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For colocated multiple-input multiple-output (MIMO) radar target detection in colored noise, we propose two adaptive detectors according to the Rao andWald test criteria. These detectors do not need training data and possess constant false alarm rate properties. We investigate the manner in which they work. From a detection viewpoint, we show that there is no need for 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 for both deterministic and random signals. Numerical examples are provided to compare the detection performance of the Rao andWald tests with an existing detector. [ABSTRACT FROM PUBLISHER]

Range-dependent clutter suppression is a challenging problem in nonsidelooking, bistatic, and conformal antenna array airborne radar, especially in the presence of range ambiguity. Superimposed stepped frequency (SSF) radar employs a small frequency increment across the stepped signals in each pulse, which introduces a dimension related to slant range. In this article, an airborne radar framework is established, which takes SSF signal as the transmit waveform. Thus, a two-stage adaptive clutter suppression method is proposed, which utilizes the degrees of freedom in range, space, and time domains provided by airborne SSF radar. In the first stage, the secondary range dependence compensation approach is adopted to distinguish the clutter of each range region in the carrier frequency domain. Then, a target-free covariance matrix is estimated by the compensated data, and the covariance matrix is used for the range-ambiguous clutter separation in the carrier frequency domain. Thus, the space-time snapshot of each range region can be extracted. In the second stage, a clutter segmentation processing method is devised for residual clutter suppression. For the near-range unambiguous clutter, the traditional clutter compensation approach is applied to further align the spectrum distribution of clutter, while the conventional space-time adaptive processing is utilized to suppress far-range clutter directly. Since the proposed method takes full advantage of the range dimension and the two stages of adaptive processing are data-dependent, excellent clutter suppression performance can be obtained. The proposed method can also extract the real range of the target since each range region is processed separately. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Compared with conventional two-dimensional space-time adaptive processing (2D-STAP) methods, the elevation-azimuth-Doppler three-dimensional space-time adaptive processing (3D-STAP) method has the advantage of suppressing nonstationary clutter. Thereby, it is suitable for nonsidelooking airborne radar (non-SLAR) applications. However, its huge training data requirements and computational load are often beyond radar's ability in practical clutter environments. In this correspondence, we develop a simple but efficient reduced-degree-of-freedom (DOF) 3D-STAP method that significantly reduces the required training data and the computational complexity while maintaining the suboptimal clutter suppression performance. The proposed method transforms the planar array data into linear array data in azimuth and in elevation, respectively, thereby beamforming an equivalent cross-shape array prior to STAP. In consequence, only a few spatial DOFs, including azimuth and elevation dimension, are used for STAP directly resulting in the potential advantage for nonstationary clutter suppression and drastically reducing training data requirements and computational load. Furthermore, the clutter rank estimation rules of the planar array and the transformed cross-shape array are derived, and the required elevation DOFs of the proposed method are further discussed in detail. Simulations for clutter suppression of non-SLAR show that the proposed STAP method outperforms the state-of-the-art 3D-STAP method in terms of convergence and computational complexity. [ABSTRACT FROM AUTHOR]

The recognition of the interference environment for adaptive radar detection is addressed in this article. Typically, detectors are designed in one specific scenario which may not be appropriate for the varying interference environment, especially for the airborne and space-based radar system. In this article, the considered recognition task is cast in terms of multiple hypothesis tests and the theory of model order selection (MOS) techniques are exploited to devise suitable decision rules. The interference environments are divided into homogeneity, partial homogeneity, and spherically invariant random process. Three MOS techniques, namely, the Akaike information criterion (AIC), generalized information criterion, and corrected AIC, are adopted. At the analysis stage, illustrating examples for the influence of the environment model parameters on the recognition accuracy of the MOS rules are presented. Numerical experiments show the AIC rule has the most robust recognition performance. [ABSTRACT FROM AUTHOR]