The detection of manoeuvring target for space-based radar is a challenging task because of its weak energy and manoeuvring motion. Prolonging the integration time is an effective method to increase signal energy, which can improve the target detection performance. However, with the increase of integration time, the radial velocity, radial acceleration and radial acceleration rate of manoeuvring target will induce linear range walk, quadratic-range curvature, cubic-range curvature and Doppler frequency migration of echo signal, which may degrade the performance of target detection. To solve this problem, a novel method is proposed that implements a parameter separation transform to isolate the acceleration. Then, the velocity and the acceleration rate can be obtained via one-dimensional search after the compensation of linear and cubic-range migration based on Hough transform and third-order Keystone transform. The kernel steps are as follows: (i) the acceleration is isolated by multiplying the range-compressed data in range frequency domain by its timereversed data according to the symmetrical property of azimuth slow time; (ii) the cubic-range curvature is corrected by a third-order Keystone transform. The simulation results demonstrate the effectiveness of the proposed method in estimating radial velocity, radial acceleration and radial acceleration rate of a manoeuvring target, thereby the signal energy can be finely accumulated.

Under the condition of wideband interference (WBI) with the characteristics of good time-frequency concentration but high nonstationarity, the limited time width of the window function in short-time Fourier transform (STFT) causes limited instantaneous frequency resolution and leads to great performance degradation of the conventional WBI suppression algorithm based on time-frequency filtering (TFF) method. A novel WBI suppression method using iterative adaptive approach (IAA) and orthogonal subspace projection (OSP) method is proposed for synthetic aperture radar (SAR). Dispensing with parametric search and model order estimation, the proposed method improves the instantaneous frequency resolution in STFT by means of the IAA method and filters the WBI based on the OSP method, meanwhile, obtains time-frequency distribution (TFD) with 2-D high resolution and no cross-terms. Both the simulation and experimental results are provided to illustrate the performance of the proposed method.Index Terms-Iterative adaptive approach (IAA), orthogonal subspace projection (OSP), synthetic aperture radar (SAR), wideband interference (WBI) suppression.

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Although instance segmentation has made considerable advancement over recent years, it's still a challenge to design high accuracy algorithms with real-time performance. In this paper, we propose a real-time instance segmentation framework termed OrienMask. Upon the one-stage object detector YOLOv3, a mask head is added to predict some discriminative orientation maps, which are explicitly defined as spatial offset vectors for both foreground and background pixels. Thanks to the discrimination ability of orientation maps, masks can be recovered without the need for extra foreground segmentation. All instances that match with the same anchor size share a common orientation map. This special sharing strategy reduces the amortized memory utilization for mask predictions but without loss of mask granularity. Given the surviving box predictions after NMS, instance masks can be concurrently constructed from the corresponding orientation maps with low complexity. Owing to the concise design for mask representation and its effective integration with the anchorbased object detector, our method is qualified under realtime conditions while maintaining competitive accuracy. Experiments on COCO benchmark show that OrienMask achieves 34.8 mask AP at the speed of 42.7 fps evaluated with a single RTX 2080 Ti. The code is available at https://github.com/duwt/OrienMask.

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