Surveillance systems against missile attacks require the automatic detection of targets with low false alarm rate (FAR). Infrared Search and Track (IRST) systems offer a passive detection of threats at long ranges. Fo...
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ISBN:
(纸本)0819441872
Surveillance systems against missile attacks require the automatic detection of targets with low false alarm rate (FAR). Infrared Search and Track (IRST) systems offer a passive detection of threats at long ranges. For maximum reaction time and the arrangement of counter measurements, it is necessary to declare the objects as early as possible. For this purpose the detection and tracking algorithms have to deal with point objects. Conventional object features like shape, size and texture are usually unreliable for small objects. More reliable features of point objects are three-dimensional spatial position and velocity. At least two sensors observing the same scene are required for multi-ocular stereo vision. Mainly three steps are relevant for successful stereo image processing. First of all the precise camera calibration (estimating the intrinsic and extrinsic parameters) is necessary to satisfy the demand of high degree of accuracy, especially for long range targets. Secondly the correspondence problem for the detected objects must be solved. thirdly the three-dimensional location of the potential target has to be determined by projective transformation. For an evaluation a measurement campaign to capture image data was carried out with real targets using two identical IR cameras and additionally synthetic IR image sequences have been generated and processed. In this paper a straightforward solution for stereo analysis based on stationary bin-ocular sensors is presented, the current results are shown and suggestions for future work are given.
In this article we consider estimation for a discrete-time hybrid dynamical system. Hybrid dynamical systems arise quite naturally in many estimation problems, for example target tracking in a maneuvering target scena...
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ISBN:
(纸本)0819441872
In this article we consider estimation for a discrete-time hybrid dynamical system. Hybrid dynamical systems arise quite naturally in many estimation problems, for example target tracking in a maneuvering target scenario. the most commonly used suboptimal estimation algorithm for the problem class just described, is the so called Interacting Multiple Model (IMM) algorithm. In the work presented here, angle-only target tracking problems are considered, in particular we consider scenarios including maneuvering targets. One approach to this class of problem is to apply the IMM scheme. Using the scheme in Elliott and Dufour,(1) we compute estimates of position and velocity for a maneuvering target when only the target bearing is available. these estimates are compared to the IMM scheme applied to the same problem. It is shown that the algorithm proposed in Elliott and Dufour(1) offers significant improvements over the IMM scheme.
Recognizing targets in infra-red (IR) images has been a long-standing and challenging problem. In this paper we outline a combination of detection methods based on optimized feature discrimination and correlation base...
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ISBN:
(纸本)0819440744
Recognizing targets in infra-red (IR) images has been a long-standing and challenging problem. In this paper we outline a combination of detection methods based on optimized feature discrimination and correlation based classification techniques that demonstrate an improvement ability to separate target from clutter. Preliminary results are shown on a small subset of data obtained from NVESD to illustrate the possible performance gains.
In this paper we present an algorithm for initiating 3-D tracks using range and azimuth (bearing) measurements from a 2-D radar on a moving platform. the work is motivated by the need to track possibly low-flying targ...
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ISBN:
(纸本)0819441872
In this paper we present an algorithm for initiating 3-D tracks using range and azimuth (bearing) measurements from a 2-D radar on a moving platform. the work is motivated by the need to track possibly low-flying targets, e.g., cruise missiles, using reports from an aircraft-based surveillance radar. Previous work on this problem considered simple linear motion in a flat earth coordinate frame [13]. Our research extends this to a more realistic scenario where the earth's curvature is also considered. the target is assumed to be moving along a great circle at a constant altitude. After the necessary coordinate transformations, the measurements are nonlinear functions of the target state and the observability of target altitude is severely limited. the observability, quantified by the Cramer-Rao Lower Bound (CRLB), is very sensitive to the sensor-to-target geometry. the paper presents a Maximum Likelihood (ML) estimator for estimating the target motion parameters in the Earth Centered Earth Fixed coordinate frame from 2-D range and angle measurements. In order to handle the possibility of false measurements and missed detections, which was not considered in [13], we use the Probabilistic data Association (PDA) algorithm to weight the detections in a frame. the PDA-based modified global likelihood is optimized using a numerical search. the accuracies obtained by the resulting ML-PDA estimator are quantified using the CRLB for different scnsor-target configurations. It is shown that the proposed estimator is efficient, that is, it meets the CRLB. Of particular interest is the achievable accuracy for estimating the target altitude, which is not observed directly by the 2-D radar, but can be only inferred from the range and bearing observations.
In this paper, we will discuss a novel technology, which we have recently developed for automatic target detection and recognition by polarimetric imaging systems. this technology consists of an approach to non-cooper...
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ISBN:
(纸本)0819440744
In this paper, we will discuss a novel technology, which we have recently developed for automatic target detection and recognition by polarimetric imaging systems. this technology consists of an approach to non-cooperative small target detection that uses statistical techniques to exploit a target's Stokes vector infrared signature. this is applicable to sensors whose signature measurements are sensitive to the polarization of the targets and their backgrounds. Fusion is achieved by constructing the joint statistical measures for the target's polarization states. Target polarization states are in terms of the intensity, percent of linear polarization, and the angle of polarization plane. Applications of the proposed approach, for military targets under variations in target geometry are made in terms of receiver operating characteristic condition curves. the new results, which have been obtained on data from the Air Force's IRMA polarimetric infrared simulation tool, indicate the usefulness of polarimetric infrared signatures for the automatic detection of smalltargets.
We present a Bayesian algorithm for optimal multiframe detection and tracking of small extended targets in two-dimensional (2D) finite resolution images. the algorithm integrates detection and tracking into a single f...
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In this paper we describe a novel data association algorithm, termed m-best SD, that determines in O(mSkn(3)) time (m assignments, S lists of size n, k relaxations) the m-best solutions to an SD assignment problem. th...
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ISBN:
(纸本)0819428221
In this paper we describe a novel data association algorithm, termed m-best SD, that determines in O(mSkn(3)) time (m assignments, S lists of size n, k relaxations) the m-best solutions to an SD assignment problem. this algorithm is applied to the following problem. Given line of sight (i.e., incomplete position) measurements from S sensors, sets of complete position measurements are extracted, namely, the 1-st, 2-nd,... m-th best (in terms of likelihood) sets of composite measurements are determined solving a static SD assignment problem. Utilizing the joint likelihood functions used to determine the m-best SD assignment solutions, the composite measurements are then quantified with a probability of being correct using a JPDA-like technique. Lists of composite measurements from successive scans, along withtheir corresponding probabilities, are then used in turn with a state estimator in a dynamic 2D assignment algorithm to estimate the states of the moving targets over time. the dynamic assignment cost coefficients are based on a likelihood function that incorporates the "true" composite measurement probabilities obtained from the (static) m-best SD assignment solutions. We demonstrate this algorithm on a multitarget passive sensor track formation and maintenance problem, consisting of multiple time samples of line of sight (LOS) measurements originating from multiple (S = 7) synchronized high frequency direction finding sensors. Another significance of this work is that the m-best SD assignment algorithm (in a sliding window mode) provides for an efficient implementation of a Multiple Hypothesis Tracking (MHT) algorithm by obviating the need for a brute force enumeration of an exponential number of joint hypotheses.
In real-time digital-signalprocessing systems, data often enter or leave the computationally intensive parts at small integer multiples of the clocking interval. In these cases, traditional microprocessor-based archi...
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