Transmit beamforming for MIMO radar systems using partial signal correlation

By D. Fuhrmann, G. San Antonio. Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004., 2004

307 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/11f9c8bfaa4c8a3fa22c684d2f8a8f8c24a88366

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. P. Vaidyanathan
2. Jian Li
3. P. Stoica
4. Chun-Yang Chen
5. Rick S. Blum
6. A. Haimovich
7. Hana Godrich
8. Luzhou Xu

In Signal Processing

1. Jian Li
2. P. Vaidyanathan
3. P. Stoica
4. Chun-Yang Chen
5. Luzhou Xu
6. Rick S. Blum
7. A. Haimovich
8. Visa Koivunen

Citations in the corpus

• Signal Synthesis and Receiver Design for MIMO Radar Imaging (2008, background): “Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the U.S. Government.”
• On Parameter Identifiability of MIMO Radar (2007, background): “signals are narrowband and that the propagation is non-dispersive, the baseband signal at the target location can be described by the following expression (see, e.g., [ 3 ] and [8, Ch. 6 ]):”
• MIMO Radar with Widely Separated Antennas (2008, background): “Elements of MIMO radar transmit independent waveforms result in an omnidirectional beampattern or create diverse beampatterns by controlling correlations among transmitted waveforms [16].”
• Target detection and parameter estimation for MIMO radar systems (2008, methodology): “It has been shown that by exploiting this waveform diversity, MIMO radar can overcome performance degradations caused by the radar cross section (RCS) fluctuations [1—4], achieve flexible spatial transmit beampattern design [5, 6], provide high-resolution spatial spectral estimates [7—16], and signi”
• Signal Covariance Matrix Optimization for Transmit Beamforming in MIMO Radars (2007, methodology): “An optimization method that results in full-rank covariance matrix was proposed in [1].”
• MIMO Radar Waveform Optimization With Prior Information of the Extended Target and Clutter (2009, methodology): “In [5] and [6], the covariance matrix of the transmitted waveforms is designed such that the power can be transmitted to a desired range of angles.”
• On Probing Signal Design For MIMO Radar (2006, methodology): “…with location is given by (4) where is the covariance matrix of , i.e., (5) 1053-587X/$25.00 © 2007 IEEE The “spatial spectrum” in (4), as a function of , will be called the transmit beampattern (a more detailed derivation of, and further discussions on, (1)–(5) above can be found in [3], [8]).”
• Transmit beamforming for MIMO radar systems using signal cross-correlation (2008, methodology): “In [7] we showed how a gradient search algorithm in the Cholesky factor space of positive definite Hermitian matrices could be used to compute an optimal signal correlation matrix R, when using the cost function”
• MIMO Radar Space–Time Adaptive Processing Using Prolate Spheroidal Wave Functions (2008, background): “MIMO radars emit orthogonal waveforms [1]–[10] or noncoherent [11]–[13] waveforms instead of transmitting coherent waveforms which form a focused beam in traditional transmitter based beamforming.”
• Target Localization Accuracy Gain in MIMO Radar-Based Systems (2008, background): “In [7], [14], [ 15 ] the signal vector transmitted by a MIMO radar system is”
• Range Compression and Waveform Optimization for MIMO Radar: A CramÉr–Rao Bound Based Study (2007, methodology): “Waveform optimization has been used to achieve flexible transmit beampattern designs for MIMO radar (see, e.g., [ 7 ] and [17]).”