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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 1999"

共 764 条记录，以下是481-490 订阅

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On track and tracklet fusion filtering

On track and tracklet fusion filtering

引用

14th conference on signal and data processing of small targets

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

ISBN: (纸本)0819444782

Track and tracklet fusion filtering is complicated because the estimation errors of tracks from two sources for the same target may be cross-correlated. This cross-correlation of these errors should be taken into account when designing the filter used to combine the track data. This paper addresses the various track and tracklet fusion methods and their impact on communications load and tracking characteristics. In track and tracklet fusion a sequence of measurements is processed at the sensor or platform level to form tracks;then sensor level track data (in the form of tracks or tracklets) for a target is distributed and fused with each other or with a global track. Track Fusion and Tracklet Fusion are also sometimes called Hierarchical Fusion, Federated Fusion, or Distributed Fusion. Track data can include features or other information useful for target classification. One characteristic of track and tracklet fusion that distinguishes one method from another is whether the local track data is combined or if global tracks are maintained and updated using the sensor track data. Another important issue is whether there is filter process noise to accommodate target maneuvers. Some filtering methods are designed for maneuvering targets and others are not. This paper enumerates the various track and tracklet fusion methods for processing data from distributed sensors and their impact on filter performance and communications load.

关键词： target tracking multiple sensor tracking track fusion multiple target tracking sensor data fusion Bayesian methods tracklet distributed tracking estimation and filtering

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Expected likelihood for tracking in clutter with particle filters

Expected likelihood for tracking in clutter with particle fi...

引用

14th conference on signal and data processing of small targets

作者： Marrs, A Maskell, S Bar-Shalom, Y QinetiQ Ltd Malvern Technol Ctr Malvern Worcs England

ISBN: (纸本)0819444782

The standard approach to tracking a single target in clutter, using the Kalman filter or extended Kalman filter, is to gate the measurements using the predicted measurement covariance and then to update the predicted state using probabilistic data association. When tracking with a particle filter, an analog to the predicted measurement covariance is not directly available and could only be constructed as an approximation to the current particle cloud. A common alternative is to use a form of soft gating, based upon a Student's-t likelihood, that is motivated by the concept of score functions in classical statistical hypothesis testing. In this paper, we combine the score function and probabilistic data association approaches to develop a new method for tracking in clutter using a particle filter. This is done by deriving an expected likelihood from known measurement and clutter statistics. The performance of this new approach is assessed on a series of bearings-only tracking scenarios with uncertain sensor location and non-Gaussian clutter.

关键词： tracking probabilistic data association particle filters

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Adaptive mean and variance filter for detection of dim point-like targets

Adaptive mean and variance filter for detection of dim point...

引用

14th conference on signal and data processing of small targets

作者： Lim, ET Shue, L Venkarteswarlu, R Nanyang Technol Univ Ctr Signal Proc Singapore 639798 Singapore

ISBN: (纸本)0819444782

The problem of detecting small target in IR imagery has attracted much research effort over the past few decades. As opposed to early detection algorithms which detect targets spatially in each image and then apply tracking algorithm, more recent approaches have used multiple frames to incorporate temporal as well as spatial information. They often referred to as "track before detect" algorithms. This approach has shown promising results particularly for detection of dim point-like targets. However, the computationally complexity has prohibited practical usage for such algorithms. This paper presents an adaptive, recursive and computation efficient detection method. This detection algorithm updates parameters and detects occurrence of targets as new frame arrived without storing previous frames, thus achieved recursiveness. Besides, the target temporal intensity change is modeled by two Gaussian distribution with different mean and variance. The derivation of this generalized model has taken account of the wide variation of target speed, therefore detects wider range of targets.

关键词： small target temporal filter triple temporal filter continuous wavelet transform

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Adaptive sensor management in target tracking

Adaptive sensor management in target tracking

引用

14th conference on signal and data processing of small targets

作者： Sworder, DD Boyd, JE Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)0819444782

Model-based. path estimation algorithms are commonly derived from a linear kinematic equation and an expansive observation model. The premise that the measurement region is all-inclusive simplifies the performance analysis, but it does not describe the relevant characteristics of some sensors. Most sensors have an active region. If the region contains, the target. a noisy location measurement is made. Alternatively, no location measurement is returned. There is a clear tradeoff between measurement acuity and broad coverage. A sensor that adapts its active region to the most likely target location should achieve higher quality tracking and identification. However. effective utilization of this flexibility requires an accurate determination of the conditional distribution of the target-state. In this paper, the Gaussian Wavelet Estimator is used in an adaptive algorithm for sensor management. It is shown that the adaptive window provides performance that is superior to that achieved using a fixed window of comparable cover probability.

关键词： nonlinear filters target tracking data fusion

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Track-to-track association and bias removal

Track-to-track association and bias removal

引用

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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Passive relative ranging applied to sensor-to-sensor data association

Passive relative ranging applied to sensor-to-sensor data as...

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14th conference on signal and data processing of small targets

作者： Grantham, JL Brahm, SJ Zounes, RS Whitt, E Comp Sci Corp Huntsville AL 35802 USA

ISBN: (纸本)0819444782

Ballistic Missile Defense (BMD) effectiveness depends on a system's capability to acquire, track, identify, and engage threat missiles. The probability of a successful engagement can be improved by performing multiple-sensor data fusion, especially if the participating sensor systems are based on both radar frequency (RF) and infrared (IR) phenomenology. In this paper, we apply this observation to the "Target Object Map (TOM) correlation problem" for the standard configuration of a kill vehicle (with a single or multicolor IR seeker) receiving uplinks from a ground based radar. Specifically, we examine the application of a relative ranging technique that augments the angles-only track information of a passive IR sensor with non-parametric range-ranking of the threat complex. Since data association performance is significantly better for three-dimensional (3-D) matching than for two-dimensional (2-D) matching, the idea is to take advantage of relative range-ranking information of the threat complex to potentially improve performance. Numerous techniques that attempt to extract absolute range estimates from a passive IR sensor have been investigated by researchers in the BMD community and it is understood that range information allows for improved threat tracking, radiant intensity estimates, and data association performance. However, extracting absolute target range estimates from irrdiance measurements is extremely difficult because of the presence of data uncertainties / ambiguities, environment and sensor noises, and small angular rates of tracked objects. Passive Relative Ranging (PRR) is distinct in that it focuses on the "relative" range-ranking of objects;knowledge that one object is closer than a second object, while not relevant for improving track or intensity estimation performance, can possibly improve the performance of sensor-to-sensor object assignment. The proposed PRR technique is based on the physical range-squared relationship between intensity and

关键词： passive relative ranging data association passive ranging target object map Cramer-Rao bound

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Efficient multi-sensor resource management using Cramer-Rao lower bounds

Efficient multi-sensor resource management using Cramer-Rao ...

引用

14th conference on signal and data processing of small targets

作者： Hernandez, ML Kirubarajan, T Bar-Shalom, Y QinetiQ Ltd Malvern Technol Ctr Malvern WR14 3PS Worcs England

ISBN: (纸本)0819444782

This paper describes the development of a general framework for the efficient management of multiple sensors in target tracking. The basis of the technique is to quantify, and subsequently control, the accuracy of target state estimation. The Posterior Cramer-Rao lower bound provides the means of achieving this aim by enabling us to determine a bound on the performance of all unbiased estimators of the unknown target state. The general approach is then to use optimization techniques to control the measurement process in order to achieve accurate target state estimation. We are concerned primarily with the deployment and utilization of a limited sensor resource. We also allow for measurement origin uncertainty, with sensor measurements either target generated or false alarms. We exploit previous work to determine a general expression for the Fisher Information Matrix in this case. We show that by making certain assumptions we can express the measurement uncertainty as a constant information reduction factor. This enables the Fisher Information Matrix to be calculated quickly, allowing Cramer-Rao bounds to be utilized for real-time, online sensor management. An example is presented in which the aim is to track a submarine by deploying a series of passive sonobuoys. The target moves according to a constant velocity model, and each sonobuoy provides bearings-only measurements. We show that the sensor management framework determines quick deployment strategies. This enables the target to be accurately localized, and at the same time makes efficient use of the limited sensor resource.

关键词： sensor management data fusion Posterior Cramer-Rao lower bound Fisher Information Matrix target state estimation

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Tracking with asynchronous multiple sensors

Tracking with asynchronous multiple sensors

引用

13th conference on signal and data processing of small targets

作者： Gray, JE McCabe, DH USN Ctr Surface Warfare Dahlgren Div Syst Res & Technol Dept Dahlgren VA 22448 USA

ISBN: (纸本)0819441872

Sensor data fusion has long been recognized as a means to improve target tracking. Common practice assumes that the sensors used are synchronous (i.e., perform the, same operation at the identical time), take measurements at the same time and have no communication delays between sensor platforms and the central processing center. Such assumptions are not valid in practice. This paper removes these assumptions when dealing with multisensor target tracking. In particular, it assumes that the sensors used can have different data rates and communication delays between local and central platforms. A new tracking algorithm using asynchronous sensors is proposed and derived in this paper.

关键词： Filtering (signal processing) Lithium Sensors Silicon Zinc Electronic filtering Lutetium data communications data processing signal processing

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Use of joint data association probabilities for covariance consistency

Use of joint data association probabilities for covariance c...

引用

13th conference on signal and data processing of small targets

作者： Blair, WD Brown, GC Georgia Inst Technol Georgia Tech Res Inst Atlanta GA 30332 USA

ISBN: (纸本)0819441872

Covariance consistency is a critical element of a robust target tracking system. Target maneuvers and measurement origin uncertainty pose significant challenges to a tracking algorithm achieving covariance consistency. The Interacting Multiple Model (IMM) estimator is a nearly consistent estimator for tracking maneuvering targets. While the Probabilistic data Association Filter (PDAF) achieves covariance, consistency for a single target in presence of false alarms, achieving covariance consistency while tracking multiple closely-spaced targets is an open Issue. When using an unique assignment technique for associating measurements-to-tracks, the measurements are typically processed with the track filter as if the measurement-to-track association probabilities are unity for each measurement-track pair. This processing of the measurements results in poor covariance consistency for closely-spaced targets. In this paper, the use of approximate association probabilities for each measurement-to-track pair is proposed for the unique assignments and included in the track filter processing of the measurement to enhance the covariance consistency for closely-spaced targets.

关键词： tracking data association radar systems

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Multi-level thresholding and confidence testing detection algorithm for small moving target in image sequences

Multi-level thresholding and confidence testing detection al...

引用

13th conference on signal and data processing of small targets

作者： Chen, HM Sun, GF Lu, HZ Qing, C Natl Univ Def Technol ATR Lab Changsha Hunan Peoples R China

ISBN: (纸本)0819441872

This paper describes a new method of small target detection according to the features of small moving targets. This method bases on multi level threshold decision-making and sliding trajectory confidence testing technology. Energy accumulating, multi-level thresholding, trajectory searching and matching, and trajectory confidence testing are combined in this method. The detection resolves the contrary requirement of real-time processing, large data throughput, high detection capability in small moving target detection in IR image sequences. Experiments have been conducted, the results show that the algorithm has advantages of high detection probability, simple structure, and excellent real time performance.

关键词： image sequences small moving target detection multi-level thresholding Trajectory Confidence Testing

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