Precision GMTI tracking using road constraints with visibility information and a refined sensor model

Mertens, Michael; Ulmke, Martin

doi:10.1109/radar.2008.4720781

Cited by 9 publications

(9 citation statements)

References 3 publications

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“…The measurement model, as discussed in Section II, includes two scenarios: measurements recorded and not recorded. As illustrated in Figure 3, when a measurement is recorded and the target's radial velocity is outsides the Doppler blindness constraint region (−κ, +κ) (here we followed [12] and set κ to be 3 m/s), the corresponding likelihood function is Gaussian as given by (2). In the simulation study, we followed [12] and set the parameters of the measurement equation, σr and σ θ , as 20 m and 0.001 rad respectively.…”

Section: A the State And Measurement Modelsmentioning

confidence: 99%

“…In this paper, we consider the problem of ground vehicle tracking using a Ground Moving Target Indicator (GMTI) radar which discriminates a moving target against the static background based on the Doppler effect [1]. GMTI radar is well suited for detecting targets moving on ground due to its wide-area, all-weather, day/night, and real-time capabilities [2]. Therefore, the GMTI-based tracking has received a wide range of applications in continuously tracking of vehicles to support the surveillance in different environments (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The incorporation of the stop model in the VS-IMM framework improves the tracking performance when no measurements are recorded due to vehicle stopping. On the other hand, there are some other studies such as [2], [8] and [9] that applied Gaussian mixture tracking algorithms by propagating the Gaussian mixture approximation to the conditional distribution of the target state. In addition, a suitable state-dependent detection probability was introduced, which helps to determine the conditional distribution of the target state when no measurements are recorded.…”

Section: Introductionmentioning

confidence: 99%

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An enhanced particle filtering method for GMTI radar tracking

Liu

et al. 2016

IEEE Trans. Aerosp. Electron. Syst.

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This paper investigates the problem of ground vehicle tracking with a Ground Moving Target Indicator (GMTI) radar. In practice, the movement of ground vehicles may involve several different manoeuvring types (acceleration, deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the ground vehicle is below a threshold, i.e. falling into the Doppler blind region. In this paper, to incorporate the information gathered from normal measurements and knowledge on the Doppler blindness constraint, we develop an enhanced particle filtering method for which the importance distributions are inspired by a recent noise related doppler blind (NRDB) filtering algorithm for GMTI tracking. Specifically, when constructing the importance distributions, the proposed particle filter takes the advantages of the efficient NRDB algorithm by applying the extended Kalman filter and its generalization for interval-censored measurements. In addition, the linearization and Gaussian approximations in the NRDB algorithm are corrected by the weighting process of the developed filtering method to achieve a more accurate GMTI tracking performance. The simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem.

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Section: A the State And Measurement Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An enhanced particle filtering method for GMTI radar tracking

Liu

et al. 2016

IEEE Trans. Aerosp. Electron. Syst.

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“…Measurements from a GMTI radar are used to discriminate a moving target from the stationary background, such as the range and azimuth angle of the tracked object. As mentioned in [1], a GMTI radar sensor is capable of detecting targets moving on the ground for wide-area, day/night and all weather conditions. Successful tracking of single/multiple ground vehicle(s) moving on a road is important for traffic surveillance.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian framework with an auxiliary particle filter for GMTI-based ground vehicle tracking aided by domain knowledge

Liu

Chen

et al. 2014

SPIE Proceedings

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In this work, we propose a new ground moving target indicator (GMTI) radar based ground vehicle tracking method which exploits domain knowledge. Multiple state models are considered and a Monte-Carlo sampling based algorithm is preferred due to the manoeuvring of the ground vehicle and the non-linearity of the GMTI measurement model. Unlike the commonly used algorithms such as the interacting multiple model particle filter (IMMPF) and bootstrap multiple model particle filter (BS-MMPF), we propose a new algorithm integrating the more efficient auxiliary particle filter (APF) into a Bayesian framework. Moreover, since the movement of the ground vehicle is likely to be constrained by the road, this information is taken as the domain knowledge and applied together with the tracking algorithm for improving the tracking performance. Simulations are presented to show the advantages of both the new algorithm and incorporation of the road information by evaluating the root mean square error (RMSE).

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“…However, there are scenarios in the urban environment where the direct path is lost, and the only paths available utilize multipath. Utilizing prior knowledge about the environment like imagery, road maps, and building locations, the multipath returns can be used to confirm the target detection [6]. Multipath propagation can be utilized [7], and target information can be extracted from multipath returns to enhance tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Radar surveillance in urban environments

Tahmoush

Silvious

Bender

2012

2012 IEEE Radar Conference

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Abstract-Radar surveillance in difficult environments like urban areas can be challenging due to large amounts of both multipath and clutter. Additionally, buildings and clutter like parked vehicles can produce shadowed areas where the line-ofsight is broken. We analyzed urban materials to determine how to utilize multipath to see into the shadows of urban environments, which polarization has the least loss, and which frequencies performed the best across a range of environments. Urban canyons were analyzed to determine whether there was more of a multipath effect on the measurements or a waveguide effect. The detection and tracking of non-line-of-sight moving objects using multiple bounces was attempted across a variety of urban building materials with an urban radar surveillance system. We demonstrate the detection and tracking of subjects using multipath returns, but there was no disambiguation of real or multipath sources. We also discuss the challenges of classification in an urban environment.

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Precision GMTI tracking using road constraints with visibility information and a refined sensor model

Cited by 9 publications

References 3 publications

An enhanced particle filtering method for GMTI radar tracking

An enhanced particle filtering method for GMTI radar tracking

A Bayesian framework with an auxiliary particle filter for GMTI-based ground vehicle tracking aided by domain knowledge

Radar surveillance in urban environments

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