MIMO radar space–time adaptive processing using prolate spheroidal wave functions

In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on space–time adaptive processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity

Wavelets in High-Capacity DWDM Systems and Modal-Division Multiplexed Transmissions

Optical communications receivers using wavelet signals processing is proposed in this paper for dense wavelength-division multiplexed (DWDM) systems and modal-division multiplexed (MDM) transmissions. The optical signal-to-noise ratio (OSNR) required to demodulate polarization-division multiplexed quadrature phase shift keying (PDM-QPSK) modulation format is alleviated with the wavelet denoising process. This procedure improves the bit error rate (BER) performance and increasing the transmission distance in DWDM systems. Additionally, the wavelet-based design relies on signal decomposition using time-limited basis functions allowing to reduce the computational cost in Digital-Signal-Processing (DSP) module. Attending to MDM systems, a new scheme of encoding data bits based on wavelets is presented to minimize the mode coupling in few-mode (FWF) and multimode fibers (MMF). The Shifted Prolate Wave Spheroidal (SPWS) functions are proposed to reduce the modal interference