T.B. Hale

Ground moving target indication using knowledge based space time adaptive processing

Space-time adaptive processing (STAP) techniques promise to offer the best means to detect weak targets in severe dynamic interference scenarios. Traditionally, STAP techniques were developed for the detection of low RCS, high velocity airborne targets, well removed from main-beam clutter in Doppler. STAP algorithms are only now being used for ground moving target indication (GMTI) from an airborne reconnaissance platform. We present a practical approach to STAP incorporating three components: nonhomogeneity detection, statistical processing of measured data, and hybrid processing. This combined approach ties together previous research in different aspects of STAP into one algorithm. The algorithm is tested using measured data from the Multi-Channel Airborne Radar Measurements program with particular interest in ground moving target detection.

A two stage hybrid space-time adaptive processing algorithm

This research presents two new space-time adaptive processing (STAP) algorithms; a two-dimensional non-statistical method and a hybridisation of this approach with statistically based methods. The non-statistical algorithm developed here allows filtering of uncorrelated interference, such as discrete interferers, within the range cell of interest. However, the performance of these algorithms in homogeneous correlated interference scenarios is inherently inferior to traditional statistical STAP algorithms. The proposed hybrid algorithm alleviates this drawback by implementing a second stage of statistical adaptive processing. This paper illustrates the advantages of using a two stage adaptive process to combine the direct data domain and statistical algorithms. The work presented in this paper brings together two different aspects of STAP research: statistical and direct data domain processing. In doing so, this research fulfils an important need in the context of practical STAP processing.

Knowledge based adaptive processing for ground moving target indication

This paper presents a preliminary knowledge based approach to space-time adaptive processing (STAP) for ground moving target indication from an airborne platform. The KB-processor accounts for practical aspects of adaptive processing, including detection and processing of non-homogeneous data, appropriate selection of training data, and accounting for array effects such as mutual coupling and channel mismatch. In combining these hitherto separate STAP issues into a unified approach, this paper furthers the move of STAP from theory to practice. The KB-approach is tested using measured data from the multi-channel airborne radar measurements (MCARM) program.

Transform domain localized processing using measured steering vectors and non-homogeneity detection

This paper presents two transform domain non-homogeneity detectors (NHDs) that account for the non-ideal array and the non-homogeneous interference environment. Each of these effects has been accounted for separately before (Adve and Wicks 1998; and Chang 1997). However, this paper is the first attempt to incorporate both effects into a single STAP algorithm. The formulation developed for the joint domain localized algorithm is tested on measured data from the MCARM database. The example illustrates the effects in detection performance by considering both the non-ideal system and non-homogeneous interference scenario, individually and in combination. The results show that if only the non-homogeneous data is accounted for, the NHD might actually worsen the situation. Both system and interference scenario imperfections must therefore be accounted for.

SAR change detection MTI

This paper examines the theory, application, and results of using single-channel synthetic aperture radar (SAR) data with Moving Reference Processing (MRP) to focus and geolocate moving targets. Moving targets within a standard SAR imaging scene are defocused, displaced, or completely missing in the final image. Building on previous research at AFRL, the SAR-MRP method focuses and geolocates moving targets by reprocessing the SAR data to focus the movers rather than the stationary clutter. SAR change detection is used so that target detection and focusing is performed more robustly. In the cases where moving target returns possess the same range versus slow-time histories, a geolocation ambiguity results. This ambiguity can be resolved in a number of ways. This paper concludes by applying the SAR-MRP method to high-frequency radar measurements from persistent continuous-dwell SAR observations of a moving target.

Localized three-dimensional adaptive spatial-temporal processing for airborne radar

Radar space-time adaptive processing (STAP) techniques have classically focused on azimuth-Doppler adaptivity while placing minimal emphasis on elevation. Elevation adaptivity offers significant clutter suppression improvement, allowing further suppression of interference sources having identical Doppler and azimuth as the expected target. This work incorporates elevation adaptivity using two approaches: (1) a factored approach and (2) a joint domain approach, both greatly improving clutter suppression performance. The proposed concepts are validated using results based on simulated range ambiguous airborne radar data. Target detection improvements on the order of 8 dB and 10 dB (as compared to standard 2D-JDL processing) are demonstrated for the factored and joint domain approaches, respectively, using an 8 /spl times/ 8 non-uniform rectangular array.

3-dimensional STAP performance analysis using the cross-spectral metric

Research done in recent years has clearly demonstrated large improvements in clutter suppression and target detection by including elevation adaptivity, otherwise described as 3-dimensional (3D) STAP. The paper further quantifies the performance gains garnered by 3D STAP by fixing the degrees of freedom (DOF) and varying the array dimensions to include the equivalently sized linear array. The focus is placed on performance bounds established by matched filter and 3D cross-spectral metric (CSM) SINR curves generated with known covariances. The mathematical extension of the CSM from 2D to 3D is shown to be straightforward, thus allowing the CSM to serve as a partially adaptive performance bound for eigenvalue-selection based 3D STAP algorithms.

Performance improvement using interpulse pattern diversity with space-time adaptive processing

This work investigates the impact of interpulse (pulse-to-pulse) transmit pattern diversity on space-time adaptive processing (STAP) performance. It is shown that varying interpulse transmit characteristics within a coherent processing interval (CPI) can reshape the clutter power spectrum, resulting in an interference whitening effect. For conducting comparative analysis with non-adaptive transmit techniques, a commonly used clutter model is extended to effectively incorporate interpulse pattern diversity effects. The work shows promise for achieving better minimum discernable velocity (MDV) using phased array transmits weights derived from optimum STAP weights (known covariance). Pattern diversity effectively redistributes clutter energy away from the clutter ridge. For the unambiguous clutter case, the proposed adaptive transmit technique shows promise for improving MDV at the clutter ridge peak.

Ambiguity analysis for pulse compression radar using Gold code sequences

This paper presents analytic, simulation, and measured results of using Gold sequences for radar pulse compression coding. Gold-coded waveform performance is characterized using the ambiguity function diagram, synonymous with matched filtering performance. Results indicate Gold-coded waveforms offer significant improvement in radar clutter suppression, resolution, and unambiguous range properties.

Secondary data support in space-time adaptive processing

One of the primary problems with the application of space-time adaptive processing (STAP) techniques to radar is secondary data support for the interference plus noise covariance matrix estimate. Reed (1974) has shown the required secondary data support to achieve performance within 3 dB of optimal SINR is approximately twice the degrees of freedom (DOF). Reed proved this rule for sample matrix inversion (SMI) techniques. A concern arises when applying this rule to a newer class of reduced dimension STAP algorithms that do not fall under the SMI umbrella. This paper focuses on the cross spectral metric (CSM) algorithm (Goldstein and Reed, 1997). Through Monte Carlo simulations, Reeds rule for sample support is examined for this non-SMI technique. Optimum SINR performance for the CSM algorithm is obtained by choosing the number of DOF in the algorithm equal to the interference subspace dimension. With this choice, the required sample support for the covariance matrix estimate is approximately 2.5 times the interference subspace dimension. This relationship is not consistent.