Michael L. Picciolo

Airborne/spacebased radar STAP using a structured covariance matrix

It is shown that partial information about the airborne/spacebased (A/S) clutter covariance matrix (CCM) can be used effectively to significantly enhance the convergence performance of a block-processed space/time adaptive processor (STAP) in a clutter and jamming environment. The partial knowledge of the CCM is based upon the simplified general clutter model (GCM) which has been developed by the airborne radar community. A priori knowledge of parameters which should be readily measurable (but not necessarily accurate) by the radar platform associated with this model is assumed. The GCM generates an assumed CCM. The assumed CCM along with exact knowledge of the thermal noise covariance matrix is used to form a maximum likelihood estimate (MLE) of the unknown interference covariance matrix which is used by the STAP. The new algorithm that employs the a priori clutter and thermal noise covariance information is evaluated using two clutter models: 1) a mismatched GCM, and 2) the high-fidelity Research Laboratory STAP clutter model. For both clutter models, the new algorithm performed significantly better (i.e., converged faster) than the sample matrix inversion (SMI) and fast maximum likelihood (FML) STAP algorithms, the latter of which uses only information about the thermal noise covariance matrix.

Robust adaptive matched filtering using the FRACTA algorithm

An effective method is developed for selecting sample snapshots for the training data used to compute the adaptive weights for an adaptive match filter (AMF); specifically a space/time adaptive processing (STAP) airborne radar configuration is considered. In addition, a new systematic robust adaptive algorithm is presented and evaluated against interference scenarios consisting of jamming, nonhomogeneous airborne clutter (generated by the Research Laboratory STAP (RLSTAP) or knowledge-aided sensor signal processing and expert reasoning (KASSPER) high-fidelity clutter models or using the multi-channel airborne radar measurement (MCARM) clutter data base), internal system noise, and outliers (which could take the form of targets themselves). The new algorithm arises from empirical studies of several combinations of performance metrics and processing configurations. For culling the training data, the generalized inner product (GIP) and adaptive power residue (APR) are examined. In addition two types of data processing methods are considered and evaluated: sliding window processing (SWP) and concurrent block processing (CBP). For SWP, a distinct adaptive weight is calculated for each cell-under-test (CUT) in a contiguous set of range cells. For one configuration of CBP, two distinct weights are calculated for a contiguous set of CUTs. For the CBP, the CUTs are in the initial training data and there are no guard cells associated with the CUT as there would be for SWP. Initial studies indicate that the combination of using the fast maximum likelihood (FML) algorithm, reiterative censoring, the APR metric, CBP, the two-weight method, and the adaptive coherence estimation (ACE) metric (we call this the FRACTA algorithm) provides a basis for effective detection of targets in nonhomogeneous interference. For the KASSPER data, FRACTA detects 154 out of 268 targets with one false alarm (P/sub F//spl ap/3/spl times/10/sup -5/) whereas the FML algorithm with SWP detects 11 with one false alarm. The clarvoyant processor (where each range cells covariance matrix is known) detects 192 targets with one false alarm.

Median cascaded canceller using reiterative processing

A novel, robust adaptive processor is introduced, based on reiterative application of the median cascaded canceller (MCC). The MCC, though a highly robust adaptive processor, has a convergence rate that generally is dependent on the effective rank of the interference-plus-noise covariance matrix. The reiterative median cascaded canceller (RMCC) introduced here exhibits the highly desirable combination of 1) convergence-robustness to outliers/targets in adaptive weight training data, like the MCC, and 2) fast convergence performance independent of the interference-plus-noise covariance matrix and at a rate commensurate with the sample matrix inversion (SMI) algorithm, unlike the MCC. Both simulated data as well as measured airborne radar data from the MCARM space-time adaptive processing (STAP) database are used to show performance enhancements. It is concluded that the RMCC adaptive processor is a highly robust replacement for the SMI adaptive processor and all its equivalent implementations.

Band-partitioned sidelobe canceller for a wideband radar

A generic electronic counter-counter measure (ECCM) system consisting of band-partitioned (BP) sidelobe canceller (SLC) is investigated for wideband radar. This paper describes trade-off studies performed on the BP digital SLC and identifies techniques and parameters that are capable of providing improved cancellation performance.

An adaptive multistage median cascaded canceller

A multistage median cascaded canceller is introduced as a hybrid combination of the multistage Wiener filter (MWF) and the recently introduced median cascaded canceller (MCC). The hybrid processor is configured for radar space-time adaptive processing and tested using measured airborne radar data from the MCARM database as well as using simulated airborne radar clutter and jamming data. Results show the hybrid processor maintains a high signal-to-interference-plus-noise ratio and exhibits a mixture of useful characteristics from the two separate processors. Optimal rank reduction capability from the MWF portion is retained and robustness to targets/outliers and non-stationary data are attributed to the MCC portion. In addition, valuable synergistic features are discovered, such as an order 15 dB lower average azimuth-Doppler sidelobe level in the adapted pattern, compared to the MWF.

Fast converging robust adaptive arrays

A pseudo-median canceller is introduced as the canonical processor of a robust adaptive array method which significantly reduces the deleterious effects of non-Gaussian, real-world noise and interference (outliers) on typical array performance metrics such as (normalized) output noise power residue and signal-to-interference-plus-noise ratio (SINR). In addition, the proposed structure offers natural protection against signal cancellation, or equivalently, against an increase in the output noise power residue, when weight-training data contains desired signal components. The convergence rate is shown to be commensurate with sample matrix inversion (SMR) methods for Gaussian noise and interference, and convergence is essentially unaffected when outliers are added to the Gaussian weight-training data, while non-robust SMI methods slow significantly under the same circumstances.

Reiterative median cascaded canceler for robust adaptive array processing

A new robust adaptive processor based on reiterative application of the median cascaded canceler (MCC) is presented and called the reiterative median cascaded canceler (RMCC). It is shown that the RMCC processor is a robust replacement for the sample matrix inversion (SMI) adaptive processor and for its equivalent implementations. The MCC, though a robust adaptive processor, has a convergence rate that is dependent on the rank of the input interference-plus-noise covariance matrix for a given number of adaptive degrees of freedom (DOF), N. In contrast, the RMCC, using identical training data as the MCC, exhibits the highly desirable combination of: 1) convergence-robustness to outliers/targets in adaptive weight training data, like the MCC, and 2) fast convergence performance that is independent of the input interference-plus-noise covariance matrix, unlike the MCC. For a number of representative examples, the RMCC is shown to converge using ~ 2.8N samples for any interference rank value as compared with ~ 2N samples for the SMI algorithm. However, the SMI algorithm requires considerably more samples to converge in the presence of outliers/targets, whereas the RMCC does not. Both simulated data as well as measured airborne radar data from the multichannel airborne radar measurements (MCARM) space-time adaptive processing (STAP) database are used to illustrate performance improvements over SMI methods.

Robust STAP using reiterative censoring

Effective methods are developed for selecting sample snapshots for the training data used to compute the adaptive weights for an adaptive match filter; specifically a space/time adaptive processing (STAP) airborne radar configuration is considered. Several new robust adaptive algorithms are presented and evaluated against interference scenarios consisting of jamming, nonhomogeneous airborne clutter (generated by the RLSTAP model), internal system noise, and outliers (which could take the form of targets themselves). These algorithms use either the generalized inner product (GIP) or the adaptive power residue (APR) metrics in reiterative fashion for culling the training data.

Robust, Reduced Rank, Loaded Reiterative Median Cascaded Canceller

A robust, fast-converging, reduced-rank adaptive processor called the loaded reiterative median cascaded canceller (LRMCC) is introduced. The LRMCC exhibits the highly desirable combination of 1) convergence-robustness to outliers/targets/nonstationary data in adaptive weight training data, and 2) fast convergence at a rate commensurate with reduced-rank algorithms. Simulated jamming data as well as measured airborne radar data from the MCARM space-time adaptive processing (STAP) database are used to show performance enhancements. Performance is compared with the fast maximum likelihood (FML) and sample matrix inversion (SMI) algorithms. It is demonstrated that the LRMCC is easily implemented and is a highly robust replacement for existing reduced-rank adaptive processors, exhibiting superior performance in nonideal measured data environments.

Multistage anti-spoof GPS interference correlator (MAGIC)

With the proliferation of GPS jammers and spoofers, commercial GPS receivers need robust anti-jam (AJ)/anti-spoofing (AS) solutions to ensure the future integrity of precision navigation and timing (PNT) solutions. The AJ/AS challenge is perhaps greatest when confronting the size, weight, power (SWaP) constraints of commercial applications that typically have GPS receivers with single antenna configurations and limited intrinsic AJ/AS capability. This article focuses on a newly developed single antenna AJ/AS solution known as MAGIC. Our approach applies a reduced-rank MMSE based C/A code correlator for single antenna GPS receivers that replaces a standard C/A code correlator for enhanced AJ/AS capability.