Bidirectional false targets RGPO jamming

The range gate pull off (RGPO) and the velocity gate pull off (VGPO) are two kinds of common electronic countermeasure techniques used to defeat or degrade tracking radars. For countering the RGPO or VGPO, the extended Kalman filter (EKF) with a new modified probabilistic data association (PDA) is proposed in this paper. It takes full advantage of the fact that for the case of low measurement noise, the measurements for the RGPO/VGPO have virtually the same Doppler velocity/range as those of the true target. This method uses the difference between the Doppler velocity and tracking velocity of the Kalman filter to weight the probability of every measurement so that less weight is given to the false measurement. The simulations show that for the case of low measurement noise, the EKF method with the new modified PDA can counter the RGPO or VGPO, and works better than the EKF with the standard PDA.

In this paper, the authors introduce the generation principle of the range gate pull-off (RGPO) jamming and its effect on the tracing radar, analyze the shortcomings of the countermeasures for the commonly-used existing anti-RGPO jamming, and finally gain a method to indentify if the radar suffers from the range gate pull-off jamming by integrating the characteristics of the range gate pull-off jamming, and propose the method of testing the variation of the radar-echo signal frequency-shift amount to eliminate the jamming.

Electronic jamming is the key technology of electronic countermeasure, for which models and simulations are studied on noise jamming, deception jamming, and chaff jamming. In the noise jamming, the simulation analysis of radio frequency noise jamming is conducted and the theoretical models of other forms of noise jamming are built. The simulation analysis of range-gate pull off (RGPO) is carried out in the deception jamming. In addition, theoretical modeling and simulation analysis of chaff jamming are carried out with the Monte Carlo method. Simulation results show that the models are correct and can be used to simulate the statistical characteristics of chaff jamming. With the development and wide application of electronic technology, radar, communication, navigation, and IFF signals constitute a complex electromagnetic environment. It is a must to study electronic jamming technology in the complex electromagnetic environment, which will play an indispensable role in the construction of information team and victory in the countermeasure of information technology.

Range gate pull-off (RGPO) jamming is an electronic countermeasure widely used to fool radar tracking systems. Nevertheless, the research on its strategy optimization has only been tepid. Optimization model, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. The algebraic description for the objective function of the optimization of the RGPO jamming strategy is difficult to obtain, and the jamming results are not deterministic under the influence of the input noise. To address these issues, this paper models the generation of the RGPO jamming strategy as a stochastic simulation optimization (SSO) problem, and proposes an optimization algorithm of the RGPO jamming strategy with the help of SSO technologies. We propose the committee based active learning (CAL) assisted optimal computing budget allocation based particle swarm optimization (CALPSO-OCBA) to alleviate the competition between solution space search and candidate solution performance evaluation by embedding CAL into PSO-OCBA. To improve the feasibility of the proposed algorithm, a scoring scheme that does not rely on the internal knowledge of the radar tracking system (i.e., tracking model, tracking method and tracking parameters) is designed. In addition, we use four most widely used tracking problems as the benchmarks for testing the proposed optimization algorithm of the RGPO jamming strategy. Experimental results demonstrate that the proposed algorithm is highly competitive in solving the optimization problem of the RGPO jamming strategy.

Deception is an effective means of jamming against synthetic aperture radar (SAR). The performance of deceptive jamming is affected by the accuracy of parameter measurement and the applied antijamming methods of SAR. In this article, we analyze the accuracy of current deceptive jamming evaluation indicators, propose a new method for evaluating the performance of deceptive jamming based on the combination of typical dominant deceptive jamming evaluation indicators, and recessive indicators extracted by convolutional neural networks, and obtain a level of deceptive jamming using a softmax activation function in a fully connected network. Deceptive jamming images affected by the SAR motion parameter measurement error are taken as training and test sets. Finally, the moving and stationary target acquisition and recognition database is used as a deceptive jamming template in order to verify the effectiveness of the proposed method.

Chaff is a Passive Electronic Countermeasure technology that plays a pivotal role in war scenarios. It can be used as a passive jammer to shield a war platform effectively. It can mimic the platform’s radar cross section (RCS) signature and act as a deceptive pseudo target. This manuscript presents an analysis of the jamming efficacy of chaff cloud. For this, three feature extraction and four AI/ML classification methods were employed, assuming that the Moving Target Indicator (MTI) and Doppler capabilities of the tracking radar are off. The effect of three different chaff deployment locations on its jamming performance has been analysed to determine the best possible deployment location. In the measurement setup, the range profile of the cloud is measured in the presence of a target. The classification performances of the extracted feature vectors are evaluated using the Support Vector Machine (SVM), Unsupervised Distance Classification (UDC), Naïve Bayes (NB) and Decision Tree (DT). A maximum 58.33 % decrease in recognition rate was observed with the introduction of chaff cloud when UDC and SVM approaches are employed and chaff is deployed from 90°. Noise has been introduced to closely predict the actual, practical performance of chaff in an actual deployment environment. The recognition rates fall less than 8.33 % for SVM and NB when AWGN (Artificial White Gaussian Noise) is 1.2 times. Based on these results, the jamming efficacy and the optimized tactical strategy of the chaff cloud are proposed. Better Out of the four AI/ML approaches, UDC and DT exhibit the best jamming performance and SVM exhibits the best anti-jamming performance. Any discrepancy and chances of overfitting can be avoided using a larger dataset with more features.

The direction-of-arrival (DOA) estimation is predominantly influenced by the antenna's aperture size. However, space constraints on flight platforms often necessitate the use of antennas with smaller apertures and fewer array elements. This inevitably imposes limitations on the DOA estimation's resolution and degrees of freedom. To address these precision constraints, we introduce an accurate DOA estimation method based on spatial synthetic aperture model. This method adopts a two-stage strategy to ensure both efficiency and precision in DOA estimation. Initially, the orthogonal matching pursuit (OMP) reconstruction algorithm processes the original aperture data, providing a rough estimate of target angles that guides the aircraft's flight direction. Subsequently, the early estimations merge with the aircraft's motion space samples, forming equivalent spatially synthesized array samples. The refined angle estimation then employs the OMP-RELAX algorithm. Moreover, with the off-grid issue in mind, we devise an estimation method integrating Bayesian parameter estimation with dictionary sequence refinement. The proposed technique harnesses the spatial synthetic aperture for pinpoint estimation, effectively addressing the challenges of atomic orthogonality and angular off-grid on estimation accuracy. Importantly, the efficiency of deploying sparse reconstruction for angle estimation is bolstered by our phased strategy, eliminating the necessity for fine grid analysis across the entire observation scene. Moreover, the poor estimation accuracy caused by coherent source targets and angular-flickering targets is improved by sparse reconstruction. Through simulation and experiment, we affirm the proposed method's efficacy in angle estimation. The results indicate that target angle estimation errors are limited to within 1°. Furthermore, we assess the impact of variables such as target state, heading angle, spatial sampling points, and target distance on the estimation accuracy of our method, showcasing its resilience and adaptability.

With the rapid development of information technology in inland waterway, radar was widely used to track the ship running, in order to ensure the safe and efficient shipping industry development, as well as to avoid a ship collision causing unnecessary economic losses. For the problem of radar target tracking data associated, a method of data association with radar data was proposed, which was based on radar target course and speed. By this method, the target error or loss problem was solved when the target density was large and used belief rule-based (BRB) methodology to verify the reliability of this method. Real target points obtained by this method had been filtered through particle filter. And then the radar target tracking was achieved. This method has a good target tracking results, verified by Monte Carlo simulations.

Sea-skim~ing cruise missiles pose the greatest threat to a surface ship in the present-day war scenario. The convenitional close-in-weapon-systems (CIWSs) are becoming less reliable against these new challenges requiring extremely fast reaction time. Naval Forces see a high energy laser as a feasible andjeffective directed energy weapon against sea-skimming antiship cruise missiles becauseof its .ability to deliver destructive energy at the speed of light on to a distant target. The paper comparesthe technology and capability of deuterium fluoride (DF) and chemical-oxygen-iodine laser (COIL) in effectively performing the role of a shipborne CIWS altainst sea-skimming missiles. Out of these twolasers, it is argued that DF laser wo.uld be more effective a,s a shipborne weapon for defence against sea-skimmin,g cruise missiles. Besides the high energy laser as the primary (killing) laser, othersub-systems required in the complete weapon system would be: A beacon laser to sense phase distor'ions in the primary laser, adaptive optics to compensate the atmospheric distortions, beam-directing optics, illuminating lasers, IRST sensors, surveillance and tracking radars, interfacing system, etc.

In the radar target tracking stage, the repeater deception jamming can produce multiple false targets around the real target after matched filtering, which makes radar lose tracking of the real target. In this paper, a frequency-phase joint agility anti-repeater deception jamming waveform for Multiple Input Multiple Output (MIMO) radar is designed by the memristive radial basis function neural network (memristive-RBFNN), and the optimization of the waveform modulation coding is transformed into the optimization of the basis function parameters of the memristive-RBFNN. The memristive-RBFNN generates the frequency-phase joint agile waveform modulation sequence directly, thus greatly reduced the computational complexity of the waveform optimization algorithm. The designed frequency-phase joint agile waveform reduced the correlation between real target echo signal and repeater deception jamming to realize the suppression of repeater deception jamming. The simulation analyzes the computation complexity of proposed algorithm, and the jamming suppression performance is also given to verify the feasibility of this method proposed in this paper.

This study presents a self-correction method for a non-retrodirective cross-eye jamming (NRCJ) system based on the measurement of reference amplitude ratio and phase difference (ARPD). Cross-eye jamming performance is highly sensitive to gain and phase mismatches between the system's dual transmission paths, which can degrade the jamming effectiveness during operation. To address this issue, a reference system (RS) is introduced to measure the transmitted jamming signal in the jamming direction and compensate for deviations by adjusting the NRCJ system's variable attenuator and phase shifter. The RS stores reference ARPD values during initial calibration and compares them with current measurements to update control parameters when errors exceed predefined thresholds. A prototype NRCJ system operating at 8GHz was fabricated, and gain and phase errors of 2 dB and 3° were intentionally induced. Experimental results demonstrate that the proposed method successfully compensates for ARPD errors, reducing the gain and phase deviations to within 0.3 dB and 1°, respectively. Consequently, the magnitude of the cross-eye gain improved from 8.57 to 49.5, and the radar angle tracking error increased from 2.45° to 14.18°. These results confirm that the proposed self-correction approach enhances the robustness and jamming effectiveness of NRCJ systems in practical applications.

In the complex electromagnetic environment of modern electronic warfare, active deception jamming poses a serious threat to radar. This paper proposes the identification of active deception jamming based on residual neural network (RESNET), and studies the bispectral characteristics of the signal and the method of identifying the active deception jamming of RESNET. This algorithm firstly extracts the bispectral features of the jamming signal, and uses the bispectral diagonal slice as the input of RSNET to realize the recognition of active deception jamming. Simulation results show that the algorithm has high recognition accuracy, not only has good anti-noise performance, but also has excellent robustness under low JSR.

The anchoring and hooking of ships, bedrock friction and biological corrosion threaten the safety and stability of submarine cables. A hydraulic jet submarine cable laying machine manages to bury the submarine cables deep into the seabed, and effectively reduces the occurrence of external damage to the submarine cables. This machine uses a hydraulic jet system to realize trenching on the seabed. However, the hydraulic jet submarine cable laying machine has complicated operation and high power consumption with high requirements on the mother ship, and it is not yet the mainstream trenching method. In this paper, a mathematical model for the hydraulic jet nozzle of the submarine cable laying machine is established, and parameters that affect the trenching efficiency are studied. The effects of jet target distance, flow, angle and nozzle spacing on the working efficiency of the burying machine are analyzed by setting up a double-nozzle model. The results of the theory, numerical simulation and experiment show that the operational efficiency of the hydraulic jet submarine cable laying machine can be distinctly improved by setting proper jet conditions and parameters.

This paper deals with the suppression of mainlobe deceptive jammers in an orthogonal frequency division multiplexing-linear frequency modulation-multiple-input multiple-output (OFDM-LFM-MIMO) radar based on the blind source separation (BSS) algorithm. At the design stage, a colocated MIMO configuration is considered, where the LFM waveform is transmitted with a frequency increment across the antenna elements. After matched filtering in the receiver, the range-dependent degrees-of-freedom (DOFs) is obtained. Subsequently, the BSS algorithm based on joint matrix diagonalization operation is applied to separate the jammers and the true targets. Finally, the angles of true targets are estimated by utilizing the range-dependent compensation in the joint transmit-receive spatial frequency domain. Numerical simulations are provided to demonstrate the effectiveness of the proposed jammer suppression method.

As the fidelity of false targets generated by active deception jamming becomes higher and higher, it poses a huge challenge to the anti-jamming ability of single radar. Therefore, distributed radar has become one of the important means for future radar systems to counter jamming. This paper proposes a method for distributed radar to counter active deception jamming based on adaptive likelihood ratio test. This method conducts data and signal fusion on the echo signal of multiple radars, mainly including three parts. First, the pulse echoes of each radar station are used to locate the target to obtain viewing angle difference. At the same time, the average power is calculated and the correlation coefficient is estimated. Then, the theoretical correlation coefficients of real targets and false targets are calculated according to obtained viewing angle difference and average power. Finally, the estimated correlation coefficient and the theoretical correlation coefficient are used to conduct the likelihood ratio test, so as to determine authenticity of targets. This method can conduct adaptive tests based on target location and echo power. In addition, it is proposed under the condition that the correlation coefficient of real targets is non-constant, instead of simply assuming that correlation coefficient is a constant. Finally, effectiveness of the algorithm is verified by simulation in this paper.