In order to effectively enhance the low detection rates of dim and small targets caused by dynamic backgrounds, this paper proposes a detection algorithm for dim and small targets in sequence images based on spatiotemporal motion characteristics. With regard to the spatial domain, this paper proposes an improved anisotropic background filtering algorithm that makes full use of the gradient differences between the target and the background pixels in the eight directions of the spatial domain and selects the mean value of the three directions with the lowest diffusion function in the eight directions as the differential filter to obtain a differential image. Then, the paper proposes a directional energy correlation enhancement algorithm in the time domain. Finally, based on the above preprocessing operations, we construct a dim and small targets detection algorithm in sequence images with local motion characteristics, which achieves target detection by determining the number of occurrences of the target, the number of displacements, and the total cumulative area in these sequential images. Experiments show that the proposed detection algorithm in this paper can effectively improve the detection of dim and small targets in dynamic scenes.

In the dim-small target detection field, background suppression is a key technique for stably extracting the target. In order to effectively suppress the background to enhance the target, this paper presents a novel background modeling algorithm, which constructs base functions for each pixel based on the local region background and models the background of each pixel, named single pixel background modeling (SPB). In SPB, the low-rank blocks of the local backgrounds are first obtained to construct the background base functions of the center pixel. Then, the background of the center pixel is optimally estimated by the background bases. Experiments demonstrate that in the case of extremely low signal-to-noise ratio (SNR < 1.5 dB) and complex motion state of targets, SPB can stably and effectively separate the target from the strongly undulant sky background. The difference image obtained via SPB background modeling has the characters: the non-target residual could be white noise, and the target is significantly enhanced. Compared with the other typical five algorithms, SPB remarkably outperforms other algorithms to detect the target of a low signal-to-noise ratio.

A novel coplanar waveguide-fed triple-band antenna with omni directional radiation patters in the azimuth plane is proposed. The proposed antenna consists of a rectangular patch monopole with a Π-shaped slot. It can provide three separate impedance bandwidths of 27%, range from 2.22 to 2.89GHz; bandwidth of 15%, range from 3.18 to 3.71GHz; and bandwidths of 22%, range from 5.09 to 6.33GHz, respectively, covering all the 2.4/5.2/5.8GHz WLAN bands and 2.5/3.5/5.5GHz WiMAX bands.

In order to effectively detect dim-small targets in complex scenes, background suppression is applied to highlight the targets. This paper presents a statistical clustering partitioning low-rank background modeling algorithm (SCPLBMA), which clusters the image into several patches based on image statistics. The image matrix of each patch is decomposed into low-rank matrix and sparse matrix in the SCPLBMA. The background of the original video frames is reconstructed from the low-rank matrices, and the targets can be obtained by subtracting the background. Experiments on different scenes show that the SCPLBMA can effectively suppress the background and textures and equalize the residual noise with gray levels significantly lower than that of the targets. Thus, the difference images obtain good stationary characteristics, and the contrast between the targets and the residual backgrounds is significantly improved. Compared with six other algorithms, the SCPLBMA significantly improved the target detection rates of single-frame threshold segmentation.

According to the requirements of high resolution real time imaging of curved trajectory Bistatic Synthetic Aperture Radar (BiSAR), an improved RD algorithm is proposed in this paper. Firstly, the slant range of curved BiSAR is established by Chebyshev polynomials. Secondly, linear range cell migration (LRCM) and Doppler linear phase are compensated, and the high order approximate two-dimensional spectrum of echo signal is obtained by the method of series inversion (MSR) and Chebyshev decomposition. Finally, the focused image is obtained by matched filtering and phase compensation. By using Chebyshev polynomials to approximate slant range and spectral phase, the focus quality of BiSAR data is improved. Experimental results show that the algorithm can effectively compensate the motion error caused by three-dimensional acceleration and improve the imaging quality of the long distance edge point targets.

In object detection tasks, scale variation is one of the major challenges affecting detection accuracy. Existing methods typically enhance the scale generalization of models through strategies through image pyramids, feature pyramid networks, or multi-scale training. However, these approaches either increase inference computational costs or have not previously established explicit associations between different scale inputs during training. We propose Dual-Scale Parallel Training (DSPT) as a novel training strategy for object detection models. This method introduces high-and-low-resolution homologous image branches for associative learning during training. By fusing the losses from both branches, DSPT enables shared-parameter, same-iteration cross-scale joint optimization strategy, thereby enhancing the model’s multi-scale perception and generalization ability without altering the network structure or inference efficiency. Experimental results demonstrate that DSPT achieves stable performance improvements across several popular datasets (MS COCO, PASCAL VOC, VisDrone, etc.) and two model series (YOLO and RTDETR), with the mAP@50 metric improving by up to 4%. In scale-aware evaluation experiments, DSPT outperforms conventional and multi-scale training methods in detection accuracy and generalization, and provides positive gains across all object sizes. This strategy is simple, general, and scalable, providing a new approach to training object detection models with promising practical applications.