William L. Melvin

Space-time adaptive radar performance in heterogeneous clutter

Traditional analysis of space-time adaptive radar generally assumes the ideal condition of statistically independent and identically distributed (IID) secondary data. To the contrary, measured data suggests realistic clutter environments appear heterogeneous and so the secondary data is no longer IID. Heterogeneity leads to mismatch between actual and estimated covariance matrices, thereby magnifying the loss between the adaptive implementation and optimum condition. Concerns regarding the impact of clutter heterogeneity on space-time adaptive processing (STAP) warrant further study. To this end, we propose space-time models of amplitude and spectral clutter heterogeneity, with operational airborne radar in mind, and then characterize expected STAP performance loss under such heterogeneous scenarios. Simulation results reveal loss in signal-to-interference plus noise ratio (SINR) ranging between a few tenths of a decibel to greater than 16 dB for specific cases.

Improving practical space-time adaptive radar

This paper discusses the incorporation of nonhomogeneity detection with space-time adaptive processing to improve the formation of adaptive weights in practical adaptive airborne radar. We examine the problem of improving interference covariance matrix estimation in real-world environments and discuss several approaches for integrating nonhomogeneity detection with space-time adaptive processing. We use measured airborne data from the Rome Laboratory Multichannel Airborne Radar Measurements Program to illustrate key points.

An approach to knowledge-aided covariance estimation

This paper introduces a parametric covariance estimation scheme for use with space-time adaptive processing (STAP) methods operating in heterogeneous clutter environments. The approach blends both a priori knowledge and data observations within a parameterized model to capture instantaneous characteristics of the cell under test (CUT) and reduce covariance errors leading to detection performance loss. We justify this method using both measured and synthetic data. Performance potential for the specific operating conditions examined herein include: 1) averaged behavior within roughly 2 dB of the optimal filter, 2) 1 dB improvement in exceedance characteristic relative to the optimal filter, highlighting improved instantaneous capability, and 3) impervious ness to corruptive target-like signals in the secondary data (no additional signal-to-interference-plus-noise ratio (SINK) loss, compared with 10 dB or greater loss for the standard STAP implementation), with corresponding detections comparable to the optimal filter case

Assessment of multichannel airborne radar measurements for analysis and design of space-time processing architectures and algorithms

System design studies and detailed radar simulations have identified the utility of space-time adaptive processing (STAP) to accomplish target detection in cases where the target Doppler is immersed in sidelobe clutter and jamming. A recent US Air Force investment in STAP has produced a database of multichannel airborne data, through Rome Laboratorys Multichannel Airborne Radar Measurement (MCARM) program, to further develop STAP architectures and algorithms suited to operational environments. An aspect of actual data not typically incorporated into simulation scenarios is the nonhomogeneous features of real-world clutter and interference scenarios. In this paper we investigate the impact of nonhomogeneous data on the performance of STAP. Furthermore, we propose a preliminary scheme to detect and excise nonhomogeneous secondary data in the sample covariance estimation, thereby dramatically improving STAP performance as shown through a specific example using monostatic MCARM data.

Adaptive cancellation method for geometry-induced nonstationary bistatic clutter environments

This paper describes and characterizes a new bistatic space-time adaptive processing (STAP) clutter mitigation method. The approach involves estimating and compensating aspects of the spatially varying bistatic clutter response in both angle and Doppler prior to adaptive clutter suppression. An important feature of the proposed method is its ability to extract requisite implementation information from the data itself, rather than rely on ancillary - and possibly erroneous or missing - system measurements. We justify the essence of the proposed method by showing its ability to align the dominant clutter subspaces of each range realization relative to a suitably chosen reference point as a means of homogenizing the space-time data set. Moreover, we numerically characterize performance using synthetic bistatic clutter data. For the examples considered herein, the proposed bistatic STAP method leads to maximum performance improvements between 17.25 dB and 20.75 dB relative to traditional STAP application, with average improvements of 6 dB to 10 dB.

Knowledge-based space-time adaptive processing

This paper describes an innovative concept for knowledge-based control of space-time adaptive processing (STAP). The knowledge-based approach holds potential for significant performance improvements over classical STAP processing in non-homogeneous environments by taking advantage of a priori knowledge. Under this approach, knowledge-based control is used to direct pre-adaptive filtering, and to carefully select STAP algorithms, parameters, and secondary data cells.

Analyzing space-time adaptive processors using measured data

We use monostatic data obtained from the Multichannel Airborne Radar Measurements (MCARM) program to conduct a comparative analysis of various space-time adaptive processing techniques. Specifically, we consider reduced-dimension and reduced-rank methods. The measured data analysis shows the significance of training sample selection, effects of nonhomogeneity in the clutter data, sensitivity of certain classes of adaptive filters to the rank of the measured clutter signal and suitability of the various approaches.

Detection and identification of human targets in radar data

Radar offers unique advantages over other sensors, such as visual or seismic sensors, for human target detection. Many situations, especially military applications, prevent the placement of video cameras or implantment seismic sensors in the area being observed, because of security or other threats. However, radar can operate far away from potential targets, and functions during daytime as well as nighttime, in virtually all weather conditions. In this paper, we examine the problem of human target detection and identification using single-channel, airborne, synthetic aperture radar (SAR). Human targets are differentiated from other detected slow-moving targets by analyzing the spectrogram of each potential target. Human spectrograms are unique, and can be used not just to identify targets as human, but also to determine features about the human target being observed, such as size, gender, action, and speed. A 12-point human model, together with kinematic equations of motion for each body part, is used to calculate the expected target return and spectrogram. A MATLAB simulation environment is developed including ground clutter, human and non-human targets for the testing of spectrogram-based detection and identification algorithms. Simulations show that spectrograms have some ability to detect and identify human targets in low noise. An example gender discrimination system correctly detected 83.97% of males and 91.11% of females. The problems and limitations of spectrogram-based methods in high clutter environments are discussed. The SNR loss inherent to spectrogram-based methods is quantified. An alternate detection and identification method that will be used as a basis for future work is proposed.

Doubly adaptive bistatic clutter filtering

Bistatic aerospace radar systems offer certain operational advantages over their monostatic counterparts. However, bistatic radar must contend with spectrally severe ground clutter returns. Effective adaptive filtering is necessary to improve detection performance, but the non-stationary nature of bistatic clutter degrades performance. As a result, bistatic STAP techniques must incorporate modifications accommodating the unique aspects of the clutter returns. This paper introduces a new bistatic STAP technique based on a doubly adaptive process. The method offers substantial improvement over traditionally applied STAP methods. We also compare this method against some other recently proposed bistatic STAP approaches.

Adaptive detection in dense target environments

We consider the impact of target signals corrupting the covariance estimate when implementing STAP in ground moving target indication (GMTI) scenarios. Herein, we propose a model for seeding the radar data cube with target signals and calculate both asymptotic and finite training data detection losses. In general, both the location and strength of the corruptive target signals influence performance: under practical conditions, SINR loss can exceed 10 dB. Measured MCARM data confirms the presence of ample ground moving target signals in the radar field of regard under typical operating conditions. We conclude by briefly considering several mitigating strategies as well.