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Trajectory estimation of closely spaced objects (CSO) using infrared focal plane data of an STSS (Space tracking and surveillance system) platform

Trajectory estimation of closely spaced objects (CSO) using ...

引用

16th conference on signal and data processing of small targets

作者： Korn, J Holtz, H Farber, MS Aerosp Corp El Segundo CA 90245 USA

ISBN: (纸本)081945351X

A major challenge in tracking midcourse objects by means of an infrared (IR) sensor is that during a significant part of the trajectory they are closely spaced (Closely Spaced Objects, or CSO). the imprints of C the CSOs on the IR focal plane create blurred unresolved clusters where the number, the coordinates, and the radiant intensities of the objects are not immediately apparent. this paper presents two methods for solving the problem of midcourse CSO resolution using IR focal plane data in the context of the Space Tracking and Surveillance System (STSS). Both approaches are based on dynamics/radiant intensity models of the focal plane objects, and use least squares-based minimization procedures(1). the first and more traditional baseline approach estimates the focal plane coordinates of the objects and their intensities on a frame-by-frame basis. the object tracks are then established by associating and fitting the estimates of all the frames to the postulated models. An alternative, multi-frame approach explored in this paper, uses the focal plane information from an entire sequence of frames, and, using least squares criteria over space and time and matched filtering, estimates the model parameters directly. With this "track-before-detect" approach, the association problem of objects to tracks is embedded in the estimation procedure.

关键词： Closely Space Objects (CSO) infrared (IR) focal plane

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An advanced missile warning processing suite

An advanced missile warning processing suite

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20th conference on signal and data processing of small targets

作者： Montgomery, Joel B. Sanderson, Richard B. McCalmont, John F. Johnson, R. S. McDermott, D. J. Taylor, M. J. M&M Aviat 2302 Schnebly Rd Spring Valley OH 45370 USA Air Force Res Lab Wright Patterson AFB OH 45433 USA MacAulay Brown Dayton OH 45430 USA Gen Dynam Corp Dayton OH 45430 USA

ISBN: (纸本)9780819471604

Effective missile warning and countermeasures remain an unfulfilled goal for the Air Force and others in the DOD community. To make the expectations a reality, newer sensors exhibiting the required sensitivity, field of regard, and spatial resolution are being developed and transitioned. the largest concern is in the first stage of a missile warning system: detection, in which all targets need to be detected with a high confidence and with very few false alarms. Typical fielded sensors are limited in their detection capability by either lack of sensitivity or by the presence of heavy background clutter, sun glints, and inherent sensor noise. Many threat environments include false alarm sources like burning fuels, flares, exploding ordinance, arc welders, and industrial emitters. Multicolor discrimination has been shown as one of the effective ways to improve the performance of missile warning sensors, particularly for heavy clutter situations. Its utility has been demonstrated in multiple demonstration and fielded systems. New exploitations of background and clutter spectral contents, coupled with advanced spatial and temporal filtering techniques, have resulted in a need to have a new baseline algorithm on which future processing advances may be judged against. this paper describes the AFRL Suite IIIc algorithm chain and its performance against long-range dim targets in clutter.

关键词： infrared background suppression multicolor missile warning point targets discrimination algorithm clutter rejection backgrounds real-time processing parallel processing

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Track-to-track association and bias removal

Track-to-track association and bias removal

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14th conference on signal and data processing of small targets

作者： Stone, LD Williams, ML Tran, TM Metron Inc Reston VA 20190 USA

ISBN: (纸本)0819444782

this paper develops methods for associating two sets of sensor tracks in the presence of missing tracks and translation bias. Key results include (1) Extension of the Maximum A Posteriori Probability method of matching tracks to use feature information as well as kinematic information;(2) translation bias removal techniques that are computationally tractable for large numbers of tracks, and effective in the presence of missing tracks. these methods were evaluated by Monte Carlo simulation. the experimental results indicate that the Maximum A Posteriori Probability method with its adaptive threshold achieves close to its best performance for matching tracks without an additional threshold adjustment.

关键词： Sensors Target detection Monte Carlo methods Computer programming Fourier transforms Computer simulations Detection and tracking algorithms signal to noise ratio Kinematics Copper

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Nonlinear estimation techniques for impact point prediction of ballistic targets - art. no. 62360C

Nonlinear estimation techniques for impact point prediction ...

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18th conference on signal and data processing of small targets

作者： Hardiman, David F. Kerce, J. Clayton Brown, George C. USA Redstone Arsenal AL 35898 USA

ISBN: (纸本)0819462926

this paper considers three nonlinear estimation algorithms for impact point prediction (IPP) of ballistic targets. the paper assumes measurements are available from a 3D surveillance radar or phased array radar over some portion of the ballistic trajectory. the ballistic target (BT) is tracked using an extended Kalman filter (EKF), an unscented Kalman filter (UKF), and a particle filter (PF). With the track estimate as an initial condition, the equations of motion for the BT are integrated to obtain a prediction of the impact point. this paper compares the performance of the EKF, UKF, and a particular choice of PF for impact point prediction. the traditional Extended Kalman Filter equations are based on a first-order Taylor, series approximation of the nonlinear transformations (expanded about the latest state estimate). Both the Unscented Kalman Filter and the Particle Filter allow nonlinear systems to be modeled without prior linearizion. the primary focus of the analysis presented in this paper is comparing the performance and accuracy of the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the chosen Particle Filter implementation for impact point prediction. the three filtering techniques are compared to the theoretical Cramer-Rao lower bounds (CRLB) of estimation error.

关键词： estimation non-linear dynamic system Cramer-Rao lower bound (CRLB) Kalman filter

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Tracking and classification with attribute data from multiple legacy sensors - art. no. 591319

Tracking and classification with attribute data from multipl...

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17th conference on signal and data processing of small targets

作者： Drummond, OE CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)0819459186

An obvious use for feature and attribute data is for classification and in combat identification. the term classification is used broadly here to include discrimination, detection, target typing, identification, and pattern recognition. An additional use is in the data (or track) association process to reduce the misassociations, often called feature aided tracking. Previous papers discussed the integration of features and attributes into target track processing in addition to their use in multiple target classification. the distinction is made between feature and attribute data because they are processed differently. the term features applies to random variables from continuous sample space and attributes applies to random variable data from discrete sample space. the primary concern of this paper is to address processing of attributes with data from legacy sensors. For example, in fusion of sensor data from distributed sensors, a sensor might distribute the likelihood of the most likely target class (or attribute property) and no information on the other possibilities. In another example, a sensor might distribute the likelihood that the target is any one of a number of target classes, a subset of all the target classes, rather than indicating one specific target class. Both attribute aided tracking and (post tracker) target classification are addressed for these examples of incomplete data from legacy sensors. the purpose of this paper is to show the feasibility of simple approaches to dealing with data from some legacy sensors.

关键词： Multiple target tracking feature aided tracking features attributes categorical features multiple models Bayesian methods Kalman filter.

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Tracking dim targets using integrated clutter estimation

Tracking dim targets using integrated clutter estimation

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19th conference on signal and data processing of small targets

作者： Brekke, Edmund Kirubarajan, thiagalingam tharmarasa, Ratnasingharn Norwegian Univ Sci & Technol Dept Engn Cybernet NO-7491 Trondheim Norway Univ Grad Ctr NO-2027 Kjeller Norway McMaster Univ Elect & Comp Engn Hamilton ON L8S 4K1 Canada

ISBN: (纸本)9780819468475

In this paper we address the problem of detecting and tracking a single dim target in unknown background noise. Several methodologies have been developed for this problem, including track-before-detect (TBD) methods which work directly on unthresholded sensor data. the utilization of unthresholded data is essential when signal-to-noise ratio (SNR) is low, since the target amplitude may never be strong enough to exceed any reasonable threshold. Several problems arise when working with unthresholded data. Blurring and non-Gaussian noise can easily lead to very complicated likelihood expressions. the back-round noise also needs to be estimated. this estimate is a random variable due to the random nature of the background noise. We propose a recursive TBD method which estimates the background noise as part of its likelihood evaluation. the background noise is estimated by averaging over nearby sensor cells not affected by the target. the uncertainty of this estimate is taken into account by the likelihood evaluation, thereby yielding a more robust TBD method. the method is implemented using sequential Monte Carlo evaluation of the optimal Bayes equations, also known as particle filtering. Simulation results show how our method allows detection and tracking to be carried out in an uncertain environment where current recursive TBD methods fail.

关键词： track-before-detect dim target clutter estimation particle filter

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Future prospects for algorithm development of tracking related processing - art. no. 66990E

Future prospects for algorithm development of tracking relat...

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19th conference on signal and data processing of small targets

作者： Drummond, Oliver E. CyberRnD Inc Culver City CA 90230 USA

ISBN: (纸本)9780819468475

the state of the art of tracking has matured and consequently, the priorities for improved performance and expanded or new processing capabilities have changed. Future directions in algorithm development in tracking and related data processing are not easy to predict with accuracy. the future priorities of development tasks predicted in this presentation are subjective;that is, simply the author's view. While there will continue to be algorithm development to improve many aspects of tracking, the emphasis is expected to change in favor of expanded and new capabilities. this presentation will address not only those tracking algorithms that will have higher priority for continued algorithm development but also those aspects of tracking related processing that are expected to be of special interest. In addition, to facilitate this discussion, the categories of the state of the art of tracking are more finely decomposed. Many aspects of single sensor, multiple target tracking have matured during the last 20 years but room for improvement remains. In contrast, fusion of data from multiple distributed sensors is not as mature and interest is expected to continue to increase. Many fusion systems pose challenges that are not of much concern in tracking with data from a single sensor and algorithm development of those aspects of fusion will continue to be needed. the capability of the many functions and users of the output of trackers needs to be improved and expanded. Consequently, an increase is expected in the need to improve the content and quality of the output of both the single sensor and fusing trackers. the identified aspects of the new needs in tracker output are discussed and a dialog on this topic is encouraged.

关键词： multiple target tracking tracking with data from multiple sensors sensor data fusion network centric processing tracking open issues Bayesian methods Kalman filter

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Application of the Kalman-Levy filter for tracking maneuvering targets

Application of the Kalman-Levy filter for tracking maneuveri...

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16th conference on signal and data processing of small targets

作者： Sinha, A Kirubarajan, T Bar-Shalom, Y McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)081945351X

In target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an Interacting Multiple Model - IMM - estimator). the over-simplification resulting from the above assumptions can cause severe degradation in tracking performance. Of particular concern is the simplistic white noise or Wiener process acceleration models used to handle maneuvering targets. Presence of heavy-tailed noise in the observation process is another concern. In this paper we explore the application of Kalman-Levy filter to handle maneuvering targets. this filter assumes a heavy tailed noise distribution known as the Levy distribution. Unlike in the case of Gaussian distribution, the existence of the covariance is not guaranteed in this case. Due to the heavy tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of Kalman-Levy filter in non-maneuvering portion of track is worse than a Kalman filter's. this motivates us to develop an IMM estimator incorporating a Kalman filter and a Kalman-Levy filter. the performance of this filter is compared with an IMM estimator with two standard Kalman filters in a scenario from the 4th Navy tracking benchmark problem. It is found that the IMM estimator with a Kalman-Levy filter performs better than the other IMM estimator in both maneuvering and non-maneuvering portion of target flight.

关键词： maneuvering target tracking interacting multiple model filter Kalman filter Kalman-Levy filter heavy-tailed process noise

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Stereo vision for small targets in IR image sequences

Stereo vision for small targets in IR image sequences

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13th conference on signal and data processing of small targets

作者： Jutzi, B Gabler, R Jaeger, K FGAN FOM Res Inst Optron & Pattern Recognit D-76275 Ettlingen Germany

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.

关键词： IRST infrared stereo multiple target tracking point targets image sequence processing false alarms camera calibration projective reconstruction

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Feature aided tracking (FAT)

Feature aided tracking (FAT)

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16th conference on signal and data processing of small targets

作者： Singer, PF Coursey, AL Raytheon Co El Segundo CA 90245 USA

ISBN: (纸本)081945351X

Association of observations and tracks is a fundamental component of most solutions to the tracking problem. Association is frequently formulated as a multiple hypothesis test. Typically, the test statistic, called the track score, is the likelihood or likelihood ratio of the observations conditioned upon the association hypotheses. Assuming that the test is reasonably efficient, further reduction in the association error probability necessitates the introduction of additional information into the track score. this additional information is embodied in quantities called track features which are to be included in the track score. In practice, the necessary conditional probabilities of the track features are unknown. the class of non-parametric hypothesis tests is designed to provide such a test in the absence of any probabilistic information about the data. However, the test statistics used in non-parametric tests cannot be used directly in the track score. the one probabilistic quantity generally available with non-parametric tests is the Type I error probability, the probability of failing to accept a true hypothesis. If the non-parametric test is distribution free then the Type I error probability is independent of the distribution of the track features. this paper presents a distribution free, non-parametric test of the track features that can be used to test the association hypotheses and a quantity that can be included in the track score is derived from the Type I error probability of the test.

关键词： feature aided tracking non-parametric hypothesis test distribution free data association

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