Target Detection and Parameter Estimation for MIMO Radar Systems

Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL
IEEE Transactions on Aerospace and Electronic Systems (Impact Factor: 1.76). 08/2008; 44(3):927 - 939. DOI: 10.1109/TAES.2008.4655353
Source: IEEE Xplore


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.

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    • "In light of (59) and the analysis in Section V.A, the GLRT in [30] is also CFAR. Moreover, the GLRT in (59) is statistically equivalent to the random variable f in (41). "
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    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
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    • "Subject to the current technical conditions, the monostatic colocated MIMO radar is the most practical MIMO radar system [6]. The possible advantages of the colocated MIMO radars have provided the motivation to explore their capability in various contexts such as target detection [7], target localization [5], target tracking [8], waveform design [9], [10], and antenna allocation [11]. Due to the unique structure of the colocated MIMO radar, various desired beam patterns can be generated by multiple colocated transmitters [9], [10], [12]. "
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    ABSTRACT: A colocated multiple-input multiple-output (MIMO) radar system has the ability to address multiple beam information. However, the simultaneous multibeam working mode has two finite working resources: the number of beams and the total transmit power of the multiple beams. In this scenario, a resource allocation strategy for the multibeam working mode with the task of tracking multiple targets is developed in this paper. The basis of our technique is to adjust the number of beams and their directions and the transmit power of each beam through feedback, with the purpose of improving the worst tracking performance among the multiple targets. The Bayesian Cramér–Rao lower bound (BCRLB) provides us with a lower bound on the estimated mean square error (MSE) of the target state. Hence, it is derived and utilized as an optimization criterion for the resource allocation scheme. We prove that the resulting resource optimization problem is nonconvex but can be reformulated as a set of convex problems. Therefore, optimal solutions can be obtained easily, which greatly aids real-time resource management. Numerical results show that the worst case tracking accuracy can be efficiently improved by the proposed simultaneous multibeam resource allocation (SMRA) algorithm.
    IEEE Transactions on Signal Processing 06/2015; 63(12):3110-3122. DOI:10.1109/TSP.2015.2417504 · 2.79 Impact Factor
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    • "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 [5] [9], 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 [10]. 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. "
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    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
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