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检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2000"

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A flexible implementation for Doppler radar to verify various base-band array signal processing algorithms - art. no. 591307

A flexible implementation for Doppler radar to verify variou...

引用

17th conference on signal and data processing of small targets

作者： Yang, E Lee, J Jung, B Chun, J Korea Adv Inst Sci & Technol Comp Sci Lab Taejon 305701 South Korea

ISBN: (纸本)0819459186

We describe a flexible hardware system of the Doppler radar which. is designed to verify various baseband array signal processing algorithms. In this work we design the Doppler radar system simulator for baseband signal processing in laboratory level. Based on this baseband signal processor, a PN-code pulse doppler radar simulator is developed. More specifically, this simulator consists of an echo signal generation part and a signal processing part. For the echo signal generation part, we use active array structure with 4 elements, and adopt baker coded PCM signal in transmission and reception for digital pulse compression. In the signal processing part, we first transform RF radar pulse to the baseband signal because we use the basebands algorithms using IF sampling. Various digital bearnforming algorithms can be adopted as a baseband algorithm in our simulator. We mainly use Multiple Sidelobe Canceller (MSC) with main array antenna elements and auxiliary antenna elements as beamforming and sidelobe canceller algorithm. For Doppler filtering algorithms, we use the FFT. A control set is necessary to control overall system and to manage the timing schedule for the operation.

关键词： Doppler radar FFT beamforming sidelobe canceller CFAR pulse compression

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Tracking multiple targets with coordinated turn maneuvers

Tracking multiple targets with coordinated turn maneuvers

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17th conference on signal and data processing of small targets

作者： Morelande, MR Gordon, NJ Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

ISBN: (纸本)0819459186

The problem of tracking multiple maneuvering targets is considered. The usual multiple model approach is adopted in which maneuvering target motion is modeled by assuming that the target motion at each point in time can be described by one of a finite set of dynamic models. Transitions between each mode of target motion are assumed to be Markovian. Target positions are measured in polar coordinates leading to a nonlinear measurement equation. A particle filter is proposed as a solution to the problem. The proposed algorithm seeks to improve upon the performance of a previously proposed particle filter by using measurement-directed proposals and exploiting the structure of the measurement likelihood. The performance analysis focuses on targets which perform coordinated turn maneuvers. An improved model for target motion in this regime is suggested. The performance analysis, using Monte Carlo simulations, demonstrates the improved performance of the proposed algorithm compared to the previously proposed particle filter and the standard Gaussian approximation, the IMM-JPDAF.

关键词： Motion models Particles Detection and tracking algorithms Particle filters Monte Carlo methods Target detection Stochastic processes Sensors Electronic filtering data modeling

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Poisson models for extended target and group tracking

Poisson models for extended target and group tracking

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17th conference on signal and data processing of small targets

作者： Gilholm, K Godsill, S Maskell, S Salmond, D QinetiQ Farnborough GU1 0LX Hants England

ISBN: (纸本)0819459186

It is common practice to represent a target group (or an extended target) as set of point sources and attempt to formulate a tracking filter by constructing possible assignments between measurements and the sources. We suggest an alternative approach that produces a measurement model (likelihood) in terms of the spatial density of measurements over the sensor observation region. In particular, the measurements are modelled as a Poisson process with a spatially dependent rate parameter. This representation allows us to model extended targets as an intensity distribution rather than a set of points and, for a target formation, it gives the option of modelling part of the group as a spatial distribution of target density. Furthermore, as a direct consequence of the Poisson model, the measurement likelihood may be evaluated without constructing explicit association hypotheses. This considerably simplifies the filter and gives a substantial computational saving in a particle filter implementation. The Poisson target-measurement model will be described and its relationship to other filters will be discussed. Illustrative simulation examples will be presented.

关键词： target tracking extended targets Poisson models particle filters

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A multiple model SNR/RCS likelihood ratio score for radar-based feature-aided tracking

A multiple model SNR/RCS likelihood ratio score for radar-ba...

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17th conference on signal and data processing of small targets

作者： Slocumb, BJ Klusman, ME Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

Most approaches to data association in target tracking use a likelihood-ratio based score for measurement-to-track and track-to-track matching. The classical approach uses a likelihood ratio based on kinematic data. Feature-aided tracking uses non-kinematic data to produce an "auxiliary score" that augments the kinematic score. This paper develops a non-kinematic likelihood ratio score based on statistical models for the signal-to-noise (SNR) and radar cross section (RCS) for use in narrowband radar tracking. The formulation requires an estimate of the target mean RCS, and a key challenge is the trackina of the mean RCS through significant "jumps" due to aspect dependencies. A novel multiple model approach is used track through the RCS jumps. Three solution are developed: one based on an a-filter, a second based on the median filter, and the third based on an IMM filter with a median pre-filter. Simulation results are presented that show the effectiveness of the multiple model approach for tracking through RCS transitions due to aspect-angle changes.

关键词： feature-aided tracking RCS tracking multiple model signal score swerling fluctuation

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A range rate based detection technique for tracking a maneuvering target

A range rate based detection technique for tracking a maneuv...

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17th conference on signal and data processing of small targets

作者： Ru, JF Chen, HM Li, XR Chen, GH Univ New Orleans Dept Elect Engn New Orleans LA 70148 USA

ISBN: (纸本)0819459186

In this paper a novel approach for detecting unknown target maneuver using range rate information is proposed based on the generalized Page's test with the estimated target acceleration magnitude. Due to the high nonlinearity between the range rate measurement and the target state, a measurement conversion technique is used to treat rancre rate as a linear measurement in Cartesian coordinates so that a standard Kalman filter can be applied. The detection performance of the proposed algorithm is compared with that of existing maneuver detectors over various target maneuver motions. In addition, a model switching tracker based on the proposed maneuver detector is compared with the state-of-the-art IMM estimator. The results indicate the effectiveness of the maneuver detection scheme which simplifies the tracker design. The tracking performance is also evaluated using a steady state analysis.

关键词： Target detection Motion models Sensors Error analysis Filtering (signal processing) Detection and tracking algorithms Switching Motion estimation Performance modeling Radar

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Performance evaluation of time-weighted backvalues least squares vs. NOGA track estimators via sensor data fusion and track fusion for small target detection applications - art. no. 59130Z

Performance evaluation of time-weighted backvalues least squ...

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17th conference on signal and data processing of small targets

作者： Imam, N Barhen, J Glover, CW Oak Ridge Natl Lab Comp Sci & Math Div Ctr Engn Sci Adv Res Oak Ridge TN 37831 USA

ISBN: (纸本)0819459186

The rapid growth and increasing sophistication of airborne surveillance technology have spurred intense research efforts in the development and implementation of tracking algorithms capable of processing a large number of targets using multi-sensor data. In this paper, a, novel tracking algorithm, the NOGA tracker, is presented and compared with the more conventional Time-Weighted Backvalues Least Squares (TWBLS) estimator for accuracy (numerical and phenomenological), ease of implementation, and time performance. The NOGA tracker combines model predictions and sensor measurements to produce best estimates for quantities of interest. The state estimator model used for NOGA is a simple second order auto-regression which is combined with an uncertainty reduction scheme involving nonlinear Lagrange optimization process in which the inverse of a global covariance matrix is used as the natural metric for the Bayesian inference that underlies the combining process. The NOGA tracker explicitly incorporates sensor and model uncertainties in the estimation process, and uses model sensitivities to propagate the associated covariance matrices accurately and in a systematic way.

关键词： Uncertainty reduction track fusion sensor fusion multi-sensor tracking auto-regressive

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Complexity reduction in MHT/MFA tracking

Complexity reduction in MHT/MFA tracking

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17th conference on signal and data processing of small targets

作者： Poore, AB Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

The performance advantage of the multiple frame data association methods over the single frame methods follows from the ability to hold difficult decisions in abeyance until more information is available and the opportunity to change past decisions to improve current decisions. In dense tracking environments the performance improvements of multiple frame methods over single frame methods is very significant, making it the preferred solution for difficult tracking problems. The price that one pays for this performance gain is the computational complexity (NP-hard) of the resulting data association problem. The number of strings of data or arcs that one forms for N frames of data each containing M reports is M(N) wherein we have omitted missed reports. This number grows exponentially with the length of the window (i.e., N). Thus, much preprocessing is required to manage memory usage and to achieve good runtime performance. The control of the computations makes use of a variety of techniques including a) bin gating, b) coarse pair and triple point dynamic gating, c) multiframe gating, d) medium gating based on filter prediction gates, e) fine gating based on likelihood ratios, f) track hypothesis pruning, h) problem partitioning, and i) cluster tracking. While this work comments on many of these methods, the goal is to derive methods bin, coarse pair, and multiframe gating in a simple setting as possible.

关键词： Detection and tracking algorithms Sensors Electronic filtering 3D metrology Computing systems Mahalanobis distance Time metrology Calculus data processing signal processing

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A study of factors affecting multiple target tracking with a pixelized sensor - art. no. 59130L

A study of factors affecting multiple target tracking with a...

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17th conference on signal and data processing of small targets

作者： Morelande, MR Kreucher, CM Kastella, K Univ Melbourne Dept Elect & Elect Engn Parkville Vic 3010 Australia

ISBN: (纸本)0819459186

Factors affecting the performance of an algorithm for tracking multiple targets observed using a pixelized sensor are studied. A pixelized sensor divides the surveillance region into a grid of cells with targets generating returns on the grid according to some known probabilistic model. In previous work an efficient particle filtering algorithm was developed for multiple target tracking using such a sensor. This algorithm is the focus of the study. The performance of the algorithm is affected by several considerations. The pixelized sensor model can be used with either thresholded or non-thresholded measurements. While it is known that information is lost when measurements are thresholded, quantitative results have not been established. The development of a tractable algorithm requires that closely-spaced targets are processed jointly while targets which are far apart are processed separately. Selection of the clustering distance involves a trade-off between performance and computational expense. A final issue concerns the computation of the proposal density used in the particle filter. Variations in a certain parameter enable a trade-off between performance and computational expense. The various issues are studied using a mixture of theoretical results and Monte Carlo simulations.

关键词： Particles Detection and tracking algorithms signal to noise ratio Sensors Algorithm development Monte Carlo methods Surveillance Particle filters Target detection Solids

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Network centric MFA tracking architecture based on soft-level data association - art. no. 591316

Network centric MFA tracking architecture based on soft-leve...

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17th conference on signal and data processing of small targets

作者： Novoselov, R Gadaleta, S Poore, A Numerica Corp Ft Collins CO 80527 USA

ISBN: (纸本)0819459186

This work presents a new network centric architecture for multiple frame assignment (MFA) tracking. The architecture improves on earlier network tracking schemes by allowing trackers to broadcast decisions about their local soft-level associations, via the Soft Associated Measurement Reports (SAMRs). The SAMR may be followed by an "Oops" message, if the soft association was incorrect and must be revoked. We show, however, that such revocations are very rare in most scenarios. This paper discusses the implementation of the new algorithm and presents simulation results. Considerable improvements in the consistency of the air picture are demonstrated, owing to the the reduced latency in transmission of measurement-to-track associations. The earlier network architectures, namely, the Centralized MFA, the Replicated Centralized MFA, and the Network MFA on Local and All data, are also discussed in this work, as they form the foundation for the "Oops" algorithm.

关键词： Multiple hypothesis tracking multiple frame assignment distributed tracking architecture

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Comparison of LMITS and MHT algorithms

Comparison of LMITS and MHT algorithms

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17th conference on signal and data processing of small targets

作者： Musicki, D Mallick, M La Scala, B Strange, S Evans, R Univ Melbourne Dept Elect & Elect Engn Melbourne Syst Lab Parkville Vic 3010 Australia

ISBN: (纸本)0819459186

The Multiple Hypotheses Tracking (MHT) algorithm has been shown to have the best tracking performance among existing multi-target tracking algorithms using real world sensors with probability of detection less than unity and in the presence of false alarms. The improved performance of the Multiple Hypotheses Tracking comes at the cost of significantly higher computational complexity. Most Multiple Hypotheses Tracking implementations only form the best global hypothesis. This paper compares the Linear Multitarget Integrated Track Splitting (LMITS) tracking algorithm with the Multiple Hypotheses Tracking algorithm. LMITS has a simpler structure than Multiple Hypotheses Tracking as it decouples local hypotheses and avoids the measurement to multi-track allocation entirely. The number of LMITS hypotheses equals the sum of the number of local hypotheses added to the number of initiation hypotheses. Thus LMITS can retain a deeper hypotheses subtree which can result in better performance. We compare tracking performances of LMITS and MHT algorithms using simulated data for multiple maneuvering targets in heavy and non-uniform clutter.

关键词： multi-target multi sensor tracking multiple hypotheses tracking (MHT) linear multitarget (LM) integrated track splitting (ITS) data association

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