Principles of Modern Radar - Volume 2 1891121537

474 CHAPTER 10 Clutter Suppression Using Space-Time Adaptive ProcessingFIGURE 10-10Clutter-plus-noisePSD.800Power Spectral Density0−5600−10Doppler Frequency (Hz)4002000−200−400−15−20−25−30−35dB−600−40−800−45−80 −60 −40 −20 0 20 40 60 80Angle (Degrees)Figure 10-10 shows a typical clutter-plus-noise PSD; the clutter ridge is apparent inthe figure. We simulate the data giving rise to Figure 10-10 using the MCARM parametersgiven in [12] with minor modification. Salient parameters include 1 GHz transmitfrequency, 1 MHz waveform bandwidth, 1.33 m × 0.97 m sidelooking receive array, sixdegrees of crab (yaw), 22 spatial channels (M = 22) in an 11-over-11 configuration,32 pulses (N = 32) in the CPI, and a PRF of 1984 Hz.The PSD in Figure 10-10 shows that the clutter power occupies only a portion ofthe overall two-dimensional spectrum. Additionally, the portion of the occupied spectrumexhibits a distinct angle-Doppler coupling. Detecting moving target signals competingwith the clutter returns is the primary goal of the radar signal processor. Exploiting thenotion that a moving target generally possesses an angle-Doppler response distinct fromthat of stationary clutter is the goal of the space-time filter. Some space-time filter designsyield better performance than others. In subsequent sections we first discuss genericspace-time processing as a linear filtering operation applied to space-time snapshots froma given range, next consider critical MTI performance metrics, and then develop the optimalspace-time filter providing the best possible detection performance for a linear filter.Consequently, we define the STAP as a data-domain implementation of the space-timeoptimal filter.10.4 SPACE-TIME PROCESSINGFigure 10-11 shows a generic space-time processing chain. After vectorizing the datamatrix X k ∈ C M×N to generate the snapshot x k ∈ C MN×1 (i.e., x k = vec (X k )), theprocessor applies the space-time weight vector w k ∈ C MN×1 , thereby yielding the scalaroutputy k = w H k x k (10.51)