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ABSTRACT: The usage of multi-input multi-output (MIMO) systems such as a MIMO radar allows the array elements to transmit different waveforms freely. This waveform diversity can lead to flexible transmit beampattern synthesis, which is useful in many applications such as radar/sonar and biomedical imaging. In the past literature most attention was paid to receive beampattern design due to the stringent constraints on waveforms in the transmit beampattern case. Recently progress has been made on MIMO transmit beampattern synthesis but mainly only for narrowband signals. In this paper we propose a new approach that can be used to efficiently synthesize MIMO waveforms in order to match a given wideband transmit beampattern, i.e., to match a transmit energy distribution in both space and frequency. The synthesized waveforms satisfy the unit-modulus or low peak-to-average power ratio (PAR) constraints that are highly desirable in practice. Several examples are provided to investigate the performance of the proposed approach.
IEEE Transactions on Signal Processing 03/2011; · 2.63 Impact Factor
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ABSTRACT: One of the main objectives of cognitive radar is to adapt the spectrum of transmit waveforms to certain needs, such as avoiding reserved frequency bands or narrowband interferences. Besides spectral requirements, good correlation properties of the transmit waveforms are also desired in specific applications, such as range compression. Moreover, practical hardware constraints usually require the transmit waveforms be unimodular (i.e. only phase-modulated). In this paper, we propose a new algorithm named SCAN (stopband cyclic algorithm new) to design unimodular sequences with spectral power suppressed in arbitrary bands and with low correlation sidelobes as well. The SCAN algorithm, which starts from random initializations, can generate many sequences possessing similarly good properties. Furthermore, the SCAN algorithm is based on FFT (fast Fourier transform) operations and thus is computationally efficient, which facilitates long-sequence design and real-time waveform update.
Cognitive Information Processing (CIP), 2010 2nd International Workshop on; 07/2010
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ABSTRACT: We present a new derivation of a lower bound for an aperiodic correlation metric: the integrated sidelobe level (ISL) of a set of sequences under the energy constraint. Sequences (or sequence sets) with low aperiodic correlations are widely demanded in many applications, including radar/sonar range compression, medical imaging, channel estimation and multi-user spread-spectrum communications. While the lower bound has been implicitly discussed in the literature before, here we adopt a different framework to derive the bound. In particular, we make use in the derivation of our recently proposed cyclic algorithm framework, which can also be used to efficiently synthesize unimodular sequences with low correlations. We also show that by relaxing the unimodular constraint, the ISL lower bound can be approached closely.
IEEE Signal Processing Letters 04/2010; · 1.39 Impact Factor
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ABSTRACT: Sequence sets with low periodic correlations are used in many areas, such as asynchronous code-division multiple access (CDMA) systems, medical imaging, radar and sonar. Lower bounds on the integrated sidelobe level (ISL) and the peak sidelobe level (PSL) of periodic sequence sets, under a power constraint, have been previously derived in the literature. In this letter, we obtain the ISL and PSL lower bounds using a different framework. The main contribution of the letter consists in using this framework to derive closed-form expressions for all power constrained periodic sequence sets that meet the ISL lower bound.
IEEE Signal Processing Letters 02/2010; · 1.39 Impact Factor
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ABSTRACT: Sequence sets with good periodic correlation properties can be used in many areas, including communications, medical imaging, radar (such as over-the-horizon radar) and sonar. Practical hardware constraints, such as power amplifiers, usually require the transmitted waveforms be unimodular. We present herein new computationally efficient algorithms that can be used for the design of unimodular sequence sets with essentially zero auto-correlation sidelobes and cross-correlations in a specified time lag zone, as well as of sequence sets with good correlations over all time lags. The proposed algorithms start from random phase initializations and can generate many different sequence sets (including very long sequence sets) possessing similarly good correlation properties.
Signals, Systems and Computers, 2009 Conference Record of the Forty-Third Asilomar Conference on; 12/2009
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ABSTRACT: A multiple-input multiple-output (MIMO) radar system that transmits orthogonal waveforms via its antennas can achieve a greatly increased virtual aperture compared with its phased-array counterpart. This increased virtual aperture enables many of the MIMO radar advantages, including enhanced parameter identifiability and improved resolution. Practical radar requirements such as unit peak-to-average power ratio and range compression dictate that we use MIMO radar waveforms that have constant modulus and good auto- and cross-correlation properties. We present in this paper new computationally efficient cyclic algorithms for MIMO radar waveform synthesis. These algorithms can be used for the design of unimodular MIMO sequences that have very low auto- and cross-correlation sidelobes in a specified lag interval, and of very long sequences that could hardly be handled by other algorithms previously suggested in the literature. A number of examples are provided to demonstrate the performances of the new waveform synthesis algorithms.
IEEE Transactions on Signal Processing 12/2009; · 2.63 Impact Factor
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ABSTRACT: Sequences with impulse-like correlations are at the core of several radar and communication applications. Two criteria that can be used to design such sequences, and which lead to rather different results in the aperiodic correlation case, are shown to be identical in the periodic case. Furthermore, two simplified versions of these two criteria, which similarly yield completely different sequences in the aperiodic case, are also shown to be equivalent. A corollary of these unexpected equivalences is that the periodic correlations of an arbitrary sequence must satisfy an intriguing identity, which is also presented in this letter.
IEEE Signal Processing Letters 09/2009; · 1.39 Impact Factor
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ABSTRACT: A multi-input multi-output (MIMO) radar system that transmits orthogonal waveforms via its antennas can achieve a greatly increased virtual aperture compared with its phased-array counterpart. Practical radar requirements such as unit peak-to-average power ratio and range compression dictate that we use MIMO radar waveforms that have constant modulus and good auto- and cross-correlation properties. We present in this paper new computationally efficient cyclic algorithms for MIMO radar waveform synthesis. These algorithms can be used for the design of unimodular MIMO sequences that have very low auto-and cross-correlation sidelobes in a specified lag interval, and of very long sequences that could hardly be handled by other algorithms previously suggested in the literature. A number of examples are provided to demonstrate the performances of the new waveform synthesis algorithms.
Radar Conference, 2009 IEEE; 06/2009
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ABSTRACT: Unimodular (i.e., constant modulus) sequences with good autocorrelation properties are useful in several areas, including communications and radar. The integrated sidelobe level (ISL) of the correlation function is often used to express the goodness of the correlation properties of a given sequence. In this paper, we present several cyclic algorithms for the local minimization of ISL-related metrics. These cyclic algorithms can be initialized with a good existing sequence such as a Golomb sequence, a Frank sequence, or even a (pseudo)random sequence. To illustrate the performance of the proposed algorithms, we present a number of examples, including the design of sequences that have virtually zero autocorrelation sidelobes in a specified lag interval and of long sequences that could hardly be handled by means of other algorithms previously suggested in the literature.
IEEE Transactions on Signal Processing 05/2009; · 2.63 Impact Factor
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ABSTRACT: We begin by revisiting the periodogram to explain why arguably the plain least-squares periodogram (LSP) is preferable to the ldquoclassicalrdquo Fourier periodogram, from a data-fitting viewpoint, as well as to the frequently-used form of LSP due to Lomb and Scargle, from a computational standpoint. Then we go on to introduce a new enhanced method for spectral analysis of nonuniformly sampled data sequences. The new method can be interpreted as an iteratively weighted LSP that makes use of a data-dependent weighting matrix built from the most recent spectral estimate. Because this method is derived for the case of real-valued data (which is typically more complicated to deal with in spectral analysis than the complex-valued data case), it is iterative and it makes use of an adaptive (i.e., data-dependent) weighting, we refer to it as the real-valued iterative adaptive approach (RIAA). LSP and RIAA are nonparametric methods that can be used for the spectral analysis of general data sequences with both continuous and discrete spectra. However, they are most suitable for data sequences with discrete spectra (i.e., sinusoidal data), which is the case we emphasize in this paper.
IEEE Transactions on Signal Processing 04/2009; · 2.63 Impact Factor
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ABSTRACT: We begin by revisiting the plain least-squares periodogram (LSP) for real-valued data. Then we introduce a new method for spectral analysis of non-uniformly sampled data by "iteratively weighting LSP", and we name the new method real-valued iterative adaptive approach (RIAA). LSP and RIAA are most suitable for data sequences with discrete spectra. For such type of data, we present a procedure to obtain a parametric spectral estimate, from the LSP or RIAA non-parametric estimate, by means of the Bayesian information criterion (BIC). We also discuss a possible strategy for designing the sampling pattern of future measurements. Several numerical examples are provided to illustrate the performance of our proposed approaches.
Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th; 02/2009
