Generalized forward/backward subaperture smoothing techniques for sample starved STAP

Abstract

A major issue in space-time adaptive processing (STALP) for airborne moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the interference covariance matrix leads to severe rank deficiency, thereby precluding STAP beamforming based on the direct sample matrix inversion (SMI) method. The intrinsic mterference subspace removal (ISR) technique, which is a computationally useful form of diagonally loaded SIMI method, can handle this case, although the performance is poor in low sample situations. In this context, new subarray-subpulse schemes using forward and backward data vectors are introduced to overcome the data deficiency problem. It is shown here that multiplicative improvement in data samples can be obtained at the expense of negligible loss in space-time aperture of the steering vector.

New ECCM Techniques Against Noiselike and/or Coherent Interferers

“…where f 1 (z; M 1 , H 1,1 |α) is the conditional pdf of z given α. Taking the negative logarithm, problem (27) is equivalent to…”

“…As an illustration of this fact, consider those decision rules devised assuming that the ICM is centrohermitian. These algorithms allow us to reduce the number of training samples required for the ICM estimation [16]- [23], [27], [28] by almost a half while maintaining a satisfactory detection performance. Further examples are provided in [24], [25], where it is shown that the spectral symmetry of the clutter can be used for instance to obtain gains of about 3 dB (in SINR, namely Signal-to-Interference plus Noise Ratio), for a Probability of Detection P d = 0.9 and Probability of False Alarm P f a = 10 −4 , in comparison to conventional detectors.…”

Adaptive Detection of Point-Like Targets in Spectrally Symmetric Interference

“…It is important to highlight that the persymmetric structure is not limited to linear arrays but it can be found in different geometries such as standard rectangular arrays, uniform cylindrical arrays (with an even number of elements), and some standard exagonal arrays [16]. Several approaches, relying on the persymmetry, have been developed to achieve improved detection performances in training-limited scenarios; just to give some examples, see [17]- [24].…”

Angle-Doppler processing using sparse regularization

“…Joint spatial-temporal processing addresses this issue [18] and many algorithms for space-time adaptive processing (STAP) have been developed [11]. Of particular interest are algorithms requiring minimal data for the estimation of covariance matrices needed for STAP [12,16].…”