Frequency modulation continuous wave (FMCW) Lidar inevitably suffers from vibration and nonlinear frequency modulation, which influences the ranging and imaging results. In this paper, we analyze the impact of vibration error coupled with nonlinearity error on ranging for FMCW Lidar, and propose a purely theoretical approach that simultaneously compensates for time-varying vibration and nonlinearity in one-period triangular FMCW (T-FMCW) signals. We first extract the localized characteristics of dechirp signals in time-frequency domain by using a second-order synchro-squeezing transform (second-order SST), and establish an instantaneous ranging model based on second-order SST which can characterize the local distributions of time-varying errors. Second, we estimate the nonlinearity error by using time-frequency information of an auxiliary channel and then preliminarily eliminate the error from the instantaneous measurement range. Finally, we construct a particle filtering (PF) model for T-FMCW using the instantaneous ranging model to compensate for the time-varying vibration error and the residual nonlinearity error, and calculate the range of target by using triangular symmetry relations of T-FMCW. Experimental tests prove that the proposed method can accurately estimate the range of target by compensating for the time-varying vibration and the nonlinearity errors simultaneously in one-period T-FMCW signal.

Autonomous driving requires vehicles to achieve fine detection of objects in the surrounding environment, especially living pedestrians. Nevertheless, in real world road environments there are living pedestrians and roadside portrait billboards. Existing vision-based object detection technologies fail to ac-curately distinguish living pedestrians from human figures. As an important sensor of autonomous driving system, mmWave radar has extra help to detect living pedestrians. In this paper, we extract the radar cross section (RCS) of the object from the low-cost mmWave radar signal as a distinguishing feature between living pedestrian and portrait billboard. Based on this observation, we propose a feature fusion network of mmWave radar and computer vision based on attention mechanism, and detect living pedestrians from fusion features. We implement the design with commodity mmWave radar IWR6843ISK-ODS and RGB camera Logitech Pro C920. The evaluation results show that our method effectively detects living pedestrians with an mAP of 97.7% and outperforms existing studies.

Triangular frequency-modulated continuous-wave (FMCW) laser radars (ladars) are extremely sensitive to vibration errors. An FMCW ladar 3D imaging system may suffer from severe vibrations and can use only one-period echoes for the ranging of each observation spot; consequently, it can provide only few measurement results. These vibrations may cause large errors because conventional vibration compensation methods are ineffective when applied to fast disturbances with limited measurement results. To solve this problem, we analyze the influence of vibrations on FMCW ladar ranging and propose a vibration compensation method based on an instantaneous ranging model for one-period triangular FMCW ladar signals. We first use a synchrosqueezing wavelet transform to extract time-frequency curves of the up- and down-dechirp signals and then build an instantaneous ranging model that can characterize local vibration errors. Based on the instantaneous ranges, we remove the disturbance vibration errors by taking the mean values of the instantaneous ranges and obtain the target range by using the triangular relations of the up and down observations. Experiments based on synthetic and real data verify the effectiveness of the proposed method and its superiority over the three-point method and Doppler shift method in compensating for vibrations with different frequencies and noise levels.

With the rapid development of deep learning, significant progress has been made in remote sensing image target detection. However, methods based on deep learning are confronted with several challenges: (1) the inherent limitations of activation functions and downsampling operations in convolutional networks lead to frequency deviations and loss of local detail information, affecting fine-grained object recognition; (2) class imbalance and long-tail distributions further degrade the performance of minority categories; (3) large intra-class variations and small inter-class differences make it difficult for traditional deep learning methods to effectively extract fine-grained discriminative features. To address these issues, we propose a novel remote sensing aircraft recognition method. First, to mitigate the loss of local detail information, we introduce a learnable Gabor filter-based texture feature extractor, which enhances the discriminative feature representation of aircraft categories by capturing detailed texture information. Second, to tackle the long-tail distribution problem, we design a dynamic feature hallucination module that synthesizes diverse hallucinated samples, thereby improving the feature diversity of tail categories. Finally, to handle the challenge of large intra-class variations and small inter-class differences, we propose a contrastive learning module to enhance the spatial discriminative features of the targets. Extensive experiments on the large-scale fine-grained datasets FAIR1M and MAR20 demonstrate the effectiveness of our method, achieving detection accuracies of 53.56% and 89.72%, respectively, and surpassing state-of-the-art performance. The experimental results validate that our approach effectively addresses the key challenges in remote sensing aircraft recognition.

Dynamic vibration absorbers array (DVAA) is a newly developed and promising technique for vibration attenuation of integrally bladed disk (blisk) by mounting underneath the disk. In this paper, the vibration attenuation characteristics and energy dissipation mechanism of DVAA for tuned and mistuned blisk are parametrically studied, where the viscous and frictional damping are both considered. The lumped parameter model of a blisk is employed for the convenience of parametrical study. Analytical power flow formulas within the blisk–DVAA system are derived to characterize the dynamic interaction between blisk and DVAA. Four typical modes of a blisk with different nodal diameters and deformation characteristics are selected to evaluate the performance of DVAA. Then, the effects of the mass ratio, the frequency ratio and the damping ratio of DVAA on the vibration of tuned and mistuned blisk are addressed. Numerical results show that a light DVAA can significantly mitigate the resonant amplitudes of the tuned and mistuned blisk. Such damper is effective for the modes with different modal characteristics and can provide robust vibration attenuation performance against random mistuning.