Rapidly Adaptive Intelligent Radar (RAIR)

Period of Performance: 03/15/2001 - 11/15/2001


Phase 1 SBIR

Recipient Firm

C & P Technologies, Inc.
317 Harrington Avenue Suites 9 - 10
Closter, NJ 07624
Principal Investigator


The sample support problem in space-time adaptive processing (STAP) applications arises from the requirement to adapt many spatial and temporal degrees-of-freedom (DOF) to a changing interference environment that includes clutter and jammers. Often, in heterogeneous overland strong clutter environments, the available wide sense stationary sample support is severely limited to preclude the direct implementation of the sample matrix inverse (SMI) approach. In this proposal we outline several approaches to address the sample support problem: (i) Generalized forward/backward sub-aperture-subspace smoothing techniques to reduce the number of data samples in estimating the sample covariance matrices (ii) Projection methods using alternating projections or relaxed projection operators onto desired convex sets to retain the a-priori known structure of the covariance matrix. Our initial analysis shows that by combining these approaches with eigenbased techniques, it is possible to reduce significantly the data samples required in non-stationary environment and consequently achieve superior target detection. In fact, multiplicative improvement in data reduction compared to direct eigen-based methods can be obtained at the expense of negligible loss in space-time aperture. Phase I efforts will concentrate on obtaining the improvement in performance by combining these methods and will be supported by analytical study as well as simulation results.The improved adaptive transmit signal design can be critically important for detection of extended targets including non-military applications such as monitoring drug trafficking activities and location identification of cellular systems using limited number of data samples, as well as high resolution SAR technology.