Three-Dimensional Micromotion Signature Extraction of Rotating Targets in Ofdm-LFM Mimo Radar

Abstract

Abstract-In monostatic radars systems, only the micromotion signatures projected onto the radar line-of-sight (LOS) can be observed from echoes.As a result, the obtained micromotion signatures (e.g., the radius length of rotation) are sensitive to the radar LOS. In this paper, we propose a method for the accurate estimation of three-dimensional (3-D) micromotion signature with the orthogonal frequency division multiplexing -linear frequency modulation (OFDM-LFM) multi-input multi-output (MIMO) radar technique, which makes use of the advantages of the multi-view of MIMO radar systems and the broad bandwidth of the OFDM-LFM signals. In the proposed method, the Hough transform and Orthogonal Matching Pursuit (OMP) algorithm are introduced to extract the m-D curve features from echoes, and then the 3-D micromotion signatures of the rotating targets are obtained by solving nonlinear multivariable equation systems. The extracted 3-D micromotion signatures are no longer sensitive to the radar LOS, and can provide realistic feature information for target recognition. Simulations are given to validate the effectiveness of the proposed method.

Time‐varying three‐dimensional interferometric imaging for space rotating targets with stepped‐frequency chirp signal

“…In [7], a T/R‐R bistatic‐radar‐based method is proposed to obtain 3D target image. Making use of the advantages of multi‐view of distributed radar networks, a method of extracting the 3D precession features of cone‐shaped space targets is proposed in [8]. Multistatic‐radar‐based imaging method can successfully get the real image of target but it will face complex synchronisation and joint treatment of echoes of radars located in different view angles.…”

Performance bounds on micro-Doppler estimation and adaptive waveform design using OFDM signals

“…[5][6][7] Since then several research efforts have addressed the problem of target classification and recognition by extracting the distinguishable micro-Doppler features using the linear/nonlinear joint time-frequency distributions (TFDs). [8][9][10][11][12][13][14][15][16][17] Recently, estimation techniques of micro-Doppler frequency are also proposed based on the maximum likelihood and TFD approaches. [18][19][20][21][22][23] However, the characterization of statistical performance bounds on the micro-Doppler parameter estimation is quite limited in the literature, except for a few scenarios involving vibrating and rotating targets.…”