An angle error extraction algorithm based on JADE for three-channel radar seeker system with the existence of deception jamming

The chaotic compound short-range detection system is a new type of short-range detection system, which has strong anti-jamming ability. However, for the deception jamming, the characteristics of the complex short-range detection system are very similar to the detection echo, which poses a serious threat to the detection system. In order to analyze and extract the different characteristics between deceptive jamming and target echo signal, so as to realize the anti-deceptive jamming of chaotic compound short-range detection system, this article analyzes and simulates the mathematical model of deceptive jamming and target echo, and analyzes the bispectral characteristics of the simulated echo and jamming signal, and a set of anti-deception jamming feature parameters has been constructed. The identification of deceptive interference is realized by genetic algorithm–back propagation neural network, and the recognition accuracy is high and the real-time performance is good.

During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments.

In this chapter, an effective blind source separation (BSS) algorithm is applied to solve the co-channel interference problem in wireless communication systems. Algorithms developed for this purpose must not only have the capability of working in the complex domain and improving output signal to interference plus noise ratio (SINR), but also have relatively low computational complexity. We propose a fast Fourier transform (FFT)-based algorithm called feedback independent component analysis (FebICA) that is able to blindly separate complex modulated digital signals. By applying this algorithm to communication signals, it is observed that it has the advantages of SINR gain improvement as well as low computational complexity. The performance of the FebICA algorithm is shown to be better than the joint approximate diagonalization of eigen-matrices (JADE) algorithm in terms of the output SINR and requires lower computational complexity than the analytical constant modulus algorithm (ACMA). The algorithm is also shown to be more robust with increasing number of sources compared to other algorithms. The separation performance by using the collected field data has also been demonstrated.

Electroencephalogram (EEG) extraction has widely used Stone's Blind Source Separation (Stone's BSS) algorithm. However, Stone's BSS algorithm is sensitive to the initial half-life (hlong, hshort) and weight vector W parameters, which affect the convergence of the algorithm. This paper proposes a hybridization of Stone's BSS with Particle Swarm Optimization (PSO) to boost the separation process. An improved Stone's BSS (ISBSS) method is employed to reject eye blinking from the electroencephalogram (EEG) mixture. The electroencephalogram (EEG) mixed-signal is first centralized and whitened; then, it is incorporated into the particle swarm optimization (PSO) iterative algorithm to process the initial (hlong, hshort) and generate the weight vector W parameters randomly. Finally, the generalized eigenvalue decomposition (GEVD) method is applied to extract EEG singles to obtain a clean EEG signal. A clinical EEG database is used to test the improved and other algorithms. The GEVD method estimates the measurement performance of the proposed algorithm using a carrier-to-interference ratio and integral square error and compares the proposed algorithm with the conventional Stone's BSS, fast independent component analysis (FastICA), evolutionary fast independent component analysis (EFICA), and joint approximate diagonalization of eigen matrices (JADE) algorithms to check its effectiveness. The results show that the suggested hybrid method has a better performance and decreasing elapsed time than conventional Stone's BSS and other algorithms.

Mutual information map as a new way for exploring the independence of chemically meaningful solutions in two-component analytical data

The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non-negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple-to-use-interactive self-modeling mixture analysis (SIMPLISMA) and multivariate curve resolution-alternating least squares (MCR-ALS)] are applied for simultaneous (1)H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight-component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography-mass spectrometry as well as the MCR-ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures.

The broadcast nature of wireless channels leads to the developments and applications of physical layer security techniques. The Artificial Noise (AN) aided technique is proposed to interfere with the eavesdropper for secure communications. A suppression method called Joint Approximate Diagonalization of Eigen-matrices (JADE), which is based on blind source separation algorithm (BSS), is presented under this circumstance. The eavesdropper is able to separate the communication signal and the noise. Simulation results demonstrate that the proposed method is feasible under certain circumstances, which helps the eavesdropper to defeat the AN aided secure communications effectively.

Aiming at the issue of detection and suppression of the direct-wave deceptive jamming modulated and forwarded by the jammer, an anti-jamming approach discriminating and suppressing false targets is proposed in this paper. This approach utilizes displace phase-center antenna (DPCA) processing of three channels, which based on the model of jamming and target signal of synthetic aperture radar(SAR), to make the stationary target signal disappear and display the false targets, then adopts the along-track interference method to estimate the direction of the jammer. In addition, the least square error pattern synthesis with nulls is also introduced to suppress deceptive jamming in Doppler and spatial domain. Finally, the simulation results demonstrate the correctness of the proposed deceptive jamming estimation and suppression algorithm.

Target echo cancellation is an ingenious method that protects the target of interest (TOI) from being detected by radar. Interrupted-sampling repeater jamming (ISRJ) is a novel deception jamming method for linear frequency modulation (LFM) radar countermeasures, which has been applied in target echo cancellation recently. Compared with the conventional cancellation method, not only can the target echo be successfully cancelled at radar receiver, but a train of false targets is also produced and forms deception jamming by applying the ISRJ technique. In this paper, an improved radar target echo cancellation method based on ISRJ is proposed that utilizes an extra frequency shifting modulation on the intercepted LFM radar signal. The jammer power is more efficiently utilized by the proposed method. Moreover, more flexible multi-false-target deception jamming can be obtained by adjusting the interrupted sampling frequency. The real target remains effectively protected by the false preceding target in the presence of amplitude mismatch of cancellation signal and target echo. Numerical simulations and measured data experiments are conducted to demonstrate the effectiveness of the proposed method.

The application of denoising source separation (DSS) technology to the mechanical vibration signal processing provides a new technique of mechanical fault diagnosis. The DSS theory is investigated in this paper. The analog signals of the rotating machine are separated and the performance index and correlation coefficient of DSS are better than those of the joint approximate diagonalization of eigen-matrices (JADE). Applying the DSS method to rotating machine fault diagnosis, the measured fault signals are analyzed and the results are found to be in agreement with practice. The results show that the DSS method is efficient in analyzing the fault diagnosis of rotating machine.

Spectrum sharing between radar and communication systems has attracted substantial recent research attention. In order to avoid the mutual interference, source separation of the aliasing signal is a promising solution for joint communication and radar (JCR) sensing. However, most of the existing source separation methods are very sensitive to wide-band signals with rapidly varying instantaneous amplitude (IA) and not applicable to scenarios with complex overlapping subsignals. To overcome the above limitations, a data-driven sparsity-based source separation method is proposed. Without depending on signal parameters, a sparse signal observation model of the received signal is established. Then, source separation is transformed into estimation of the IA and instantaneous frequency (IF), and an iterative method is developed. Moreover, the Cramér-Rao lower bound (CRLB) of the source separation error (SSE) variance is derived based on the CRLB of IA and IF. Simulation results demonstrate that our proposed method is superior to the benchmark schemes and is robust to noise and initial IF errors.

This article deals with the problem of mainlobe jamming suppression for polarization-sensitive phased-array radar system via a joint polarization-space-time processing method resorting to tensor decomposition. First, a radar model accounting for one target and multiple mainlobe jammers in the far field is considered, which is equipped with a polarized transmitted antenna and a uniform linear dual-polarization receiving array. Then, exploiting the polarization-space-time characteristics, tensor CANDECOMP/PARAFAC (CP) decomposition is introduced to recast the received signal as a third order tensor consisting of three factor matrices. Furthermore, the target echo and the jamming signal can be obtained by minimizing the Frobenius norm of the approximation error between the tensor and the outer product of the factor matrices. Finally, the effectiveness of the proposed method by numerical simulation is verified, showing its capability to combat various types of mainlobe jamming as well as their compound forms compared with the art competing method.

In the recent past, blind source separation (BSS) algorithms using multivariate statistical data analysis technique have been successfully used for source identification and separation in the field of biomedical and statistical signal processing. Recently numbers of different BSS techniques have been developed. With BSS methods being the feasible method for source separation and decomposition of biosignals, it is important to compare the different techniques and determine the most suitable method for the applications. This paper presents the performance of five BSS algorithms (SOBI, TDSEP, FastICA, JADE and Infomax) for decomposition of sEMG to identify subtle finger movements. It is observed that BSS algorithms based on second-order statistics (SOBI and TDSEP) gives better performance compared to algorithms based on higher-order statistics (FastICA, JADE and infomax).

Multichannel synthetic aperture radar (SAR) systems represent a critical advancement in SAR development, yet their effectiveness is increasingly challenged by complex electromagnetic environments. Deceptive jamming, capable of generating false targets, significantly compromises target detection and tracking performance. Conventional blind source separation (BSS) methods fail when jammers exceed system degrees of freedom (DOF), while channel cancellation techniques are ineffective against multiple interference sources. To address these challenges, this paper presents an innovative integrated approach combining jammer localization and suppression. First, jammer directions-of-arrival (DOAs) are estimated via clustering analysis of SAR-imaged data, exploiting the inherent sparsity of deceptive targets to resolve mixing-matrix ambiguity without post-separation identification. Second, a pre-transformation processing method based on sequential quadratic programming (SQP) was implemented to equivalently modify the DOAs distribution of jammers. This innovative approach enables the proposed scheme to effectively handle scenarios with most general distributed jammer directions. Finally, effective jamming suppression is achieved combined with the improved minimum <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula>-norm method. Experimental results demonstrate significant performance improvements, including a 21.75 dB enhancement in the target region's peak signal-to-noise ratio (PSNR) and an interference suppression ratio (ISR) reduction to -21.75 dB in distributed target, validating the method's effectiveness in general deceptive jamming scenarios.

Tropospheric duct is an anomalous atmospheric phenomenon over the sea surface that seriously affects the normal operation and performance evaluation of electromagnetic communication equipment at sea. Therefore, achieving precise sensing of tropospheric duct is of profound significance for the propagation of electromagnetic signals. The approach of inverting atmospheric refractivity from easily measurable radar sea clutter is also known as the refractivity from clutter (RFC) technique. However, inversion precision of the conventional RFC technique is low in the low-altitude evaporation duct environment. Due to the weak attenuation of the over-the-horizon target signal as it passes through the tropospheric duct, its strength is much stronger than that of sea clutter. Therefore, this study proposes a new method for the joint inversion of evaporation duct height (EDH) based on sea clutter and target echo by combining deep learning. By testing the inversion performance and noise immunity of the new joint inversion method, the experimental results show that the mean error RMSE and MAE of the new method proposed in this paper are reduced by 41.2% and 40.3%, respectively, compared with the conventional method in the EDH range from 0 to 40 m. In particular, the RMSE and MAE in the EDH range from 0 to 16.7 m are reduced by 54.2% and 56.4%, respectively, compared with the conventional method. It shows that the target signal is more sensitive to the lower evaporation duct, which obviously enhances the inversion precision of the lower evaporation duct and has effectively improved the weak practicality of the conventional RFC technique.