Formation flying synthetic aperture radar (FF-SAR) systems, as an important development direction of multichannel SAR, can achieve high-resolution wide-swath (HRWS) imaging. Coherently combining data from satellite receivers puts a strain on traditional real-time processing systems based on individual satellites. Characteristics such as the power of real-time on-orbit processing platform must be properly balanced with constrained memory and parallel computational resources. This paper proposes a distributed SAR real-time imaging method based on embedded Graphics Processing Units (GPUs). The parallel computing method of chirp scaling (CS) algorithm is designed based on parallel programming model of compute unified device architecture (CUDA), and the optimization methods of memory and performance are proposed for the hardware architecture of embedded GPUs. In particular, unified memory management method is used to avoid data copying and communication delays between the CPU and GPU. A hardware verification system for distributed SAR real-time imaging processing based on multiple embedded GPUs is constructed. The proposed algorithm takes 5.86 seconds to process single-precision floating-point complex imaging with a data size of 8192 × 8192 on a single Jetson Nano platform. The actual power consumption is less than 5 W, and the performance-to-power ratio is greater than 1.7%. The experimental results show that the real-time processing method based on embedded GPUs proposed in this paper has high performance and low power consumption.

Fall detection based on radar technology has recently attracted increasing attention. Human motions are often multidirectional and have complex trajectories. Numerous time-frequency representation methods have been used to extract micro-Doppler fall features. Peak-to-peak Doppler frequency measurements are limited due to the relatively weak energy in the high-Doppler-frequency area. Fall features are not obvious when using a single mmWave radar if human motions are nearly perpendicular to the radar. A fall feature enhancement and fusion method using the Stockwell transform with dual mmWave radars is proposed in this paper. Two mmWave radars synchronously collect human motions in different directions, and the micro-Doppler features of these human motions are obtained by using the Stockwell transform. To enhance the features obtained for recognition, the ridgelines extracted from the two radars are fused in the time-frequency domain. In addition, a model trained by a convolutional neural network (CNN) is used for fall recognition. 960 fall motions and 720 nonfall motions are collected from 60 volunteers. The experimental results show that the proposed method can produce a recognition accuracy of up to 94.14%, which is beneficial for the realization of an indoor fall detection system.

The UAV-borne video SAR (ViSAR) imaging system requires miniaturization, low power consumption, high frame rates, and high-resolution real-time imaging. In order to satisfy the requirements of real-time imaging processing for the UAV-borne ViSAR under limited memory and parallel computing resources, this paper proposes a method of embedded GPU-based real-time imaging processing for the UAV-borne ViSAR. Based on a parallel programming model of the compute unified device architecture (CUDA), this paper designed a parallel computing method for range-Doppler (RD) and map drift (MD) algorithms. By utilizing the advantages of the embedded GPU characterized with parallel computing, we improved the processing speed of real-time ViSAR imaging. This paper also adopted a unified memory management method, which greatly reduces data replication and communication latency between the CPU and the GPU. The data processing of 2048 × 2048 points took only 1.215 s on the Jetson AGX Orin platform to form a nine-consecutive-frame image with a resolution of 0.15 m, with each frame taking only 0.135 s, enabling real-time imaging at a high frame rate of 5 Hz. In actual testing, continuous mapping can be achieved without losing the scenes, intuitively obtaining the dynamic observation effects of the area. The processing results of the measured data have verified the reliability and effectiveness of the proposed scheme, satisfying the processing requirements for real-time ViSAR imaging.

The unmanned aerial vehicle (UAV)-borne video synthetic aperture radar (SAR) possesses the characteristic of having high-continuous-frame-rate imaging, which is conducive to the real-time monitoring of ground-moving targets. The real-time imaging-processing system for UAV-borne video SAR (ViSAR) requires miniaturization, low power consumption, high frame rate, and high-resolution imaging. In order to achieve high-frame-rate real-time imaging on limited payload-carrying platforms, this study proposes a miniaturization design of a high-integration UAV-borne ViSAR real-time imaging-processing component (MRIPC). The proposed design integrates functions such as broadband signal generation, high-speed real-time sampling, and real-time SAR imaging processing on a single-chip FPGA. The parallel access mechanism using multiple sets of high-speed data buffers increases the data access throughput and solves the problem of data access bandwidth. The range-Doppler (RD) algorithm and map-drift (MD) algorithm are optimized using parallel multiplexing, achieving a balance between computing speed and hardware resources. The test results have verified that our proposed component is effective for the real-time processing of 2048 × 2048 single-precision floating-point data points to realize a 5 Hz imaging frame rate and 0.15 m imaging resolution, satisfying the requirements of real-time ViSAR-imaging processing.

To solve the problem of performance degradation owing to imprecise knowledge of the array steering vector and inaccurate estimation of the sample covariance matrix, a novel algorithm based on beam‐space steering vector estimation for robust adaptive beamforming is presented. By using the complementary set of the spatial sector in which the actual steering vector lies, a beam‐space transformation matrix can be constructed to remove the signal of interest from the sampling covariance matrix. The beam‐space steering vector is estimated by solving a non‐convex quadratically constrained quadratic optimisation problem. The effectiveness and robustness of the proposed algorithm is verified by the simulations.

The real-time performance and precision requirements posed by the ranging method for proximity fuze determines the effect and hit accuracy of the explosive device on which it is mounted. This further demands not only high spectrum resolution near the burst point for the fuze, but also the ability to monitor the spectrum of the entire observation area. This paper proposes a ranging method for proximity fuze based on warped fast Fourier transform (WFFT). By selecting appropriate all-pass warping factors, our proposed method can improve the spectrum precision within a specific spectrum region observed without increasing signal processing points. By fitting non-uniform filter banks, the diffused energy of target object in high-frequency region is accumulated, which can be regarded as improving the signal-to-noise ratio (SNR) in high-frequency region, high-frequency information can be accurately captured, thereby facilitating real-time ranging of proximity fuze with higher precision. The experimental results have verified the feasibility and effectiveness of our proposed method in practical proximity fuze ranging.