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Bryan Clarke

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07 Jan 2014 Screenshot Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke probability hypothesi..., tracking, ospa 67 13
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07 Jan 2014 Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke

Hi Hannes,
Good find, it was used in the moving-target spawn of GM_PHD_Create_Birth at line 65 but was missed at the static-target spawn at line 116. It should be fixed now.

10 Dec 2013 Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke

Hi Kelin,
That was an untidy bug that I left in there because I kept forgetting to fix it. It didn't break anything, it just meant that the simTarget3Vel was half what it should have been. Well spotted, it should be gone now.

12 Sep 2013 Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke

Hey Hannes,
Good finds! They are fixed now, thank you for pointing them out.

15 Aug 2013 Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke

Hi Ying. Currently the sensor is simulated as a direct measurement of target position in cartesian space; the measurement vector Z = [X; Y] where X = [x1, x2, ... xN], Y = [y1, y2, ... yN] for detected targets/clutter 1:N. See the file GM_PHD_Simulate_Measurements.m for how this is done. This is in line with the simulation described in Vo & Ma, and allows a linear Kalman filter to be used.
The same paper describes an extended Kalman filter EKF-PHD algorithm which can be used in nonlinear problems, such as those using range-bearing sensors. I will upload an implementation of this as a separate submission in a couple of days.

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17 Jul 2014 Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke Kai

Hi Bryan,

To my understanding, new targets can only "birth" (assume no spawning) at predetermined positions defined in the birth intensity model.

As a result, how PHD can pick up the new targets "birth" at time step other than t=0. Assume measurements for these new targets are available.

Thanks in advance.
Kai

25 Jun 2014 Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke Fernando Iglesias

Just a very minor detail. As per MATLAB R2013b deg2rad (used in GM_EKF_PHD_Initialise_Jacobians) has been replaced by degtorad.

12 Jun 2014 Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke Kai

Hi Bryan,

I am new to RFS and PDH filter. Could you please explain to me in the "GM_PHD_Initialisation.m" L147 and L147 about the target birth and spawn:
1) what does the weight "0.1 and 0.1" means in target birth.
2) what does the weight "0.05" means in target spawn.

Thanks a lot.
Kai

13 Mar 2014 Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke Kelin Lu

Hi, Bryan. I hava a question with the target modelling. In function `GM_PHD_Initialisation`, the standard deviation of process noise `sigma_v` is initialised. While in function `GM_PHD_Simulate_Measurements`, the functions for target 1, 2 and 3 are like `simTargetiState = F * simTarget1State`. It seems that the process noise has not been involved. And in function `GM_PHD_Predict_Birth`, there is also no process noise. Can you please tell me how did you treat with the process noise? Thank you.

07 Jan 2014 Gaussian Mixture Probability Hypothesis Density Filter (GM-PHD) Implementation of the Gaussian mixture probability hypothesis density filter GM-PHD. Author: Bryan Clarke Bryan Clarke

Hi Hannes,
Good find, it was used in the moving-target spawn of GM_PHD_Create_Birth at line 65 but was missed at the static-target spawn at line 116. It should be fixed now.

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