When several foreign fighters with the same type enter detection range, the electronic warfare (EW) receivers will intercept many the same type radar emitter signals. If the intercepted pulse is processed by the traditional sorting methods, the number of emitters cannot be identified. The main reason is that the same type of radar has similar parameters. It will cause a devastating influence on subsequent strategic decisions. A novel sorting method based on the trajectory features is proposed to solve the aforementioned problems. First, the trajectory features of the intercepted pulse signal are extracted. Then, the segmentation method is utilized to preprocess the signals, which enhances the computing efficiency and improves the sorting accuracy. Meanwhile, a prediction framework based on long short-term memory (LSTM) recurrent neural network is established to forecast pulses. Finally, the radar stagger pulses are sorted by forecast pulses. The simulation results show that the proposed method can recognize the number of emitters and achieve high sorting accuracy. It provides a new idea for the radar signals sorting of the same type.

Aiming at solving the problem that the unambiguous Doppler range of slow-time MIMO is narrower than that of conventional MIMO signals, a new signal processing method based on tensor decomposition is proposed. In the proposed method, a novel tensor model is designed to describe the echo signals of slow-time MIMO radar and retains more useful information which is lost in conventional methods based on Doppler spectrum separation. The proposed algorithm applies PARAFAC to separate target parameters into three matrices, then minimum mean squared error estimation and least squares method are used to estimate the target parameters respectively. Simulation experiments have demonstrated that the proposed method can exploit unambiguous Doppler range, and simultaneously estimate target’s direction of departure, direction of arrival and Doppler frequency without further parameter pairing.

The dynamically changing ionospheric conditions are the biggest impediment to realizing ideal performance in long-range for over-the-horizon radar (OTHR). Most related studies focus on developing the signal processing techniques and antenna arrays design. However, ionosphere clutter derives from the interaction between radar signal and the ionosphere, it exhibits different behaviors in three dimensions. OTHR should have the ability to sense when and where (in range) a certain type of ionosphere clutter appears before proposing the appropriate suppression scheme. Therefore, this report aims at identifying the various types of ionosphere clutter in Earth’s middle-latitude regions and characterizing them in terms of temporal, Doppler, azimuth, range and overall amplitude distribution domain, thereby establishing a multi-dimensional information profile. An experimental OTHR has been developed to monitor the surrounding oceans in middle-latitude area, we collected a sufficient real radar return data for two years running and then selected data specifically designed to observe ionosphere clutter. Then, we identify the various types of ionosphere clutter and catalogue their multi-dimensional characteristics. A knowledge of where a certain type of ionosphere clutter appears and pertinent information are essential for developing the appropriate clutter mitigation scheme in real time.

High-frequency surface wave radar (HFSWR) as a maritime surveillance facility faces the tough challenges. One of them is that various unwanted echo components constantly destroy the detection performance of HFSWR, especially the dynamically changing ionosphere clutter. This article proposes an adaptive ionosphere clutter suppression and target detection algorithm (AICSTD) based on improved Higher-Order Singular Value Decomposition (HOSVD) and deep learning network. First, we analyze the multiple-dimension characteristics of different types of ionosphere clutter echo data. Then, refer to the multi-channel HFSWR echo data structure, we apply simplified HOSVD and orthogonal subspace projection to eliminate the unwanted echo component, meanwhile, preserve the target echo. The key parameter of simplified HOSVD is determined by the mean structural similarity (MSSIM) index, which contributes to reduce the computation complexity of proposed method. Finally, we improved the deep learning network to detect the targets in various behaviors after the clutter suppression. In order to address the issue of limited clutter data, transfer learning based on historical radar echo data is applied. A thorough experimental analysis on various real clutter data shows that the proposed method performs competitively. Our method shows a superior performance over many state-of-the-art clutter suppression methods, with a covered target detection accuracy of 92.4% under strong spread-clutter background.

The problem that this paper is concerned with is High Frequency Surface Wave Radar (HFSWR) detection of desired targets against a complex interference background consisting of sea clutter, ionosphere clutter, Radio Frequency Interference (RFI) and atmospheric noise. Eliminating unwanted echoes and exploring obscured targets contribute to achieving ideal surveillance of sea surface targets. In this paper, a Self-regulating Multi-clutter Suppression Framework (SMSF) has been proposed for small aperture HFSWR. SMSF can remove many types of clutter or RFI; meanwhile, it mines the targets merged into clutter and tracks the travelling path of the ship. In SMSF, a novel Dynamic Threshold Mapping Recognition (DTMR) method is first proposed to reduce the atmospheric noise and recognize each type of unwanted echo; these recognized echoes are fed into the proposed Adaptive Prophase-current Dictionary Learning (APDL) algorithm. To make a comprehensive evaluation, we also designed three novel assessment parameters: Obscured Targets Detection Rate (OTDR), Clutter Purification Rate (CPR) and Erroneous Suppression Rate (ESR). The experiment data collected from a small aperture HFSWR system confirm that SMSF has precise suppression performance over most of the classical algorithms and concurrently reveals the moving targets, and OTDR of SMSF is usually higher than compared methods.