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Radar tracking of aircraft targets at low elevation angles can be complicated by clutter from wind turbines, which we define to be clutter. In this paper, we study detection in a staring pulse-Doppler radar. By exploiting the second-order correlation structure of this wind-turbine clutter we derive a target detector that uses a sequence of adaptive coherence scores. The detector uses a multipulse coherence statistic consisting of an incoherent geometric average of coherently computed adaptive coherence scores. The detector compares favorably to a multipulse coherence detector that uses no modeling or estimation of the clutter. [ABSTRACT FROM PUBLISHER]

Sparse signal representation is used to coherently separate the main rotor blade components of a helicopter from the composite data of complex time series radar returns. Received signals of this type typically consist of returns from the rotating main and tail rotor blades, the helicopter body, and other residual components which may overlap in time and frequency, thus making it difficult for conventional time–frequency separation techniques to be applied for the signal separation. In the proposed algorithm, a sparse signal representation is applied with the use of tunable Q wavelet transform to construct the dictionary, and basis pursuit denoising is used for the signal reconstruction of the component of interest. The algorithm shows very effective separation of the main rotor blade component from the composite radar returns, which is demonstrated using both simulated and real radar data at X-band. [ABSTRACT FROM AUTHOR]