Ground Moving Target Indicator Data Research Articles

Outlier Detection over Massive-Scale Trajectory Streams

The detection of abnormal moving objects over high-volume trajectory streams is critical for real-time applications ranging from military surveillance to transportation management. Yet this outlier detection problem, especially along both the spatial and temporal dimensions, remains largely unexplored. In this work, we propose a rich taxonomy of novel classes of neighbor-based trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real-time applications. Our theoretical analysis and empirical study on two real-world datasets—the Beijing Taxi trajectory data and the Ground Moving Target Indicator data stream—and one generated Moving Objects dataset demonstrate the effectiveness of our taxonomy in effectively capturing different types of abnormal moving objects. Furthermore, we propose a general strategy for efficiently detecting these new outlier classes called the <underline>m</underline>inimal <underline>ex</underline>amination (MEX) framework. The MEX framework features three core optimization principles, which leverage spatiotemporal as well as the predictability properties of the neighbor evidence to minimize the detection costs. Based on this foundation, we design algorithms that detect the outliers based on these classes of new outlier semantics that successfully leverage our optimization principles. Our comprehensive experimental study demonstrates that our proposed MEX strategy drives the detection costs 100-fold down into the practical realm for applications that analyze high-volume trajectory streams in near real time.

Long-CPI Multichannel SAR-Based Ground Moving Target Indication

The detection of ground moving targets with arbitrary linear motion from an airborne multichannel radar via a long coherent processing interval is considered. The technique maximizes the target signal energy in synthetic aperture radar (SAR) images by matching to the target's linear motion profile. A reparameterization of the target's linear motion is developed that decouples the array processing from the image formation process. An explicit approach to forming SAR images focused with respect to a particular along-track velocity is presented. The ground clutter is canceled via an adaptive array technique, which also yields an estimate of the target radial velocity. Because the algorithm generates estimates of the target's along-track and cross-track velocity components, a unique determination of the target's motion parameters is possible. The approach is derived and investigated via simulated point spread functions. Algorithm performance is demonstrated on the GOTCHA SAR ground moving target indication data set.

Multichannel SAR-GMTI in Maritime Scenarios With F-SAR and TerraSAR-X Sensors

This paper explores the ground moving target indication (GMTI) capabilities of the German Aerospace Center’s state-of-the-art airborne (F-SAR) and spaceborne (TerraSAR-X) synthetic aperture radars (SARs) when operating over maritime scenarios. The performance of classical dual channel GMTI techniques, such as displaced phase center antenna (DPCA) and along-track interferometry (ATI), as well as the promising adaptive techniques, like extended DPCA (EDPCA) and imaging space–time adaptive processing (ISTAP) have been analyzed on the basis of experimental acquisitions with both sensors. The objective of the paper is to highlight the limitations and challenges to be considered when processing real, multichannel GMTI data from pioneering SAR sensors for maritime surveillance. Different calibration or channel balancing strategies, on the basis of the digital balancing (DB) method, are studied, considering their impact on SAR-GMTI performance. An adaptive SAR processor, accounting for target kinematics and based on a matched filter bank (MFB) approach, is integrated in the SAR-GMTI processing chain in order to retrieve refocused images of the moving vessels.

Ground Moving Target Indication via Multichannel Airborne SAR

We consider moving target detection and velocity estimation for multichannel synthetic-aperture-radar (SAR)-based ground moving target indication (GMTI). Via forming velocity versus cross-range images, we show that small moving targets can be detected even in the presence of strong stationary ground clutter. Furthermore, the velocities of the moving targets can be estimated, and the misplaced moving targets can be placed back to their original locations based on the estimated velocities. An iterative adaptive approach, which is robust and user parameter free, is used to form velocity versus cross-range images for each range bin of interest. Moreover, we discuss calibration techniques to estimate the relative antenna distances and antenna gains in practical systems. Furthermore, we present a simple algorithm for stationary clutter cancelation. We conclude by demonstrating the effectiveness of our approaches by using the Air Force Research Laboratory publicly released Gotcha airborne SAR-based GMTI data set.

X-band experimental airborne radar – Phase II: synthetic aperture radar and ground moving target indication

Defence Research and Development Canada, Ottawa, has completed Phase II of a multifunction X-band wideband experimental airborne radar. The system consists of a high average power transmitter, a digital waveform generator, two wideband 8-bit channels for synthetic aperture radar (SAR)/inverse synthetic aperture radar and two narrowband 14-bit channels for ground moving target indication (GMTI). The reflector antenna uses a novel multimode feedhorn to derive two phase centres displaced in azimuth. The radar was designed to support research into SAR imaging of fixed and moving targets (ocean and land), time–frequency analysis of moving targets, clutter suppression for GMTI radar and ocean surveillance for small and large target detection. Highlights of the data collection capabilities, include a swath width 16 K points wide in the single-channel SAR modes, 8 K points wide in the two-channel integrated SAR–GMTI modes and 4 K points wide in the GMTI surveillance modes. The architecture of the radar, its modes of operation with respect to the SAR and GMTI data collection capabilities, the MATLAB-based GMTI processor, and the real-time kernel developed for the SAR processor are discussed; and results from its high resolution stripmap, landspot, seaspot and GMTI modes are presented from trials in July 2003.

Simultaneous ground moving target tracking and identification using wavelets features from HRR data*1

An algorithm for simultaneous ground moving target tracking and identification (ID) using both Ground Moving Target Indication (GMTI) radar reports and High Resolution Range (HRR) radar profiles is presented in this paper. The GMTI data provide target kinematics information and the HRR profiles deliver target ID information. Wavelet coefficients are first extracted from HRR profiles and then processed by a state-of-the-art wavelets-based statistical signal processing technique: wavelets domain hidden Markov trees (HMT). A probabilistic approach is adopted to effectively track and identify ground moving targets in confusing scenarios.

Distributed Multirate Interacting Multiple Model Fusion (DMRIMMF) With Application to Out-of-SequenceGMTI Data

This note develops a distributed approach for fusing ground moving target indicator data with out-of-sequence (OOS) measurements. A multirate interacting multiple model (MRIMM) fusion algorithm is developed for effectively fusing multirate information. The multirate approach provides an excellent framework for efficient information retrodiction and forward update. A multirate interacting multiple model filter is employed locally to track a target with or without maneuvering behavior. The combination of global MRIMM fusion and local MRIMM tracking proves to be powerful for tracking and fusing maneuvering and nonmaneuvering targets in an environment of OOS measurement reporting.

Simultaneous ground moving target tracking and identification using wavelets features from HRR data

An algorithm for simultaneous ground moving target tracking and identification (ID) using both Ground Moving Target Indication (GMTI) radar reports and High Resolution Range (HRR) radar profiles is presented in this paper. The GMTI data provide target kinematics information and the HRR profiles deliver target ID information. Wavelet coefficients are first extracted from HRR profiles and then processed by a state-of-the-art wavelets-based statistical signal processing technique: wavelets domain hidden Markov trees (HMT). A probabilistic approach is adopted to effectively track and identify ground moving targets in confusing scenarios.