A Real-Time Statistical Radar Target Model

Sandhu, G.S.; Saylor, Annie V.

doi:10.1109/taes.1985.310637

Cited by 21 publications

(6 citation statements)

References 6 publications

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“…With the target DXF model (perfect conductor) and Greco method, the back-scattering fields are simulated by (5), Where the scattering field of the nth surface and the nth edge are represented by E s n and E d n , and the total scattering field is their vectorial sum by (6):…”

Section: Greco Algorithm Explanationmentioning

confidence: 99%

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The Verification of Chaotic Characteristics of Radar Angular Glint

Miao¹

2012

PIER B

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Abstract-In this paper, we present the chaotic verification for angular glint of complex radar target. Angular glint is a key factor in the generation loss probability in radar detections, and the intrinsic physical characteristic and suppression techniques of glint have been a hot topic in radar signal analysis. In this paper, the radar angular glint samples of a typical complex target are calculated by the Greco method based on Phase Gradient method. The simulated glint series fit the prerequisites of chaos for deterministic, nonlinear generation and no regularities in time domain, therefore the analysis the chaotic traits is required. We propose the design of chaotic verification flow, which is proved to be efficient and effective by the experiment of Lorenz Attractor signal model, and the details have been explained. The algorithm flow begins with the determination of optimum time lag and minimum embedding dimension, and is followed by the timedelay reconstruction in phase space. The results are presented with three qualitative verification results of attractor geometry, Poincare section and principal component analysis and two quantitative results of correlation dimension and largest Lyapunov exponent for the glint series. With comparison with results obtained by Lorenz attractor, the chaotic traits of angular glint data are verified. Therefore, the paper has proposed new possible reduction and prediction ideas to refrain angular glint in the digital processing unit of radar receiver in the future.

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Section: Greco Algorithm Explanationmentioning

confidence: 99%

“…Researchers have gained an insight into the relationship between RCS and angular glint. Some drew the conclusion that: (1) the two are negatively correlative; (2) they are neither correlative nor independent [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

The Verification of Chaotic Characteristics of Radar Angular Glint

Miao¹

2012

PIER B

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“…In this example, a scenario of great practical interest in which the statistics of the glint noise and the target maneuver are correlated, was tested. In target tracking, changes in the target aspect with respect to the radar due to maneuver dramatically increases the radar cross section uctuations resulting in significant glint noise [20], [53]- [57]. Therefore, glint noise increases dramatically during the maneuver.…”

Section: B4 Maneuvering Target Tracking In the Presence Of Correlatementioning

confidence: 99%

MMSE-Based Filtering for Linear and Nonlinear Systems in the Presence of Non-Gaussian System and Measurement Noise

Bilik¹,

Tabrikian²

2009

Kalman Filter Recent Advances and Applications

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coordinates, while the observation model is in polar coordinates. There is no general analytic expression for the posterior PDF in nonlinear problems and only suboptimal estimation algorithms have been studied [1]. The extended Kalman filter (EKF) is the most popular approach for recursive nonlinear estimation [8], [9]. The main idea of the EKF is based on a first-order linearization of the model where the posterior PDF and the system and measurement noises are assumed to be Gaussian. The nonlinearity of the measurement model leads to non-Gaussian, multi-modal PDF of the system state, even when the system and the measurement noises are Gaussian. The Gaussian approximation of this multi-modal distribution leads to poor tracking performance. The unscented Kalman filter (UKF) approximates the PDF at the output of the nonlinear transformation using deterministic sampling [10]-[11]. The advantage of the UKF over the EKF stems from the fact that it does not involve approximation of the nonlinear model per se [12], [13]. The UKF provides an unbiased estimate, however its convergence is slow [13]. Many researchers addressed the problem of filtering in non-Gaussian models. One of the effective algorithms in the non-Gaussian problems is the Masreliez filter [14], [15] that employs a nonlinear "scorefunction", calculated from known a-priori noise statistics. The score-function is customized for the noise statistics and has to be redesigned for each application. The main disadvantage of this approach is that it involves a computationally expensive score function calculation [6]. In [16], the Masreliez filter was used in the target tracking problem with glint noise. Recently, a few new filtering approaches have been proposed for the problem of target tracking. One of them is the multiple modeling (MM) approach, in which the time-varying motion of the maneuvering target is described by multiple models [17]. In this approach, the non-Gaussian system is represented by a mixture of parallel Gaussian-distributed modes [8]. Using the Bayesian framework, the posterior PDF of the system state is obtained as a mixture of conditional estimates with a-priori probabilities of each mode [18]. Various filters are used for mode-conditioned state estimation. For example, the Gaussian sum filter (GSF), was implemented in [8], [19] using a bank of KFs. The EKF and Masreliez filters were used as mode-conditioned filters for the nonlinear problems of target tracking in [6], [16], [20]. The main drawback of the MM approach is the exponential growth of the number of the modes, and exponentially increasing number of mode-conditioned filters [18], [21]. Therefore, optimal algorithms, such as the GSF, are impractical. The direct approximation methods for target tracking in the presence of clutter with GMM distribution approximation were proposed in [22]-[28]. The joint probabilistic data association (JPDA) [18] and global nearest neighbor (GNN) [25] approximate the entire GMM by a single Gaussian, loosing important information contained in other mixt...

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“…It is feasible to compute target glint based on this kind of approximation. G. S. Sandhu proved the feasibility of this method through experiments [2]. Suppose that target consists of N independent scatterers shown as …”

Section: Glint Modelmentioning

confidence: 95%

Research on high accuracy angle measuring technology of MMW monopulse radar

Zhao

IEEE International Radar Conference, 2005.

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Frequency diversity plus amplitude weighting is a common way to suppress glint.In this paper, it is demonstrated that square amplitude weighting is optimal among all integer power amplitude weighting methods. For monopulse radar, common noncoherent-integration anglemeasuring method is equivalent to the linear weighting on the result of single pulse angle measuring by signal amplitude of sum channel. A new noncoherent-integration monopulse radar angle measuring algorithm based on square amplitude weighting is developed and its performance is verified through computer simulation to be prior to common noncoherentintegration angle measuring method.

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A Real-Time Statistical Radar Target Model

Cited by 21 publications

References 6 publications

The Verification of Chaotic Characteristics of Radar Angular Glint

The Verification of Chaotic Characteristics of Radar Angular Glint

MMSE-Based Filtering for Linear and Nonlinear Systems in the Presence of Non-Gaussian System and Measurement Noise

Research on high accuracy angle measuring technology of MMW monopulse radar

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