4.1 A Lower Confidence Limit for the Number of Signals
By: Pinyuen Chen and Michael C. Wicks
Air Force Research Laboratory | Abstract: We propose a multi-step procedure for constructing a lower confidence limit for the number of signals present. We derive the probability of a correct estimation, P(CE), and the least favorable configuration (LFC) for our procedure. Under LFC, the P(CE) attains its minimum over the parameter space of all eigenvalues. Therefore a minimum sample size can be determined in order to implement our procedure with a guaranteed probability requirement. |
4.2 Block Toeplitz with Toeplitz Block Covariance Matrix for Space-Time Adaptive Processing
By: Youming Li and Chee Hoo Cheong
DSO National Laboratories, Singapore | Abstract: Interference (Jamming plus clutter) covariance matrix estimations and computational complexity are two main concerns in Space-Time Adaptive Processing (STAP). This paper deals with the two problems by exploring structured covariance matrix. First, a Conjugate Gradient Iterative Algorithm (CGIA) with reduced computational complexity is presented, which is based on FFT by using Block Toeplitz with Toeplitz Block (BTTB) structure of interference covariance matrix. To ensure the convergence of CGIA, a BTTB covariance matrix approximation is also proposed. In this approximation, we find two matrices whose kronecker product is as close as possible to the sample covariance matrix. Based on the approximated BTTB matrix, the corresponding STAP algorithm provides robust performance in limited sample support and in the presence of system errors. |
4.3 Short CPI STAP for Airborne Radar
By: Peter Parker
MIT Lincoln Laboratory | Abstract: Circular, electronically scanned antenna arrays are currently being considered for use in airborne surveillance systems. These arrays are capable of pointing a beam in any direction at any time. This allows the radar to perform both 360o search and high update rate track modes simultaneously. To manage the radar's resources efficiently, shorter coherent processing intervals (CPs) should be used for nearby or large targets. However, shortening the CPI effects STAP performance by increasing the amount of Doppler space that is lost to the clutter null. This paper analyzes the useable Doppler space performance of short CPI STAP. It is shown that CPIs as short as 6 pulses can be used to track high velocity targets. Short CPI STAP performance is analyzed theoretically and by simulation. |
4.4 Theoretical Analysis of Small Sample Size Behaviour of Eigenvector Projection Technique Applied to STAP
By: Bhashyam Balaji and Christoph H. Gierull
Defence Research and Development Canada | Abstract: In this paper, we investigate finite sample size performance of the Eigenvector projection method when applied to space-time adaptive processing (STAP). A theoretical analysis of the expectation of the signal to interference plus noise ratio (SINR) for the Eigenvector projection technique is presented. This gives insight into the problem of determining the optimum choice of the projected clutter subspace. An estimator of the sample-size dependent optimum subspace dimension, which can be significantly smaller than clutter rank, is also presented. This result, combined with near-optimal Eigenvector-free projection techniques with minimal sample support, helps in reducing the computational burden significantly. |
4.5 Application of Fast Projection Technique without Eigenanalysis to STAP
By: Christoph H. Gierull and Bhashyam Balaji
Defence Research and Development Canada | Abstract: In ground surveillance from an airborne or space-based radar it is desirable to be able to detect small and slowly moving targets, within severe ground clutter. For operational moving target indication (MTI) systems the clutter filter coefficients have to be updated frequently due to rapidly changing interference environment. This paper examines the small sample size performance of different fast fully adaptive space-time processors (STAP) and compares it to the optimum-detector performance. These recently proposed techniques, named Matrix Transformation based Projection (MTP) and Lean Matrix Inversion (LMI), were originally developed to provide fast jammer suppression in phased array radars with many elements. For this application they have been proven to operate with near-optimum performance, yet with a computational expense extremely reduced from that of the optimum detector in most practical cases. The investigation herein focuses on the performance achieved when only a very few data samples are available to adapt (update) the clutter filter coefficient. |
4.6 Colored Diagonal Loading
By: John D. Hiemstra
Science Applications International Corp. | Abstract: In this paper we develop a beamforming technique called colored diagonal loading. This technique is a generalization of diagonal loading in which the covariance matrix is augmented with a scaled version of a colored matrix as opposed to using the identity matrix as with conventional diagonal loading. Thus as the loading is increased, the beam pattern increasingly takes on the form of a desired quiescent pattern as opposed to that of a conventional (high sidelobe) pattern. The attractiveness of this technique is that it retains the robustness and simple formulation of diagonal loading while allowing insertion of additional quiescent structure. We compare this technique is to conventional diagonal loading and to other quiescent pattern techniques. |
4.7 GMTI STAP in Target-Rich Environments: Site-Specific Analysis
By: Jameson S. Bergin and Paul M. Techau
Information Systems Laboratories, Inc.
and: William L. Melvin
Georgia Tech Research Institute
and: Joseph R. Guerci
DARPA/SPO | Abstract: In this paper we address the problem of training data corruption in space-time adaptive processing (STAP) for ground moving target indication (GMTI) radar scenarios characterized by high densities of ground targets. A site-specific clutter simulation is used to demonstrate the impact that target signals in the training data have on STAP performance. Measured MCARM data results are presented that reveal similar performance trends as those observed in the simulations. A strategy for mitigating the deleterious effects of targets in the training data using a priori knowledge of the radar environment (e.g., locations of roads) to edit the training data is presented. |
4.8 Projection Approach for STAP
By: S. Unnikrishna Pillai
Polytechnic University
and: S. Radhakrishnan Pillai
C & P Technologies, Inc. | Abstract: 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 paper we outline an approach to address the sample support problem by utilizing projection methods - 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 eigenbased methods can be obtained at the expense of negligible loss in space-time aperture. |
4.9 A New Constrained Joint-Domain Localized Approach for Airborne Radar
By: Braham Himed, Michael C. Wicks and Peter Zulch
Air Force Research Laboratory | Abstract: In this paper, critical issues associated with the application of multi-dimensional adaptive filtering including Space-Time Adaptive Processing (STAP) to real-world radar systems are considered. In particular, the design of transform domain localized techniques are examined from the perspective of receiver beam position and Doppler filter selection relative to mainlobe clutter as well as target returns. In particular, asymmetry in the selection of auxiliary beams and the effects of spatial tapering are shown to offer dramatic improvements in signal to interference ratio for targets with low Doppler. |
4.10 Elevation Interferometric STAP Using a Thinned Planar Array
By: Todd B. Hale, Michael A. Temple, John F. Raquet, and Mark E. Oxley
Air Force Institute of Technology
and: Michael C. Wicks
Air Force Research Laboratory | Abstract: The research applies Space-Time Adaptive Processing (STAP) techniques to a pseudo-circular array generated by selectively thinning a rectangular array. A hybrid approach incorporating elevation interferometry and STAP techniques is used. Results show the thinned 16- element pseudo-circular array offers significant detection performance improvement over the baseline Factored Time-Space (FTS) technique operating on a linear array, e.g., an 8-element horizontal linear array. Results are demonstrated for cases with and without range ambiguous clutter. This performance level is achieved using a factor of M less sample support than required for full adaptivity where M represents the number of pulses within a coherent processing interval. |