Due to the complex electromagnetic environment, the airborne Synthetic Aperture Radar (SAR) system is faced with the challenge of missing part of the echo data, leading to the degradation on SAR imaging performance. To solve this problems, a novel SAR sparse imaging algorithm based on low rank matrix completion is proposed in this paper. Based on the mechanism of joint aperture and bandwidth synthesis, a two-dimensional BP-based SAR sparse imaging model is constructed at first. And then, on the basis of the above model, the concept of low rank matrix completion is introduced in this paper, to reconstruct the missing elements of the SAR signals in the frequency domain, due to the sparse sampling. Specifically, the sparse signal reconstruction problem is transformed into a regularized least squares optimization problem. Which can be solved iteratively by using the imprecise augmented Lagrange multiplier (IALM) method alternately. Therefore, the issue of failure for compressed sensing based sparse imaging under low sampling rate can be resolved effectively. Numerical results demonstrate the effectiveness of the proposed method, and it also shows that the Integrated Side Lobe Ratio (ISLR) of the proposed algorithm is lower than that of the existed methods.
Falls pose a serious threat to the safety of the elderly. Accurately detecting falls and their direction is crucial for medical personnel to assess injury locations and promptly formulate treatment plans. Traditional methods primarily integrate distance and Doppler features of fall behavior, neglecting a significant amount of spatial features. Additionally, the determination of fall direction is easily affected by environmental noise and limb-induced Doppler interference. To this end, this paper proposes a parallel convolutional neural network fall direction detection method based on millimeter-wave radar multidimensional feature fusion, which introduces the fall angle as spatial information while effectively attenuating the interference of environmental noise and limb micro-Doppler frequency to improve the accuracy of fall direction detection. Specifically, this method utilizes techniques such as pulse compression, MTI, Range Fast Fourier Transform (RFFT), STFT, and Capon beamforming to obtain the range-time, Doppler-time, and angle-time features from the human motion state to the falling state. Then, the Doppler-time-map (DTM) and angle-time-map (ATM) after the accumulation of multiple frames are subjected to column threshold feature extraction and column normalization feature processing, respectively, to achieve the effect of scrambling frequency removal and feature enhancement. Finally, the processed feature spectrograms are input into the parallel convolutional neural network for independent extraction of each dimension's features and multi-dimensional fusion, enabling the detection of the fall direction. The experimental results show that the proposed method achieves an average recognition accuracy of 97.52% for different fall directions, compared to an improvement of 2.31% over the traditional feature fusion method.
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