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Joint detection and tracking of unresolved targets with a monopulse radar using a particle filter - art. no. 59131T

Joint detection and tracking of unresolved targets with a mo...

引用

17th conference on signal and data processing of small targets

作者： Nandakumaran, N Sinha, A Kirubarajan, T McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)0819459186

Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. In this paper, a new algorithm is developed to jointly detect and track more than two targets from a single detected bin. this method involves the use of tracking data in detection. For this purpose, target states are transformed into detection parameter space, which involves high nonlinearity. In order to handle this, the sequential Monte Carlo (SMC) method, which is proved to be effective for nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard SMC steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. It in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. that is, we evaluate the likelihoods of different hypotheses of possible combinations of targets being in different detected bins. the hypothesis testing is used to find the correct detection event. the particles are updated and resampled according to the hypothesis that has the highest likelihood (score). A simulated amplitude comparison monopulse radar is used to generate the data with more than unresolved two targets. Simulation results confirm the possible extraction and tracking of more than two targets jointly.

关键词： U nresolved targets closely-spaced targets monopulse radar multitarget tracking particle filters

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Efficiently identifying close track/observation pairs in continuous timed data

Efficiently identifying close track/observation pairs in con...

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17th conference on signal and data processing of small targets

作者： Kubica, J Moore, A Connolly, A Jedicke, R Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)0819459186

In this paper we examine new data structures and algorithms for efficient and accurate gating and identification of potential track/observation associations. Specifically, we focus on the problem of continuous timed data, where observations arrive over a range of time and each observation may have a unique time stamp. For example, the data may be a continuous stream of observations or consist of many small observed subregions. this contrasts with previous work in accelerating this task, which largely assumes that observations can be treated as arriving in batches at discrete time steps. We show that it is possible to adapt established techniques to this modified task and introduce a novel data structure for tractably dealing with very large sets of tracks. Empirically we show that these data structures provide a significant benefit in both decreased computational cost and increased accuracy when contrasted with treating the observations as if they occurred at discrete time steps.

关键词： data association algorithms data structures

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data association in target tracking using priority queues

Data association in target tracking using priority queues

引用

17th conference on signal and data processing of small targets

作者： Sinha, A Kirubarajan, T Farooq, M McMaster Univ Dept Elect & Comp Engn ETFLab Hamilton ON Canada

ISBN: (纸本)0819459186

data association is one of the main components of target tracking. While, in its simplest form, data association links a list of tracks to a list of measurements or links two lists of measurements (2-D association), the more complex problem involves assignment of multiple number of such lists (S-D association where S > 3). In target tracking, the presence of false detections (false alarms) and the absence of detections from some targets (missed detections) complicate the problem of data association further. In this work, we explore the possibility of applying track ordering in priority queues to solve the association problem more efficiently. the basic component of our algorithm is to form priority queues by permutations of the tracks. Each queue is served on a first-come-first-served basis, i.e., each track is assigned to the best measurement available based on its turn in the queue. It can be shown that the best solution to the 2-D problem can be obtained from one of these queues. However, the solution is computationally expensive even for a moderate number of targets. In this paper we show that due to redundancy only a small fraction of the total number of permutations is required to be evaluated to obtain the best solution.

关键词： Multisensor-multitarget tracking 2-D assignment algorithms priority queue m-best solutions

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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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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...

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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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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 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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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...

引用

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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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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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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