Elimination of cross‐terms in the Wigner–Ville distribution of multi‐component LFM signals

Shen

Trans Emerging Tel Tech

2019

With the increase of radar signal modulations and the emergence of new system radars, the receiver will intercept radar signals at the same time. In order to accurately estimate and suppress the signals, this paper proposes an accurate recognition system for radar emitter signals. The system can effectively separate multiple signals and accurately recognize Binary Phase Shift Keying (BPSK), Linear Frequency Modulation (LFM), Continuous Wave (CW), Costas, Frank code, and P1 to P4 codes. The separation technique based on fractional Fourier transform is proposed to decompose received signals into multiple components.Furthermore, a transferable GoogleNet is explored to achieve accurate recognition of the first component with better separation effect. Meanwhile, variational mode decomposition is developed to eliminate the noise of the second component; then, the fusion features are extracted to improve the recognition rate of the second component. Finally, the improved particle swarm optimization algorithm is proposed to find best support vector machine parameters. The simulation results show that the recognition rate of single signal and double signals can reach 96.23% and 72%, respectively, when signal-to-noise ratio is 0 dB.The system can also bring some inspiration to medical and mechanical signal recognition.Trans Emerging Tel Tech. 2019;30:e3612.wileyonlinelibrary.com/journal/ett

Section: Introductionmentioning

confidence: 99%

Modulation recognition for radar emitter signals based on convolutional neural network and fusion features

Shen

Trans Emerging Tel Tech

2019

“…A pure idea is offered by Aiordachioaie and Popescu, as starting initial point to compose an approach to suppress the cross terms and to attain an exact image of WVD, including only the auto-terms [22]. A new method is offered by Wu and Li to suppress cross terms in the WVD of linear frequency modulation signals with multicomponent [23].…”

Section: Introductionmentioning

confidence: 99%

Spatial time-frequency distribution of cross term-based direction-of-arrival estimation for weak non-stationary signal

Shao

Liu

et al. 2019

J Wireless Com Network

In the radar array signal processing direction of arrival (DOA), the estimation of weak non-stationary signal is an important and difficult problem when both strong and weak signals are coexisting particularly because the weak non-stationary signals are often submerged in noise. In this paper, we proposed a novelty method to estimate the direction of arrival (DOA) of weak non-stationary signal in scenario for strong non-stationary interference signals and Gaussian white noise. The method utilizes spatial time-frequency distribution (STFD) of cross terms rather than suppressing cross terms in time-frequency analysis. The STFD of cross terms are introduced as an alternative matrix, which is similar to data covariance matrix in multiple signal classification (MUSIC), for the DOA estimation of a weak non-stationary signal. The cross-term amplitude of the strong and weak signals is usually above the noise and is easier to use than the auto-term of the weak signal. In the cross term, the information of the weak signal is included, and the auto-term of these weak signals is difficult to extract directly. The ability to incorporate the STFD of cross terms empowers information about a weak non-stationary signal for DOA estimation, leading to improved signal estimates for direction finding. The method based on the STFD of cross terms for DOA estimation of the weak non-stationary signal is revealed to outperform the time-frequency MUSIC and traditional MUSIC algorithm by simulation, respectively. This method has the advantages of the time-frequency direction finding method and also deals with the situation of weak signals. When the strong and weak signals exist at the same time and the two angles are similar, the cross-terms can be used to perform DOA estimation on the weak signal.

“…Although WVD represents the energy distribution of the signal well, the effect of the cross-terms still restricts the detection performance. A method was put forward by Yushuang Wu and Xiukun Li to remove the cross-terms in WVD [ 10 ], but it has not been applied for target detection. The energy of the LFM signal can be focused by fractional Fourier transform (FrFT), which is used for radar detection in heavy sea clutter [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Moving Target Detection Using Dynamic Mode Decomposition

Yin

Liu

Zhu

et al. 2018

Sensors

It is challenging to detect a moving target in the reverberant environment for a long time. In recent years, a kind of method based on low-rank and sparse theory was developed to study this problem. The multiframe data containing the target echo and reverberation are arranged in a matrix, and then, the detection is achieved by low-rank and sparse decomposition of the data matrix. In this paper, we introduce a new method for the matrix decomposition using dynamic mode decomposition (DMD). DMD is usually used to calculate eigenmodes of an approximate linear model. We divided the eigenmodes into two categories to realize low-rank and sparse decomposition such that we detected the target from the sparse component. Compared with the previous methods based on low-rank and sparse theory, our method improves the computation speed by approximately 4–90-times at the expense of a slight loss of detection gain. The efficient method has a big advantage for real-time processing. This method can spare time for other stages of processing to improve the detection performance. We have validated the method with three sets of underwater acoustic data.