Abstract-In civilian communication systems, the signature sequence of the desired signal in training phase is known to the receiver. In this letter, using the mutual information, we bridge the probability density function and minimum mean-square error (MMSE) between the observed data and training sequence of the desired signal, and then employ the MMSE to construct a minimum description length (MDL) criterion for accurate source enumeration. Numerical results demonstrate that the proposed method is superior to existing MDL methods in terms of detection performance particularly for small number of snapshots and/or source angular separation.Index Terms-Eigenvalue decomposition, minimum description length, sensor array processing, source number estimation.
The coupling among multiple coded orthogonal waveforms occupying the same frequency band may seriously affect the distributed terrain imaging of multiple-input and multiple-output synthetic aperture radar (MIMO-SAR). Based on the inter-pulse phase modulation among different transmitting waveforms, a range-Doppler decouple filtering method is presented to separate waveforms effectively. Finally, numerical experiments are provided to demonstrate the effectiveness of the proposed method.
The unwanted coupling exists inevitably among multiple orthogonal waveforms in a same frequency area for multiple-input and multiple-output synthetic aperture radar (MIMO-SAR). In this paper, a new polarized MIMO-SAR model is established with two transmitting antennas and multiple receiving antennas at first. Then, a virtual polarization filter (VPF) is proposed to separate superposed returns caused by multiple transmitted waveforms based on detection on the polarized parameters via particle swarm optimizer (PSO). Compared with the conventional matched filter to separate the orthogonal waveforms, it is shown that the coupling noises can be significantly suppressed by the proposed VPF-based method and the orthogonality is not necessary among different transmitting waveforms. Finally, experimental data experiments are also provided to demonstrate the effectiveness of the proposed method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.