Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software

By Yaakov Bar-Shalom, Xiaorong Li, T. Kirubarajan. 2001

5,004 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/275a7ee84df197beb51cae1c31cc57902243e82b

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In Artificial Intelligence

Citations in the corpus

Some Relations Between Extended and Unscented Kalman Filters (2012, background): “The result is in for instance [5] given without proof, so we include it here for completeness and for preparing for Theorem 2.”
Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees (2022, neutral): “Such algorithms utilize methods that are commonly used for target tracking and describe target trajectories using kinematic models (see e.g., Bar Shalom et al., 2004).”
Applied Stochastic Differential Equations (2019, neutral): “Stochastic differential equation models of this kind are commonly used in navigation and control systems (see, e.g., Jazwinski, 1970; Bar-Shalom et al., 2001; Grewal and Andrews, 2001).”
Survey of maneuvering target tracking. Part I. Dynamic models (2003, methodology): “…= 0 1 : (12) The direct discrete-time counterpart of the above model is [2] xk+1 = Fxk +Gwk = diag[Fcv,1]xk +diag[Gcv,T]wk = diag[F2,F2,1]xk +diag[G2,G2,T]wk (13) where Fcv = diag[F2,F2], Gcv = diag[G2,G2] F2 = 1 T 0 1 , G2 = T2=2 T (14) wk = [wx,wy,wz]k is a discrete-time white noise sequence and…”
Survey of Maneuvering Target Tracking. Part II: Motion Models of Ballistic and Space Targets (2010, methodology): “The unknown input u as well as the kinematic state variables is estimated in [57] by the well-known input-estimation method for adaptive filtering [14, 6, 63].”
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics (2021, methodology): “To tackle this problem we model the Segway kinematics (in each axis separately) using the linear Wiener velocity model, where the acceleration is modeled as a white Gaussian noise process wτ with variance q(2) [36]:”
Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models (2018, methodology): “The longitudinal parameters along the road X longR are predicted by the discrete Wiener process acceleration model [29], [30]”
The Rise of Radar for Autonomous Vehicles: Signal processing solutions and future research directions (2019, methodology): “Temporal filtering, which is usually implemented using Kalman, particle, or multimodal filters [23], further increases the fidelity of the output data by associating temporally close clusters into tracks.”
Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models (2014, background): “Kalman filters are influential in radar tracking applications, where wt represents the latent position of an object in space, the state-space model captures the assumed process by which the position moves—which may be more complex than the simple process laid out above—and the observations represent”
Tracking of extended objects and group targets using random matrices — a new approach (2008, methodology): “…the mean square error with corresponding estimation error variance P k | (cid:1) for an initial estimate x 0 | 0 with variance P 0 | 0 if one uses for prediction and (5) with the innovation y k − Hx k | k − 1 and the corresponding covariance as well as the Kalman gain for update (see, e. g., [10]).”
Bayesian approach to extended object and cluster tracking using random matrices (2008, neutral): “= B(Xk; (1)2 ±kjk¡1, 1 2 (ok¡1jk¡1¡ d¡ 1),Xk¡1jk¡1=±kjk¡1): (34) Via moment matching [1] and inspired by the discussion in [12, p.”
Measures of performance for evaluation of estimators and filters (2001, neutral): “Then, it can be shown [2] that var´RMSE ¾ µ µ ½ Å ¾ Å ½ var´´ Ü ¼ Üµ µ µ ¾ tr´¦¦µ · ¼ ¦ ´ ¼ µ ¾ Å (7) In this case, RMSE ¾ is a scaled sum of (dependent and noncentral) chi-square RVs, which has a complex distribution, in general not chi-square unless components of Ü are independent.”
Survey of maneuvering target tracking. Part V. Multiple-model methods (2005, methodology): “” A detailed design and evaluation of an IMM algorithm with a two-model (CV-CT) configuration was given in [199] (see also [18] and [21]), along with many guidelines and insights on parameter selection and tuning of the algorithm.”
ESTIMATOR'S CREDIBILITY AND ITS MEASURES (2002, methodology): “To our knowledge, the most notable prior publications with a considerable amount of treatment of the issue of credibility are (Bar-Shalom and Birmiwal, 1983; Bar-Shalom et al., 2001), referred to as (finite-sample) consistency.”
Shifted Rayleigh filter: a new algorithm for bearings-only tracking (2007, background): “The ‘density’ category, which contains particle filters [3] , IMM filters [4], and the range-parameterized extended Kalman filt er [5], comprises algorithms that aim at direct approximation of the conditional densities of the state.”