Radar Human Activity Recognition with an Attention-Based Deep Learning Network.

Radar-based human activity recognition (HAR) provides a non-contact method for many scenarios, such as human-computer interaction, smart security, and advanced surveillance with privacy protection. Feeding radar-preprocessed micro-Doppler signals into a deep learning (DL) network is a promising approach for HAR. Conventional DL algorithms can achieve high performance in terms of accuracy, but the complex network structure causes difficulty for their real-time embedded application. In this study, an efficient network with an attention mechanism is proposed. This network decouples the Doppler and temporal features of radar preprocessed signals according to the feature representation of human activity in the time-frequency domain. The Doppler feature representation is obtained in sequence using the one-dimensional convolutional neural network (1D CNN) following the sliding window. Then, HAR is realized by inputting the Doppler features into the attention-mechanism-based long short-term memory (LSTM) as a time sequence. Moreover, the activity features are effectively enhanced using the averaged cancellation method, which improves the clutter suppression effect under the micro-motion conditions. Compared with the traditional moving target indicator (MTI), the recognition accuracy is improved by about 3.7%. Experiments based on two human activity datasets confirm the superiority of our method compared to traditional methods in terms of expressiveness and computational efficiency. Specifically, our method achieves an accuracy close to 96.9% on both datasets and has a more lightweight network structure compared to algorithms with similar recognition accuracy. The method proposed in this article has great potential for real-time embedded applications of HAR.

Costas DFC-Based Random Stepped Wideband Waveform for Interference Countermeasure in SAR Imagery.

Interference in SAR imagery will induce false targets or form a mask in specific areas to prevent accurate scene assessment. Traditional anti-jamming methods based on waveform agility require a trade-off between anti-jamming performance and imaging quality in waveform design. In this paper, we proposed a SAR ECCM scheme including a Costas DFC-based random stepped wideband waveform and corresponding imaging processing method. The waveform exhibits high flexibility against forwarding interference due to the decomposition of a wideband signal into multiple pulses with different Costas discrete frequency encoding, carrier frequency and phase modulation. Furthermore, the combination of FCDC and the imaging processing successfully overcomes the Doppler sensitivity of the proposed waveform. Extensive simulations confirmed the superiority of this waveform and processing method under different interference strategies.

Channel Phase Calibration for High-Resolution and Wide-Swath SAR Imaging with Doppler Spectrum Sharpness Optimization.

Channel phase calibration is a crucial issue in high resolution and wide swath (HRWS) imagery with azimuth multi-channel synthetic aperture radar (SAR) systems. Precise phase calibration is definitely required in reconstructing the full Doppler spectrum for precise HRWS imagery without high-level ambiguities. In this paper, we propose a novel calibration for HRWS SAR imagery by optimizing the reconstructed unambiguous Doppler spectrum. The sharpness of the reconstructed Doppler spectrum is applied as the metric to measure the unambiguity quality, which is maximized to retrieve the element phase error caused by channel imbalance. Real data experiments demonstrate the performance of the proposed calibration for ambiguity suppression in HRWS SAR imagery.

Fast Near-Field Frequency-Diverse Computational Imaging Based on End-to-End Deep-Learning Network.

The ability to sculpt complex reference waves and probe diverse radiation field patterns have facilitated the rise of metasurface antennas, while there is still a compromise between the required wide operation band and the non-overlapping characteristic of radiation field patterns. Specifically, the current computational image formation process with a classic matched filter and other sparsity-driven algorithms would inevitably face the challenge of a relatively confined scene information sampling ratio and high computational complexity. In this paper, we marry the concepts of a deep convolutional neural network with computational imaging literature. Compared with the current matched filter and compressed sensing reconstruction technique, our proposal could handle a relatively high correlation of measurement modes and low scene sampling ratio. With the delicately trained reconstruction network, point-size objects and more complicated targets can both be quickly and accurately reconstructed. In addition, the unavoidable heavy computation burden and essential large operation frequency band can be effectively mitigated. The simulated experiments with measured radiation field data verify the effectiveness of the proposed method.

Alkaloids from Tabernaemontana divaricata combined with fluconazole to overcome fluconazole resistance in Candida albicans.

Nineteen indole alkaloids including eleven new ones, taberdines A-K (1-11), were isolated from Tabernaemontana divaricata. Their structures were assigned by MS, NMR, single crystal X-ray diffractions, and ECD analyses. Alkaloid 1 is an aspidosperma-type monoterpenoid indole alkaloid and possesses a rearranged pyrrolidine moiety due to C-3 degradation, and 4 has a rare 1,3-oxazolidine moiety within iboga-type alkaloids. Alkaloids 2, 4, 6, and 11-19 combined with 5 µg/mL fluconazole exhibited significant activity to reverse fluconazole resistance in Candida albicans strains while no one used alone showed any activities against the resistant strain.

Low Elevation Angle Estimation with Range Super-Resolution in Wideband Radar.

Height detection of a low elevation angle target is crucial in radar applications. Due to the presence of the multiple path reflections, elevation angle estimation is difficult with conventional narrowband radar waveforms. The reflection ground area parameters are especially hard to obtain for modeling. In this paper, we proposed a wideband, low elevation angle estimator based on range super-resolution, achieving a high robustness to variations in reflection coefficients. A relaxation (RELAX) algorithm was applied as the range super-resolution algorithm to separate the direct target echo and the reflected signal thanks to the relatively abundant frequency diversity. The grazing angle was obtained by synthesizing the steering vector of the direct signal and the range structure relationship between the two signal components. Theoretical analysis and simulation results confirmed the improved behavior of the proposed robust estimator in contrast to other conventional algorithms.

Bayesian Compress Sensing Based Countermeasure Scheme Against the Interrupted Sampling Repeater Jamming.

The interrupted sampling repeater jamming (ISRJ) is considered an efficient deception method of jamming for coherent radar detection. However, current countermeasure methods against ISRJ interference may fail in detecting weak echoes, particularly when the transmitting power of the jammer is relatively high. In this paper, we propose a novel countermeasure scheme against ISRJ based on Bayesian compress sensing (BCS), where stable target signal can be reconstructed over a relatively large range of signal-to-noise ratio (SNR) for both single target and multi-target scenarios. By deriving the ISRJ jamming strategy, only the unjammed discontinuous time segments are extracted to build a sparse target model for the reconstruction algorithm. An efficient alternate iteration is applied to optimize and solve the maximum a posteriori estimate (MAP) of the sparse targets model. Simulation results demonstrate the robustness of the proposed scheme with low SNR or large jammer ratio. Moreover, when compared with traditional FFT or greedy sparsity adaptive matching pursuit algorithm (SAMP), the proposed algorithm significantly improves on the aspects of both the grating lobe level and target detection/false detection probability.

A lightweight hybrid vision transformer network for radar-based human activity recognition.

Radar-based human activity recognition (HAR) offers a non-contact technique with privacy protection and lighting robustness for many advanced applications. Complex deep neural networks demonstrate significant performance advantages when classifying the radar micro-Doppler signals that have unique correspondences with human behavior. However, in embedded applications, the demand for lightweight and low latency poses challenges to the radar-based HAR network construction. In this paper, an efficient network based on a lightweight hybrid Vision Transformer (LH-ViT) is proposed to address the HAR accuracy and network lightweight simultaneously. This network combines the efficient convolution operations with the strength of the self-attention mechanism in ViT. Feature Pyramid architecture is applied for the multi-scale feature extraction for the micro-Doppler map. Feature enhancement is executed by the stacked Radar-ViT subsequently, in which the fold and unfold operations are added to lower the computational load of the attention mechanism. The convolution operator in the LH-ViT is replaced by the RES-SE block, an efficient structure that combines the residual learning framework with the Squeeze-and-Excitation network. Experiments based on two human activity datasets indicate our method's advantages in terms of expressiveness and computing efficiency over traditional methods.