Structured Covariance Matrix Estimation for the Range-Dependent Problem in STAP

X. Neyt, P. Druyts, M. Acheroy, and J.G. Verly (Belgium)


Bistatic radar, STAP, maximum likelihood, struc tured covariance matrix estimation, range dependence, Expectation-Maximization


We propose a method to compute an estimate of the clutter plus-noise covariance matrix in bistatic radar configura tions. The estimation is based on the computation of the clutter scattering coefficients based on a single data snap shot at each range using a model of the received signal. The covariance matrix of the data is modeled as a struc tured covariance matrix with the scattering coefficients as unknown parameters. The method is based on the compu tation of the maximum likelihood. We use the Expectation Maximization method as estimation benchmark. Since the problem is ill-posed, regularization is mandatory. This reg ularization is performed by spatial smoothing. The method we propose, unlike the Expectation Maximization, is not iterative and is thus less computationally demanding. The obtained covariance matrix estimate is used to compute the matched filter in order to perform target detec tion. The performance of the proposed estimation method is evaluated in terms of signal to interference-plus-noise ra tio (SINR) losses and is found to be almost indistinguish able from the performance of the clairvoyant case.

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