Multiple-input Multiple-output Radar Applications Research Articles

Fractal antenna arrays for MIMO radar applications

In this contribution, fractal antenna arrays are analyzed for their applicability in multiple-input multiple-output (MIMO) radars. Array geometries based on the Fudgeflake fractal and the Gosper island fractal are investigated. In addition, a concept for the combination of both fractals is shown in order to increase the flexibility concerning the number of transmitting and receiving antennas. The presented fractal MIMO concepts can be utilized in order to improve the angular resolution and to reduce the sidelobe level for a given number of transmitting and receiving antennas. It is shown that a fractal MIMO concept with 21 transmitting antennas and 21 receiving antennas improves the angular resolution to 4.6 degrees and reduces side lobe level by 3.1 dB compared to a MIMO configuration based on two linear arrays with the same number of antenna elements. In addition, the results are experimentally validated by broadband radar measurements.

Large Time-Bandwidth Product MIMO Radar Waveform Design Based on Chirp Rate Diversity

Multiple-input multiple-output (MIMO) radar waveform diversity design has received much attention, but most of the existing waveforms lack a large time-bandwidth product and are difficult to be implemented by real-life hardwares. In this paper, we propose a large time-bandwidth product waveform diversity design scheme based on chirp rate diversity for MIMO radar. The proposed scheme can generate a large number of pseudo-orthogonal waveforms that have large time-bandwidth product, constant modulus, no range-Doppler coupling target response, good orthogonality, and Doppler tolerance. In addition, the waveforms are orthogonal on both transmit and receive. The waveform performance is in-depth analyzed with the correlation and ambiguity functions. Furthermore, the corresponding frequency demodulation in the receiver is also discussed. Numerical results validate the superiorities of the designed waveforms in MIMO radar applications.

Minimum integrated sidelobe ratio filters for MIMO radar

By simultaneously transmitting multiple waveforms, a multiple-input, multiple-output (MIMO) radar retains transmit degrees of freedom, but the range-sidelobe performance will be degraded by the cross-correlation between waveforms. Techniques for providing low sidelobes are required for many MIMO radar applications such as MIMO synthetic aperture radar. This paper presents a new mismatched-filtering approach for designing optimal filters that minimize the integrated sidelobe ratio for MIMO radar. Unlike other approaches presented in the literature, this new method jointly applies pulse compression and spatial beamforming, which allows improved sidelobe control compared to techniques that apply these processing stages in a separable manner.

Iterative Adaptive Kronecker MIMO Radar Beamformer: Description and Convergence Analysis

We introduce an iterative procedure for design of adaptive KL-variate linear beamformers that are structured as the Kronecker product of K-variate (transmit) and L-variate (receive) beamformers. We focus on MIMO radar applications for scenarios where only joint transmit and receive adaptive beamforming can efficiently mitigate multi-mode propagated backscatter interference. This is because the direction-of-departure (DoD) on one interference mode, and the direction-of-arrival (DoA) on the other, coincide with those of a target, respectively. We introduce a Markov model for the adaptive iterative routine, specify its convergence condition, and derive final (stable) signal-to-interference-plus-noise ratio (SINR) performance characteristics. Simulation results demonstrate high accuracy of the analytical derivations. In addition, we demonstrate, that for the considered class of multiple-input multiple-output (MIMO) radar interference scenarios, the diagonally loaded sample matrix inversion (SMI) algorithm provides additional performance improvement and convergence rate for this iterative adaptive Kronecker beamformer.

Iterative Adaptive Approaches to MIMO Radar Imaging

Multiple-input multiple-output (MIMO) radar can achieve superior performance through waveform diversity over conventional phased-array radar systems. When a MIMO radar transmits orthogonal waveforms, the reflected signals from scatterers are linearly independent of each other. Therefore, adaptive receive filters, such as Capon and amplitude and phase estimation (APES) filters, can be directly employed in MIMO radar applications. High levels of noise and strong clutter, however, significantly worsen detection performance of the data-dependent beamformers due to a shortage of snapshots. The iterative adaptive approach (IAA), a nonparametric and user parameter-free weighted least-squares algorithm, was recently shown to offer improved resolution and interference rejection performance in several passive and active sensing applications. In this paper, we show how IAA can be extended to MIMO radar imaging, in both the negligible and nonnegligible intrapulse Doppler cases, and we also establish some theoretical convergence properties of IAA. In addition, we propose a regularized IAA algorithm, referred to as IAA-R, which can perform better than IAA by accounting for unrepresented additive noise terms in the signal model. Numerical examples are presented to demonstrate the superior performance of MIMO radar over single-input multiple-output (SIMO) radar, and further highlight the improved performance achieved with the proposed IAA-R method for target imaging.