems, while the serial configuration is preferred in multi-user systems.Comment: 13 pages, 8 figure