Generation of Correlated Finite Alphabet Waveforms Using Gaussian Random Variables

Abstract

This application claims priority to, and the benefit of, co-pending U.S. provisional application entitled "Generation of Correlated Finite Alphabet Waveforms Using Gaussian Random Variables" having serial no. 62/022,832, filed July 10, 2014, and co-pending U.S. provisional application entitled "Generation of Correlated Finite Alphabet Waveforms Using Gaussian Random Variables" having serial no. 14/751,916, filed June 26, 2015, both of which are hereby incorporated by reference in their entirety. BACKGROUND [0002] Correlated waveforms or random variables (RVs) are utilized in a number of fields. For example, in communications the noise at different receive antennas can be correlated (colored). Similarly depending on the radar parameters and sea surface conditions, each component of the received sea clutter may be correlated. In both of these applications, correlated RVs are generated for simulations. Therefore, generation of such waveforms is not challenging. Software radar is an emerging technology, where characteristics of radar, such as beampattern, signal to interference plus noise ratio (SINR), and side-lobe-levels (SLLs), can be changed through software without changing any hardware. This technology requires the design of correlated waveforms. To use such waveforms in practice, their peak-to-average power ratio (PAPR) should be close to unity and the symbols of the waveform should be drawn from finite alphabets. Generation of such waveforms is very challenging.

Low Complexity Moving Target Parameter Estimation for MIMO Radar Using 2D-FFT

“…In collocated MIMO radar, fully independent waveforms provide extra degrees-of-freedom (DOF), improved spatial resolution, and better parametric identifiability [3]- [5], at the price of reducing the transmit coherent processing gain. However, carefully designed correlated waveforms can electronically steer the beam in different directions of interest [1], [6], [7]. This increases the signal-to-noise-ratio (SNR) at the receiver and improves the estimation performance of the system.…”

“…design algorithm proposed in[7]. The designed signals maximize the transmitted power in the region of interest between −30 • and 30 • .…”

SINR maximization in colocated MIMO radars using transmit covariance matrix

“…The purpose of waveform design in radars is to transmit power in certain directions in order to enhance the signal-to-interference-plusnoise ratio (SINR) at the receiver. The process of waveform design can be divided into two parts; (a) designing the transmit waveform covariance matrix R [6][7][8][9][10], (b) synthesizing the transmit waveforms in order to realizes the covariance matrix R [11][12][13]. Several algorithms are proposed to design Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/sigpro transmit waveform with different constraints such as low peak-to-average power ratio (PAPR) and proper ambiguity function [14].…”

Sequential quasi‐convex‐based algorithm for waveform design in colocated multiple‐input multiple‐output radars

“…Waveform design in transmit signals is one of the most interesting and important problems in both types of MIMO radar systems and therefore has attracted significant attention [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. In general, the purpose of waveform design problem is to achieve a desired beampattern or concentrating the transmit power in certain directions by designing the transmit covariance matrix R. The waveform design process can be classified into two main classes: the first class only considers the problem in transmitter [7][8][9][10][11][12][13] and the second class of the waveform design considers both radar transmitter and receiver. In this paper, we focus on the second class whose aim is to design the transmit waveform and receive combining filter, in order to maximise the signal-to-interference-plus-noise ratio (SINR) for targets in the presence of signal-dependent interferences.…”