NULLING CLUTTER?

What is it about?

To detect moving targets in a distributed multiple-input multiple output (MIMO) radars, it is preferable to null the clutter rather than whiten the clutter. This is mainly because clutter nulling approaches do not require range training data to form a sample covariance estimate of clutter in each transmit-receive pair, especially in the distributed MIMO radars with non-homogeneous clutter. Moreover, geometry diversity of the distributed MIMO helps improve moving target detection since for a given target velocity, different transmit-receive pairs produce different Doppler frequencies that are less likely to be all small and reside in the clutter nulling region. Based on these facts, the authors consider a subspace model for clutter and derive a uniformly most powerful invariant (UMPI) detector as an optimum invariant test. In this case, analytical expressions for calculating the false alarm and detection probabilities for Swerling 0 and Swerling 1 target models are derived in the closed-forms. Moreover, theoretical and numerical analyses of the proposed UMPI test are represented for several scenarios. More importantly, the simulation results show that the proposed subspace-based UMPI detector can attain the predetermined false alarm probability and superior detection performance in the presence of correlated clutter. Finally, authors consider a situation in which perfect waveform separations at the local receivers are no longer valid, and see the detection performance degradation of the proposed optimal detector designed for ideal waveform separations.

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Why is it important?

1- For the moving target detection in the distributed MIMO radars, a UMPI composite hypothesis test is derived. 2-Closed-form expressions are developed for the false alarm and detection probability of the proposed UMPI test. In addition, we obtain the performance of the proposed UMPI test in the presence of a target with Swerling 1 target model over different transmit-receive pairs in the closed-form. 3-It is shown that the distributed MIMO gain is comprised of three components: non-coherent gain, geometry gain and spatial diversity gain. Hence, the concepts of the geometry and spatial diversity gains are discussed in detail using analysis and simulations.

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