Keith F. McDonald

Analysis of STAP algorithms for cases with mismatched steering and clutter statistics

In the majority of adaptive radar detection algorithms, the covariance matrix for the clutter-plus-noise is estimated using samples taken from range cells surrounding the test cell. In a nonhomogeneous environment, this can lead to a mismatch between the mean of the estimated covariance matrix and the true covariance matrix for the test cell. Further, an inaccurate target steering vector may also be employed. Closed-form expressions are provided, which give the performance for such cases when any of a set of popular space-time adaptive processing (STAP) algorithms are used. The expressions are exact for some interesting cases. For some other cases, it is demonstrated that the expressions provide good approximations to the exact performance. To simplify the analysis, the samples from the surrounding range cells are assumed to be independent and identically distributed, and these samples are assumed to be independent from the sample taken from the test cell. A small number of important parameters describe which types of mismatches are important and which are not. Monte Carlo simulations, which closely match the predictions of our equations, are included.

Exact performance of STAP algorithms with mismatched steering and clutter statistics

Adaptive algorithms for receivers employing antenna arrays have received significant attention for radar systems applications. In the majority of these algorithms, the covariance matrix for the clutter-plus-noise is characterized by using samples taken from range cells surrounding the test cell. If the underlying covariance matrix of the test cell is different from the average covariance matrix of the surrounding range cells, significant performance degradation may result. Exact expressions for performance are derived for such cases, when any of a set of popular space-time adaptive processing (STAP) algorithms are used. Numerical evaluation of these expressions illustrates how variations in the parameters of these equations affect probability of detection and probability of false alarm. The equations are utilized to determine an upper bound an the performance of this class of STAP algorithms.

A statistical and physical mechanisms-based interference and noise model for array observations

A statistical noise model is developed from mathematical modeling of the physical mechanisms that generate noise in communication receivers employing antenna arrays. Such models have been lacking for cases where the antenna observations may be statistically dependent from antenna to antenna. The model is developed by generalizing an approach for single antenna cases suggested by Middleton (1967, 1974, 1976, 1977). The model derived here is applicable to a wide variety of physical situations. The focus is primarily on problems defined by Middleton to be Class A interference. The number of noise sources in a small region of space is assumed to be Poisson distributed, and the emission times are assumed to be uniformly distributed over a long time interval. Finally, an additive Gaussian background component is included to represent the thermal noise that is always present in real receivers.

Track maintenance and positional estimation via ground moving target indicator and geolocation data fusion

In modern tactical environments, information from a variety of sensors may be simultaneously utilized to improve target detection and tracking procedures. Towards this goal, two data fusion algorithms are developed that implement such processing. First, a GMTI (ground moving target indicator)/geolocation algorithm is derived to assist with track maintenance during periods of GMTI data blackout. Next, a geolocation estimator is presented which fuses time difference of arrival (TDOA), frequency difference of arrival (FDOA), and angle of arrival (AOA) measurements. These algorithms lead to improved positional estimation, track accuracy, and track maintenance. They also potentially reduce the number of platforms required to successfully locate and track targets.

Lessons learned through the implementation of Space-Time Adaptive Processing algorithms for GPS reception in jammed environments

The use of Space-Time Adaptive Processing (STAP) algorithms to mitigate the detrimental effects of jamming upon GPS receivers has gained significant interest in the signal processing community. Sufficient spatial and temporal degrees of freedom allow these algorithms to position nulls in the space-time locations of jammers while concurrently allowing the reception of the GPS signal. Unfortunately, algorithm design assumptions tend to be optimistic and can prove to be unrealistic and inaccurate. Additionally, much of the analysis, development, and evaluation of these digital beamforming algorithms have been accomplished with computer-generated simulations that may not accurately address mismatches between the assumed signal model and the actual environment. In recognition of these shortcomings, MITRE has recently processed recorded data containing both the GPS signal and jamming that can be used to evaluate current and future generation antenna array electronics and signal processing algorithms. In this paper, practical issues encountered during the analysis of the recorded data are presented. These issues must be addressed to ensure the successful evaluation of STAP algorithms for GPS. Techniques are developed to accurately calculate jammer mitigation performance as well as the resulting satellite availability. A methodology for beampattern visualization is also described. We conclude that careful consideration of data collection methods and conditions is required to adequately assess adaptive algorithm performance.

Insights into Jammer Mitigation Via Space-Time Adaptive Processing

Abstract—In recent years, the military and civilian populations have become increasingly reliant upon the Global Positioning System (GPS) for knowledge of geographical location and timing information for synchronous operations, asset deployment, and communications. GPS receivers have been shown to operate with accuracy in benign environments and enjoy a wide variety of modern day applications. The high level of military and civilian dependence upon GPS makes it a likely electronic warfare (EW) target. This effort concerns the mitigation of a particular type of EW known as jamming. Jamming is the direction of electromagnetic energy towards a GPS receiver that causes degraded GPS signal reception and navigational performance. The focus of this work is on a particular jamming mitigation technique known as space-time adaptive processing (STAP). STAP is a signal processing method which combines data from multiple antenna elements to mitigate jamming while concurrently facilitating the reception of GPS signals.