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Dense false target jamming can affect radar detection performance severely. A method of dense false targets jamming recognition based on time-frequency atomic decomposition theory and support vector machine (SVM) is proposed to solve the difficulty of dense false targets jamming identification. According to the feature of ambiguity function of dense false targets jamming signal, a Gabor sub-dictionary which has adaptive variation with signal is designed. The signal is expanded into the corresponding Gabor time-frequency dictionary by sparse decomposition. After Gabor atomic time-frequency parameters are extracted as individual feature vectors, SVM is utilised for classification and recognition. The experimental results show that the extracted Gabor atomic time-frequency parameters can effectively represent the essential features of target and dense false targets jamming, respectively, and this method has a high recognition rate.

Dense false target jamming can affect radar detection performance severely. A method of dense false targets jamming recognition based on time-frequency atomic decomposition theory and support vector machine (SVM) is proposed to solve the difficulty of dense false targets jamming identification. According to the feature of ambiguity function of dense false targets jamming signal, a Gabor sub-dictionary which has adaptive variation with signal is designed. The signal is expanded into the corresponding Gabor time-frequency dictionary by sparse decomposition. After Gabor atomic time-frequency parameters are extracted as individual feature vectors, SVM is utilised for classification and recognition. The experimental results show that the extracted Gabor atomic time-frequency parameters can effectively represent the essential features of target and dense false targets jamming, respectively, and this method has a high recognition rate. [ABSTRACT FROM AUTHOR]

A novel hardware architecture of orthogonal matching pursuit (OMP) is presented here, and the test is implemented on a field-programmable gate array (FPGA). The performance is evaluated by taking RADAR pulses that are compressively sampled synthetically using the random modulation preintegrator (RMPI). Basic test signals such as Gaussian pulse and its variations are taken as input to the RMPI. The output of the OMP algorithm is multiplied by the Gabor time-frequency dictionary to obtain the reconstructed RADAR signal. A novel method to implement the Gabor time-frequency dictionary is also presented. The OMP algorithm generates an estimate of a signal in $m~(\geq M)$ iterations for an $M$ -sparse signal. The proposed design is implemented on the Artix7 FPGA device for $K=80$ , $N=1024$ , and $m=16$ , where $N$ is the number of samples and $K$ is the measurement vector length. The design is also implemented for $K=256$ , $N=1024$ , and $m=36$ using the Virtex6 FPGA device for comparison with other existing designs. The recovery signal-to-noise ratio (RSNR) of 18.336 dB is achieved with this technique. The proposed design utilizes $(3m-1)$ fewer multipliers and consumes 27% less dynamic power compared to previously published FPGA implementation of OMP. The proposed design is hardware efficient even for the higher value of $m/K$. [ABSTRACT FROM AUTHOR]