This work prioritizes the suppression of mainlobe deceptive jamming within coarray Frequency Diverse Nested Array (FDNA) Multiple-Input Multiple-Output (MIMO) radar architectures. To overcome the range resolution limitation in the conventional FDA, a novel FDNA structure is proposed. Leveraging differential processing for virtual aperture extension, the design enables precise discrimination of mainlobe deceptive jamming and target. Subsequently, a Spatial Smoothing-based Minimum Variance Distortionless Response (SS-MVDR) beamformer is introduced to eliminate the contamination of training samples by target data during jamming suppression. Furthermore, a frequency offset selection strategy is developed to simultaneously suppress both rapid and delayed repeated jamming. The efficacy of the proposed scheme in suppressing mainlobe deceptive jamming is confirmed by simulation results.

This letter proposes a time-diverse wide-swath imaging radar transmit waveform optimization design method. First, based on the imaging geometry and zebra maps, we obtained the angles corresponding to the range occlusion zone. Then, using the mapping characteristics between the range frequency and beam scanning angle in time-diverse array (TDA) radar, as well as the occlusion region information, we performed a 2-D optimization design of the transmit waveform in the fast time and range frequency domain. Finally, the limited energy can be effectively skipped over the occlusion regions and flexibly allocated to the observable areas. Compared with the traditional TDA system, this method achieves a larger imaging swath and energy utilization efficiency. The effectiveness of the proposed method is verified through simulation experiments.

The existence of range ambiguity and range dependence will seriously deteriorate the performance of space-time adaptive processing (STAP). In this regard, an adaptive range-ambiguous clutter separation method suitable for the element-pulse coding (EPC)-multiple-input multiple-output (MIMO) radar is developed in this letter. By introducing the EPC factor in both transmit elements and pulses, the clutter located in different range-ambiguous regions can be distinguished in the transmit spatial frequency dimension. Particularly, to ensure the separated performance of range-ambiguous clutter, the EPC factor is designed. Moreover, an approach on the basis of reweighted atomic norm minimization (RANM) is developed to separate the range-ambiguous clutter, leveraging the transmit spatial frequencies of clutter located in various range ambiguity areas. Furthermore, after clutter separation, the clutter is canceled via STAP individually in each range ambiguous region. A series of simulation results validate the efficacy of the proposed approach.

Correcting the range migration of moving targets and compensating for the coupled phase errors are crucial for ground moving target imaging (GMTIm). Most methods achieve target focusing through parameter search operations with substantial computational complexity. In addition, the inability to address energy spreading caused by the higher order motion further limits the applicability of these methods. To overcome these issues, this article proposes a novel refocusing method without motion parameter estimation for arbitrarily moving ground targets. First, a range frequency difference (RFD) function without parameter estimation is constructed in the range frequency domain. Then, by conjugate multiplication with the RFD function, the coupling between range frequency and slow time of target can been removed. With an appropriate frequency interval selected, the target can also be azimuthally focused. In addition, some practical factors in applications are analyzed in detail. Compared with the traditional methods, the proposed method effectively addresses range migration and azimuth defocusing caused by higher order phase errors. Also, it achieves target focusing without search operations, despite the existence of Doppler center ambiguity and Doppler spectrum splitting. In addition, since it only requires fast Fourier transform (FFT), inverse FFT (IFFT), and matrix multiplication operations, this method achieves high computational efficiency. The efficacy of the proposed method is confirmed through the examination of both simulated and real data.

In the rapidly evolving field of video traffic, ensuring a smooth video streaming experience for users is critical for network operators. Accurately and promptly detecting stalling events, a significant indicator of poor quality of experience, remains challenging due to varying detection time resolutions in existing techniques, which often detect stalls every video chunk, or every five or ten seconds. This paper makes three key contributions. First, we introduce the concept of detection granularities to enable fair performance comparisons and reveal their impact on detection performance from the data sampling perspective. Second, we propose a novel feature extraction approach that captures both packet-level and chunk-level features in a unified sequential manner to effectively detect stalling events. Third, a novel sample reweighting method is proposed to address the detection timeliness problem by focusing more on difficult samples around stalling starting or ending. Experimental results on both video-on-demand and live streaming traces demonstrate that our feature extraction approach achieves an average improvement of 5.3% in f1-score, 4.7% in coverage rate, and reduces stalling response time by 0.4 seconds compared to existing techniques. Additionally, the sample reweighting method further improves the detection sensitivity without compromising f1-scores for all detection techniques.

Keyword Spotting (KWS) plays a vital role in human-computer interaction for smart on-device terminals and service robots.It remains challenging to achieve the trade-off between small footprint and high accuracy for KWS task.In this paper, we explore the application of multi-scale temporal modeling to the smallfootprint keyword spotting task.We propose a multi-branch temporal convolution module (MTConv), a CNN block consisting of multiple temporal convolution filters with different kernel sizes, which enriches temporal feature space.Besides, taking advantage of temporal and depthwise convolution, a temporal efficient neural network (TENet) is designed for KWS system 1 .Based on the purposed model, we replace standard temporal convolution layers with MTConvs that can be trained for better performance.While at the inference stage, the MTConv can be equivalently converted to the base convolution architecture, so that no extra parameters and computational costs are added compared to the base model.The results on Google Speech Command Dataset show that one of our models trained with MTConv performs the accuracy of 96.8% with only 100K parameters.