In this paper, we address the problem of spectrum estimation of multiple frequency-hopping (FH) signals in the presence of random missing observations. The signals are analyzed within the bilinear time-frequency (TF) representation framework, where a TF kernel is designed by exploiting the inherent FH signal structures. The designed kernel permits effective suppression of cross-terms and artifacts due to missing observations while preserving the FH signal autoterms. The kerneled results are represented in the instantaneous autocorrelation function domain, which are then processed using a redesigned structure-aware Bayesian compressive sensing algorithm to accurately estimate the FH signal TF spectrum. The proposed method achieves high-resolution FH signal spectrum estimation even when a large portion of data observations is missing. Simulation results verify the effectiveness of the proposed method and its superiority over existing techniques.

The effective monitoring of low-altitude slow small (LSS) targets represented by unmanned aerial vehicle (UAV) is a great challenge in the field of security in recent years. Most of the existing infrared (IR) small target algorithms focus on high-altitude target detection. However, the low-altitude background is complex and changeable, and high-intensity suspected targets exist widely. Existing methods usually cause high false alarm or failure detection for LSS targets. In this letter, we propose a novel spatiotemporal saliency method for LSS IR targets in image sequences. First, spatial variance saliency mapping and temporal gray saliency mapping are calculated in spatial domain and temporal domain, respectively. Then, the fusion saliency map is obtained by fusing the spatial saliency map and temporal saliency map. Finally, the target is extracted by a simple adaptive threshold segmentation. The proposed method is verified in five low-altitude IR image sequences. Experimental results demonstrate that the proposed method can achieve better detection performance than the existing state-of-the-art methods for LSS targets.

The high‐speed movement of a target may cause range migration and Doppler frequency migration of the radar echo, which has a serious impact on the detection performance of the radar. To resolve the problem of detecting a high‐speed target in linear frequency modulation radar, this study analyses the effect on the integration gain caused by range migration and Doppler frequency migration, and proposes a corresponding compensation method according to the different input signal‐to‐noise ratios (SNRs) of the echo signal. To compensate for range migration in high SNRs, two‐dimensional median filtering and constant false alarm rate technology are combined to estimate the speed. For low SNRs, based on coarse valuations, the authors use the discrete Fourier transformation (DFT) to realise the fractional delay cell to improve speed accuracy. Furthermore, to compensate for Doppler frequency migration, an instantaneous cross‐correlation method is proposed for high SNRs, which is combined with the fractional Fourier transform method to estimate the acceleration for low SNRs. The input SNR threshold for the different algorithms is then analysed using simulation data, and the theoretical reference value is shown. Finally, the study verifies the effectiveness of the proposed methods through simulation and measured data.

In this study, the problem of efficient implementation of a coherent integration processor in passive bistatic radars (PBRs) in the presence of range migration is addressed. The authors present a coherent integration architecture for PBR, which consists of a frequency‐domain pulse compression module to reduce the overall runtime for the computation of the cross‐ambiguity function, and an efficient decimated keystone transform module based on the chirp z ‐transform to compensate the range migration. The proposed architecture is then implemented in a hybrid central processing unit plus graphic processing unit scheme. Real measurement data are used to verify the superior integration performance and reduced computational complexity achieved by the proposed scheme.

Infrared (IR) small target detection in the complex background is an important but challenging research hotspot in the field of target detection. The existing methods usually cause high false alarms in the complex background and fail to make full use of the complete information of the image. In this article, a novel IR small target detection model that combines facet derivative-based multidirectional edge awareness with spatial–temporal tensor (FDMDEA-STT) is presented. First, we construct an STT model (STTM) to transform the target detection problem into a low-rank and sparse tensor optimization problem based on the prior information of the target and background in the spatial–temporal domain. Then, based on the facet derivative, we define a multidirectional edge awareness mapping and fuse it into the STTM as sparse prior information. Finally, an effective algorithm based on the alternating direction method of multipliers (ADMM) is designed to solve the above model. The effectiveness of the proposed method is verified on eight real IR image sequences. Experimental results demonstrate that the proposed method has better detection performance than the existing state-of-the-art methods.

Fast and stable detection of dim and small infrared (IR) targets in complex backgrounds has important practical significance for IR search and tracking system. The existing small IR target detection methods usually fail or cause high probability of false alarm in the highly heterogeneous and complex backgrounds. Continuous motion of a target relative to the background is important information regarding detection. In this paper, A low rank and sparse decomposition method based on greedy bilateral factorization is proposed for IR dim and small target detection. Firstly, by analyzing the complex structure information of IR image sequences, the target is regarded as an independent sparse motion structure and an efficient optimization algorithm is designed. Secondly, the greedy bilateral factorization strategy is adopted to approximate the low-rank part of the algorithm, which significantly accelerates the efficiency of algorithm. Extensive experiments demonstrate that the proposed method has better detection performance than the existing methods. The proposed method can still detect targets quickly and stably especially in complex scenes with weak signal-to-noise ratio.