F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P-J Nordlund, "Particle Filters for Positioning, Navigation and Tracking," IEEE Transactions on Signal Processing, Feb 2002. Home/Search Document Details and Download
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A Sequential Monte Carlo Filter for Joint Linear/Nonlinear State.. - Iltis (Correct)
....Kalman filters. Hence, the SMC KF is best viewed as a synthesis of conditional Gaussian SIS [8] and measurement linearization methods [12] 14] tailored to DS CDMA channel estimation. The SMC KF resembles several other algorithms in the literature, but with key differences. For example, in [15], the problems of tracking and navigation are considered when the obser vations are nonlinear in the position part of the state vector. The nonlinear (position) state variables are estimated via a Sampling Importance Resampling (SIR) technique, whereas the ve locity acceleration (linear) ....
....are nonlinear in the position part of the state vector. The nonlinear (position) state variables are estimated via a Sampling Importance Resampling (SIR) technique, whereas the ve locity acceleration (linear) variables are tracked via a conventional Kalman filter. However, the algorithm in [15] assumes that the observations are independent of the linear velocity acceleration variables. Hence, the partitioning approach in [15] is not directly applicable to the DS CDMA model, whose measurements depend on both delay (nonlinear) and channel (linear) state vari ables. Similarly, the idea of ....
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F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P. Nordlund, "Particle filters for positioning, navigation, and tracking," IEEE Transactions on Signal Processing, vol. 50, pp. 425- 435, Feb. 2002.
Particle Filter for Underwater Terrain - Navigation Rickard Karlsson Self-citation (Gustafsson Karlsson) (Correct)
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F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P-J Nordlund, "Particle Filters for Positioning, Navigation and Tracking," IEEE Transactions on Signal Processing, Feb 2002.
Particle Filter For Underwater Terrain Navigation - Rickard Karlsson And Self-citation (Gustafsson Karlsson) (Correct)
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F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P-J Nordlund, "Particle Filters for Positioning, Navigation and Tracking," IEEE Transactions on Signal Processing, Feb 2002.
Particle Filtering and Cramér-Rao Lower Bound.. - Karlsson.. Self-citation (Gustafsson Karlsson) (Correct)
....GPS measurements. In some applications this is not possible, since the system can not rely on GPS information or these signals can not be received. For underwater navigation we propose a navigation method based on terrain navigation similar to the aircraft terrain navigation proposed in [1] In [2], several different positioning and navigation systems for particle filtering are discuessed. Here we use an underwater map to support our navigation system. Since the depth information is highly non linear we use the particle filter method for state estimation. By investigating the Cramer Rao ....
F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P-J Nordlund, "Particle Filters for Positioning, Navigation and Tracking," IEEE Transactions on Signal Processing, Feb 2002.
Positioning Using Time-Difference Of Arrival Measurements - Gustafsson, Gunnarsson Self-citation (Gustafsson Gunnarsson) (Correct)
....rather insensitive to fading is based on time of arrival (TOA) In future system, only the time difference of arrival (TDOA) or enhanced observed time difference (E OTD) measurements may be possible to compute. Another source of information for tracking moving objects is map information, see [7]. This work was supported by Vinnova s competence center ISIS 3 2 1 0 1 2 3 Constant TDOA using two receivers Fig. 1. The hyperbolic function representing constant TDOA for three different TDOA s (0.4, 0.6 and 0.9 scale units, respectively) Electronic warfare, where the problem ....
....step is the key to get a working algorithm. In the standard particle filter, k denotes time and there is a time update step where the particles are moved according to a velocity measurement and a movement noise w, otherwise the algorithms are quite similar. Compare to the particle filters in [7, 8]. 5. SIMULATIONS Figure 3(a) illustrates the test scenario, with four receivers computing in total six TDOA measurements. Figure 3(b) shows what happens to the hyperbolic functions when Gaussian measurement noise (standard deviation of 0.1 scale units) is added to the TDOA measurements. That ....
F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P-J. Nordlund, "Particle filters for positioning, navigation and tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2, February 2002.
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