Jeffery R. Layne

A comparison of nonlinear filtering approaches with an application to ground target tracking

With an application to ground target tracking, two groups of nonlinear filtering approaches are compared in this paper: Gaussian approximation and Monte Carlo simulation. The former group, consisting of the extended Kalman filter (EKF), Gauss-Hermite filter (GHF) and unscented Kalman filter (UKF), approximates probability densities of nonlinear systems using either single or multiple points in a state space, while the latter group, being particle filters, estimates probability densities using random samples. There are two sources contributing to nonlinearity in the ground target tracking problem: terrain and road constrained kinematic modeling and polar coordinate sensing. When tracking ground maneuvering targets with multiple models, one faces another problem, i.e., non-Gaussianity. This paper also compares interacting multiple model (IMM)-based filters IMM-EKF, IMM-GHF and IMM-UKF with particle-based multiple model filters for their capability in handling the non-Gaussian problem. Simulation results show that: (1) all the filters achieve a comparable performance when tracking non-maneuvering ground targets; (2) particle-based multiple model filters are superior to IMM-based filters in maneuvering ground target tracking.

A fuzzy dynamic model based state estimator

Systems containing uncertainty are traditionally analyzed with probabilistic methods. However, for non-linear, non-Gaussian systems solutions can sometimes be very difficult to obtain. The focus of this work is to determine if in such cases fuzzy dynamic system models may provide an alternative approach that more easily leads us to a good solution. In this paper, we present a fuzzy estimator whose system model is a fuzzy dynamic system. We show that for the linear, Gaussian case the fuzzy estimator produces the same result as the Kalman filter. More importantly, we show that the fuzzy estimator can succeed for some non-Gaussian, nonlinear systems. Finally, we illustrate the application of the fuzzy estimator on a non-linear, non-Gaussian, time-varying rocket launch problem where we show that it performs better than the extended Kalman filter. From a broad perspective this paper essentially shows how to build on Zadehs seminal ideas in fuzzy sets, logic, and systems and use Kalmans seminal ideas on optimal estimators to construct a novel fuzzy estimator for non-linear estimation problems. While this seems to reconcile some of the fundamental ideas of Zadeh and Kalman it is unfortunate that the fuzzy estimator can be very computationally complex to implement for practical applications.

Monopulse radar tracking using an adaptive interacting multiple-model method with extended Kalman filters

In this paper we investigate an adaptive interacting multiple model (AIMM) tracker using the extended Kalman filter. This adaptive algorithm is based on the interacting multiple model (IMM) tracking technique with the addition of an adaptive acceleration model to track behavior that falls in between the fixed model dynamics. In previous research, we found that the adaptive model matches more closely the true system dynamics when the target kinematics lie in between the fixed models, thus improving the overall performance of the tracking system. We also showed that the AIMM outperforms other existing adaptive approaches while reducing computational complexity. In this paper, we further investigate these superior qualities of the AIMM by considering a more realistic radar-tracking scenario where monopulse radar range, azimuth, and elevation measurements are processed using extended Kalman filters in the AIMM. Here a more complex 3D simulation is implemented instead of the simplified 2D problem considered in our previous research. Again, the result how that the AIMM outperforms the classical IMM when the target is maneuvering.

Identifying moving HRR signatures with an ATR belief data association filter

The goal of this paper is to demonstrate the benefits of a tracking and identification algorithm that uses a belief data association filter for target recognition. By associating track and ID information, the belief filter accumulates evidence for classifying High-Range Resolution (HRR) radar signatures from a moving target. A track history can be utilized to reduce the search space of targets for a given pose range. The technique follows the work of Mitchell and Westerkamp by processing HRR amplitude and location feature sets. The new aspect of the work is the identification of multiple moving targets of the same type. The conclusions from the work is that moving ATR from HRR signatures necessitates a track history for robust target ID.

Multiple model estimator for a tightly coupled HRR automatic target recognition and MTI tracking system

The goal of this research is to exploit couplings between tracking and ATR systems employing high range resolution radar (HRRR) and moving target indicator (MTI) measurements. As will be shown, these systems are coupled via pose, kinematic, and association constraints. Exploiting these couplings results in a tightly coupled system with significantly improved performance. This problem deals with two different types of spaces, namely the continuous space kinematics (e.g. position and velocity) and the discrete space target type. A multiple model estimator (MME) was chosen for this problem. The MME consist of a bank of extended Kalman filters (one for each target type). The continuous space kinematics are dealt with via these extended Kalman filter. Further, the probability of each Kalman filter is computed and used to determine the corresponding discrete space target probability. Presented in this paper are empirical results that show improvement over conventional techniques.

Content-based 3D mosaic representation for video of dynamic 3D scenes

We propose a content-based 3D mosaic representation for long video sequences of 3D and dynamic scenes captured by a camera on a mobile platform. The motion of the camera has a dominant direction of motion (as on an airplane or ground vehicle), but 6 degrees-of-freedom (DOF) motion is allowed. In the first step, a pair of generalized parallel-perspective (pushbroom) stereo mosaics is generated that captured both the 3D and dynamic aspects of the scene under the camera coverage. In the second step, a segmentation-based stereo matching algorithm is applied to extract parametric representation of the color, structure and motion of the dynamic and/or 3D objects in urban scenes where a lot of planar surfaces exist. Based on these results, the content-based 3D mosaic (CB3M) representation is created, which is a highly compressed visual representation for very long video sequences of dynamic 3D scenes. Experimental results are given

DYNAMIC PUSHBROOM STEREO VISION

We present a dynamic pushbroom stereo geometry model for both 3D reconstruction and moving target extraction in applications such as aerial surveillance and cargo inspection. In a dynamic pushbroom camera model, a “line scan camera” scans across the scene. Both the scanning sensor and the objects in the scene are moving, and thus the image generated is a “moving picture” with one axis being space and the other being time. We study the geometry under a linear motion model for both the sensor and the object, and we investigate the advantages of using two such scanning systems to construct a dynamic pushbroom stereo vision system for 3D reconstruction and moving target extraction. Two real examples are given using the proposed models. In the first application, a fast and practical calibration procedure and an interactive 3D estimation method are provided for 3D cargo inspection with dual gamma-ray (or X-ray) scanning systems. In the second application, dynamic pushbroom stereo mosaics are generated by using a single camera mounted on an airplane, and a unified segmentation-based stereo matching algorithm is proposed to extract both 3D structures and moving targets from urban scenes. Experimental results are given.

Efficient multirate interacting multiple model particle filter(MRIMM-PF) for target tracking

In many cases, tracking ground targets can be formulated as a nonlinear filtering problem when terrain and road constraints are incorporated into system modeling and polar coordinate is used. Furthermore, when tracking ground maneuvering targets with an interacting multiple model (IMM) approach, a non-Gaussian problem exists due to an inherent mixing operation. A multirate interacting multiple model particle filter (MRIMM-PF) is presented in this paper to effectively solve the problem of nonlinear and non-Gaussian tracking, with an emphasis on computational savings.

Contrast Equalization Methods for Layered-Sensing Systems

Layered sensing is a relatively new construct in the repertoire of the US Air Force. Under the layered sensing paradigm, an area is surveyed by a multitude of sensors at varying altitudes, and operating across many modalities. One of the recent pushes is to incorporate multi-sensor systems and create from them a single image. However, if the sensor parameters are not properly adjusted, the contrast amongst the images from camera to camera will vary greatly. This can create issues when performing tracking and analysis work. The contribution of this paper is to explore and provide an evaluation of various techniques for histogram equalization of Electro-Optical (EO) video sequences whose views are centered on a city. In this paper, the performance of several methods on histogram equalization are evaluated under the layered sensing construction.

Content-based 3D mosaics for dynamic urban scenes

We propose a content-based 3D mosaic (CB3M) representation for long video sequences of 3D and dynamic scenes captured by a camera on a mobile platform. The motion of the camera has a dominant direction of motion (as on an airplane or ground vehicle), but 6 DOF motion is allowed. In the first step, a set of parallel-perspective (pushbroom) mosaics with varying viewing directions is generated to capture both the 3D and dynamic aspects of the scene under the camera coverage. In the second step, a segmentation-based stereo matching algorithm is applied to extract parametric representations of the color, structure and motion of the dynamic and/or 3D objects in urban scenes where a lot of planar surfaces exist. Multiple pairs of stereo mosaics are used for facilitating reliable stereo matching, occlusion handling, accurate 3D reconstruction and robust moving target detection. We use the fact that all the static objects obey the epipolar geometry of pushbroom stereo, whereas an independent moving object either violates the epipolar geometry if the motion is not in the direction of sensor motion or exhibits unusual 3D structures. The CB3M is a highly compressed visual representation for a very long video sequence of a dynamic 3D scene. More importantly, the CB3M representation has object contents of both 3D and motion. Experimental results are given for the CB3M construction for both simulated and real video sequences to show the accuracy and effectiveness of the representation.