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Monocular 3D object detection is very challenging in autonomous driving due to the lack of depth information. This paper proposes a one-stage monocular 3D object detection algorithm based on multi-scale depth stratification, which uses the anchor-free method to detect 3D objects in a per-pixel prediction. In the proposed MDS-Net, a novel depth-based stratification structure is developed to improve the network's ability of depth prediction by establishing mathematical models between depth and image size of objects. A new angle loss function is then developed to further improve the accuracy of the angle prediction and increase the convergence speed of training. An optimized soft-NMS is finally applied in the post-processing stage to adjust the confidence of candidate boxes. Experiments on the KITTI benchmark show that the MDS-Net outperforms the existing monocular 3D detection methods in 3D detection and BEV detection tasks while fulfilling real-time requirements., Comment: 9 pages

The linear frequency modulated (LFM) frequency agile radar (FAR) can synthesize a wide signal bandwidth through coherent processing while keeping the bandwidth of each pulse narrow. In this way, high range resolution profiles (HRRP) can be obtained without increasing the hardware system cost. Furthermore, the agility provides improved both robustness to jamming and spectrum efficiency. Motivated by the Newtonalized orthogonal matching pursuit (NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed as NOMP-FAR is designed to process each coarse range bin to extract the HRRP and velocities of multiple targets, including the guide for determining the oversampling factor and the stopping criterion. In addition, it is shown that the target will cause false alarm in the nearby coarse range bins, a postprocessing algorithm is then proposed to suppress the ghost targets. Numerical simulations are conducted to demonstrate the effectiveness of NOMP-FAR.

The recovery of a low rank matrix from a subset of noisy low-precision quantized samples arises in several applications such as collaborative filtering, intelligent recommendation and millimeter wave channel estimation with few bit ADCs. In this paper, a generalized sparse Bayesian learning (Gr-SBL) combining expectation propagation (EP) is proposed to solve the matrix completion (MC), which is termed as MC-Gr-SBL. The MC-Gr-SBL automatically estimates the rank, the factors and their covariance matrices, and the noise variance. In addition, MC-Gr-SBL is proposed to solve the two dimensional line spectral estimation problem by incorporating the MUSIC algorithm. Finally, substantial numerical experiments are conducted to verify the effectiveness of the proposed algorithm.

Faced with the low signal-to-noise ratio (SNR) and the presence of time-varying interference in passive radar target detection, multi-channel joint detection is considered as an effective solution, performance of which largely depends on the accurate weight setting in fusion of multichannel detection results. Our previous work focuses on the weight setting based on exact interference power detection, which is complex in general. We then propose to set a weight based on interference state estimation over each frequency channel in this paper. We model the interference state by time series and conditioned probability, and develop an effective interference state estimation method. Numerical simulation results verify the performance of the proposed strategy.

The increasing demand of mobile multimedia services has motivated the use of new communication technologies that could improve the utilization of spectrum resource. In this paper, we study a mobile communication network that adopts phased array technology to improve the reuse of spatial spectrum resource, in which each base station (BS) communicates with multiple mobile users (MUs) simultaneously in the same frequency channel by applying beamforming. We understand from analysis that in such a network, the parallel communications supported by phased array technology could produce interference to each other. Moreover, the interference level largely depends on the angles of beams. We then propose a novel networking method that let a MU select a proper BS according to both distance, which largely decides receiving power, and the beam angle, which is the main factor that decides co-channel interference, as compared to conventional networking scheme that requests a MU to select a proper BS simply according to distance. Both analysis results and simulation results show that the proposed scheme obtains advantage over the conventional scheme in terms of the network throughput.