A Novel Sparse Stepped Chaotic Signal and Its Compression Based on Compressive Sensing

Abstract

Abstract-We propose a novel signal model by combining the sparse stepped frequency signals with chaotic signals, i.e., the sparse stepped chaotic signal (SSCS) model, as well as the corresponding compression algorithm based on compressed sensing. In SSCS, the chaotic signals are modulated to sparse stepped frequencies to compose a transmitting burst. When receiving, the echo signals are demodulated to the baseband and then can be sampled directly at a rate much lower than the Nyquist rate determined by the bandwidth of chaotic signal of each subpulse. Compared with radars using conventional stepped frequency waveforms, the SSCS radar can transmit fewer subpulses in a burst and directly use lower speed ADC next to the receiver. Both simulated and real radar data are processed to demonstrate the effectiveness of the proposed SSCS as well as the compression algorithm by which high resolution range profiles are very well reconstructed.

Novel compressive sensing‐based Dechirp‐Keystone algorithm for synthetic aperture radar imaging of moving target

“…In order to evaluate the algorithm quantitatively, we conduct Monte‐Carlo experiments in noisy environment. The mean squared error (MSE) [18, 22] is taken as the evaluation criterion. The MSE of CS‐DKA results with different down‐sampled data under different SNRs are presented in Fig.…”

“…The mean squared error (MSE) [18,22] is taken as the evaluation criterion. The MSE of CS-DKA results with different down-sampled data under different SNRs are presented in Fig.…”

“…Therefore, from (20)- (22) we can conclude that the DKO matrix T is a unitary matrix as well, that is…”

“…Nowadays, the CS has shown very good prospects in SAR/ISAR applications [17][18][19][20][21][22][23][24][25][26][27][28][29]. In [19], the authors proposed a CS-based high-resolution imaging algorithm to overcome strong noise and clutter for ISAR imaging.…”

“…In recent years, the compressive sensing (CS) has emerged, which states that it is possible to reconstruct sparse or compressible signals from highly incomplete samples or measurements even far less than that required by the Nyquist theorem [15, 16]. Nowadays, the CS has shown very good prospects in SAR/ISAR applications [17–29]. In [19], the authors proposed a CS‐based high‐resolution imaging algorithm to overcome strong noise and clutter for ISAR imaging.…”