MIMO radar is a new concept in which radar employs multiple waveforms to improve its performance. Previously, a transmit beamforming method was proposed for MIMO radars. This method allows optimization of the beampattern by altering the cross-correlation matrix of the transmitted waveforms. The optimization is based on minimization of a cost function, but the use of numerical methods in the algorithm leads to high computational complexity. Here we propose a new cost function for the beampattern optimization. For linear arrays and typical beampatterns, this cost function can be evaluated in closed form, thus reducing the computational complexity considerably. Simulation examples demonstrate that the proposed cost function also leads to faster convergence and lower approximation error.

MIMO radars use multiple waveforms simultaneously to improve performance. A beamforming method that exploits this waveform diversity has been proposed previously. This method works by optimizing the covariance matrix of the waveforms to obtain an approximation of a desired beampattern. The previous method uses gradient descent to optimize the beampattern with the constraint on the power of each antenna element. We show how this method can be extended to obtain rank-deficient covariance matrices and also to handle the total power constraint. The conjugate gradient method is used in addition to the gradient descent.In this paper, we also propose converting the constrained beampattern optimization problem into an unconstrained one. This can be done by using the method of Lagrange multipliers, but also removing all constraints and then scaling the result so that the total power constraint is satisfied. Using this approach, the beampattern optimization can be written as a least squares problem.

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The presence of nonmeteorological radar signals, such as sea clutter, birds, and chaff, is a continuous challenge for meteorological services in different regions. In this paper, we assign membership functions to these signals using spectral decompositions of copolar correlation coefficient, differential reflectivity, and differential phase. Additionally, we apply the dualpolarization spectral decomposition technique to identify and suppress nonmeteorological echoes present in radar observations. The performance of the polarimetric spectral filter is illustrated in observations from the C-band Helsinki University Kumpula radar. The results show that the spectral polarimetric filter may be a suitable solution for the mitigation of these nonmeteorological signals.