Passive jamming is believed to have very good potential in countermeasure community. In this paper, a passive angular blinking jamming method based on electronically controlled corner reflectors is proposed. The amplitude of the incident wave can be modulated by switching the corner reflector between the penetration state and the reflection state, and the ensemble of multiple corner reflectors with towing rope can result in complex angle decoying effects. Dependency of the decoying effect on corner reflectors ’ radar cross section and positions are analyzed and simulated. Results show that the angle measured by a monopulse radar can be significantly interfered by this method while the automatic tracking is employed.
In this paper, we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing (OFDM) signal. A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden. Firstly, the modulation symbol domain (MSD) method is used to roughly estimate the delay and Doppler of targets. Then, to obtain high-precision Doppler estimation, the atomic norm (AN) based on the multiple measurement vectors (MMV) model (MMV-AN) is used to manifest the signal sparsity in the continuous Doppler domain. At the same time, a reference signal compensation (RSC) method is presented to obtain high-precision delay estimation. Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms. In addition, the proposed method also possesses computational advantages compared with the joint parameter estimation.
Mainlobe jamming (MLJ) brings a big challenge for radar target detection, tracking, and identification. The suppression of MLJ is a hard task and an open problem in the electronic counter-counter measures (ECCM) field. Target parameters and target direction estimation is difficult in radar MLJ. A target parameter estimation method via atom-reconstruction in radar MLJ is proposed in this paper. The proposed method can suppress the MLJ and simultaneously provide high estimation accuracy of target range and angle. Precisely, the eigen-projection matrix processing (EMP) algorithm is adopted to suppress the MLJ, and the target range is estimated effectively through the beamforming and pulse compression. Then the target angle can be effectively estimated by the atom-reconstruction method. Without any prior knowledge, the MLJ can be canceled, and the angle estimation accuracy is well preserved. Furthermore, the proposed method does not have strict requirement for radar array construction, and it can be applied for linear array and planar array. Moreover, the proposed method can effectively estimate the target azimuth and elevation simultaneously when the target azimuth (or elevation) equals to the jamming azimuth (or elevation), because the MLJ is suppressed in spatial plane dimension.
Link16 data link is the communication standard of the joint tactical information distribution system (JTIDS) used by the U.S. military and North Atlantic Treaty Organization, which is applied as the opportunistic illuminator for passive radar in this paper. The time-domain expression of the Link16 signal is established, and its ambiguity function expression is derived. The time-delay dimension and Doppler dimension side peaks of which lead to the appearance of the false target during target detection. To solve the problem, the time-delay dimension and Doppler dimension side peaks suppression methods are proposed. For the problem that the conventional mismatched filter (MMF) cannot suppress the time-delay dimension side peaks, a neighborhood MMF (NMMF) is proposed. Experimental results demonstrate the effectiveness of the proposed methods.
In the complex countermeasure environment, the pulse description words (PDWs) of the same type of multi-function radar emitters are similar in multiple dimensions. Therefore, it is difficult for conventional methods to deinterleave such emitters. In order to solve this problem, a pulse deinterleaving method based on implicit features is proposed in this paper. The proposed method introduces long short-term memory (LSTM) neural networks and statistical analysis to mine new features from similar PDW features, that is, the variation law (implicit features) of pulse sequences of different radiation sources over time. The multi-function radar emitter is deinterleaved based on the pulse sequence variation law. Statistical results show that the proposed method not only achieves satisfactory performance, but also has good robustness.
The optimal selection of radar clutter model is the premise of target detection, tracking, recognition, and cognitive waveform design in clutter background. Clutter characterization models are usually derived by mathematical simplification or empirical data fitting. However, the lack of standard model labels is a challenge in the optimal selection process. To solve this problem, a general three-level evaluation system for the model selection performance is proposed, including model selection accuracy index based on simulation data, fit goodness indexs based on the optimally selected model, and evaluation index based on the supporting performance to its third-party. The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways, and can be popularized and applied to the evaluation of other similar characterization model selection.
This paper considers the non-line-of-sight (NLOS) vehicle localization problem by using millimeter-wave (MMW) automotive radar. Several preliminary attempts for NLOS vehicle detection are carried out and achieve good results. Firstly, an electromagnetic (EM) wave NLOS multipath propagation model for vehicle scene is established. Subsequently, with the help of available multipath echoes, a complete NLOS vehicle localization algorithm is proposed. Finally, simulation and experimental results validate the effectiveness of the established EM wave propagation model and the proposed NLOS vehicle localization algorithm.
Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recognition. To solve this problem, an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed. First, a small amount of labeled data are randomly sampled by using the bootstrap method, loss functions for three common deep learning networks are improved, the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification. Subsequently, the dataset obtained after sampling is adopted to train three improved networks so as to build the initial model. In addition, the unlabeled data are preliminarily screened through dynamic time warping (DTW) and then input into the initial model trained previously for judgment. If the judgment results of two or more networks are consistent, the unlabeled data are labeled and put into the labeled data set. Lastly, the three network models are input into the labeled dataset for training, and the final model is built. As revealed by the simulation results, the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.
The conventional two dimensional (2D) inverse synthetic aperture radar (ISAR) imaging fails to provide the targets’ three dimensional (3D) information. In this paper, a 3D ISAR imaging method for the space target is proposed based on mutli-orbit observation data and an improved orthogonal matching pursuit (OMP) algorithm. Firstly, the 3D scattered field data is converted into a set of 2D matrix by stacking slices of the 3D data along the elevation direction dimension. Then, an improved OMP algorithm is applied to recover the space target’s amplitude information via the 2D matrix data. Finally, scattering centers can be reconstructed with specific three dimensional locations. Numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed 3D imaging method.
Ultrahigh resolution synthetic aperture radar (SAR) imaging for ship targets is significant in SAR imaging, but it suffers from high frequency vibration of the platform, which will induce defocus into SAR imaging results. In this paper, a novel compensation method based on the sinusoidal frequency modulation Fourier-Bessel transform (SFMFBT) is proposed, it can estimate the vibration errors, and the phase shift ambiguity can be avoided via extracting the time frequency ridge consequently. By constructing the corresponding compensation function and combined with the inverse SAR (ISAR) technique, well-focused imaging results can be obtained. The simulation imaging results of ship targets demonstrate the validity of the proposed approach.
Pulse repetition interval (PRI) modulation recognition and pulse sequence search are significant for effective electronic support measures. In modern electromagnetic environments, different types of inter-pulse slide radars are highly confusing. There are few available training samples in practical situations, which leads to a low recognition accuracy and poor search effect of the pulse sequence. In this paper, an approach based on bi-directional long short-term memory (BiLSTM) networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed. The simulation results demonstrate that the proposed algorithm can recognize unilinear, bilinear, sawtooth, and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.
To address the problem that dynamic wind turbine clutter (WTC) significantly degrades the performance of weather radar, a WTC mitigation algorithm using morphological component analysis (MCA) with group sparsity is studied in this paper. The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo. After that, the MCA algorithm is applied and the window used in the short-time Fourier transform (STFT) is optimized to lessen the spectrum leakage of WTC. Finally, the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution, thus contributing to better estimation performance of weather signals. The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.