We investigate several target detection and parameter estimation techniques for a multiple-input multiple-output (MIMO) radar system. By transmitting independent waveforms via different antennas, the echoes due to targets at different locations are linearly independent of each other, which allows the direct application of many data-dependent beamforming techniques to achieve high resolution and excellent interference rejection capability. In the absence of array steering vector errors, we discuss the application of several existing data-dependent beamforming algorithms including Capon, APES (amplitude and phase estimation) and CAPES (combined Capon and APES), and then propose an alternative estimation procedure, referred to as the combined Capon and approximate maximum likelihood (CAML) method. Via several numerical examples, we show that the proposed CAML method can provide excellent estimation accuracy of both target locations and target amplitudes. In the presence of array steering vector errors, we apply the robust Capon beamformer (RCB) and doubly constrained robust Capon beamformer (DCRCB) approaches to the MIMO radar system to achieve accurate parameter estimation and superior interference and jamming suppression performance.
[Show abstract][Hide abstract] ABSTRACT: For colocated MIMO radar target detection in colored noise, we propose two adaptive detectors according to the Rao and Wald test criteria. These detectors do not need training data and possess constant false alarm rate properties. We investigate the manner how they work. From a detection viewpoint we show that there is no need of matched filtering for colocated MIMO radar. We derive the statistical distributions of the proposed detectors, and then obtain the analytical expressions for the probabilities of false alarm and detection both for deterministic and random signals. Numerical examples are provided to compare the detection performance of the Rao and Wald tests with an existing detector.
IEEE Transactions on Aerospace and Electronic Systems 10/2015; 51(3). DOI:10.1109/TAES.2015.130754 · 1.76 Impact Factor
"Typically, it is assumed that at each receiver a technique exists for unambiguously separating the reflected signals of interest (SOI) from each transmitter, by utilizing orthogonal waveforms and a matched filter bank. In  , many researchers have shown that, by utilizing orthogonal waveforms, a MIMO radar system with spatially diverse transmitters and receivers, can provide advantages in target detection and parameter estimation compared to a traditional phased array system . For collocated transmit and receive antennas, the MIMO radar has been shown to get higher resolution than that of a phased array radar using the same number of physical antenna elements. "
[Show abstract][Hide abstract] ABSTRACT: We propose a new algorithm to suppress the jammer signals and estimate the direction of arrival (DOA) of the signal of interest (SOI) for collocated MIMO radar by using the matrix pencil method (MPM) and the generalized likelihood ratio test (GLRT). The conventional GLRT divides the visible region into small angle samples, suppresses the jammer signals at each angle sample, and then estimates the DOA of the SOI. In the proposed algorithm, we extract the eigenvalues of received signals regardless of the SOI and jammer by using the MPM, which contain the information of the DOA of SOIs or jammers. Then, in order to suppress the jammers, we apply the GLRT to the extracted DOAs instead of to the entire visible region. By applying the MPM again to the received signals in which the jammer signals are suppressed, we can estimate the DOAs of the SOI. Since the proposed algorithm does not depend on the number of angle samples, it shows fast and accurate results regardless of the angle resolution. In order to verify the proposed algorithm, we compared the results with the results of the conventional GLRT and show the computing time.
International Journal of Antennas and Propagation 02/2015; 2015:1-8. DOI:10.1155/2015/802471 · 0.66 Impact Factor
"This general asymptotic case is relevant to real-world applications. As an example, by properly employing the waveform diversity in multiple-input, multiple-output (MIMO) radar   , we can obtain a virtual array with an extended aperture in which the number of antennas is considerably increased, probably close to or even larger than the number of snapshots. However, it has been pointed out in  that the general asymptotic case is able to provide a more accurate description for practical scenarios in which the number of snapshots and the number of antennas are finite with comparable values. "
[Show abstract][Hide abstract] ABSTRACT: It is interesting to determine the number of signals impinging upon a large array with small samples. We tackle this problem by using linear shrinkage coefficients of signal and noise subspaces, ending up with two shrinkage coefficient???based detectors (SCDs) for source enumeration. It is proved that the noise shrinkage coefficients are asymptotically Gaussian distributed as the number of antennas and number of samples tend to infinity at the same rate. Moreover, the noise shrinkage coefficients almost surely converge to one while the signal shrinkage coefficients are almost surely less than one as m,n???,∞ and m/n???c.With these properties, the threshold-like and heuristic SCD algorithms for source number detection are devised. Simulation results are included to illustrate their effectiveness.
IEEE Transactions on Aerospace and Electronic Systems 01/2015; 51(1):1-14. DOI:10.1109/TAES.2014.130579 · 1.76 Impact Factor
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