Kalman-Gain Aided Particle PHD Filter for Multitarget Tracking
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Citations
Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance.
Utilising Visual Attention Cues for Vehicle Detection and Tracking
Utilising Visual Attention Cues for Vehicle Detection and Tracking
Advanced signal processing techniques for multi-target tracking
References
Fundamentals of statistical signal processing: estimation theory
On sequential Monte Carlo sampling methods for Bayesian filtering
Beyond the Kalman Filter: Particle Filters for Tracking Applications
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Frequently Asked Questions (18)
Q2. What are the future works mentioned in the paper "Kalman-gain aided particle phd filter for multitarget tracking" ?
In their future work, the proposed filter will be extended to track maneuvering and closely spaced targets and applied to other target tracking applications including that in a multipleinput-multiple-output ( MIMO ) radar environment.
Q3. How many particles were used for the newborn track?
In the AP-PHD filter implementation, 1000 particles were used per existing track and 10005 particles were used for the newborn track.
Q4. What is the purpose of the Kalman gain as a correction technique?
the Kalman gain as a correction technique seeks to achieve minimal variance and thereby gives better accuracy (in approximating the posterior).
Q5. What is the auxiliary importance sampling process?
The auxiliary importance sampling [23] process starts with the selection of the measurements that are well described by the targets states extracted from the estimated PHD and this is achieved using the auction algorithm.
Q6. What is the proposed filter for MTT?
In their future work, the proposed filter will be extended to track maneuvering and closely spaced targets and applied to other target tracking applications including that in a multipleinput-multiple-output (MIMO) radar environment.
Q7. How many MC runs were used for the AP-PHD filter?
Tables V and VI show filter performance results averaged over 1000 MC runs with and without measurement set partition, respectively.
Q8. What is the t th target-originated nonlinear measurement model?
The t th target-originated nonlinear measurement model is given aszk = hk(xk, nk) (4) where hk(·) is a nonlinear function and nk is an i.i.d. process noise vector.
Q9. What are the assumptions that can be used to apply the measurement set partition approach?
the above measurement set partition approach can be applied under the following assumptions: that newborn targets exist for at least two consecutive time steps, the maneuvering of targets is not too abrupt, the sample period δt is not too large, measurement noise is not too large, clutter is not too dense, and clutter is not time correlated.
Q10. How many Guassian terms were set to the KGSMC-PHD filter?
The maximum number of Guassian terms was set to 100, with the merging (Tm) and pruning threshold (Tp) set at 10 m and 10−3, respectively.
Q11. How many particles per track is the proposed filter able to accurately track?
The plots indicate that the proposed filter with ρ = 500 particles per existing track is able to properly track all targets and in addition to being able to identify all target births and deaths while successfully accommodating nonlinearities under high clutter condition.
Q12. What is the main improvement in the tracking performance of the proposed filter?
The tracking performance was improved because only target-originated measurements were used for weight computation and the MSE at each time step was reduced resulting in fewer number of particles for state estimation.
Q13. What is the reason why the proposed filter outperforms the SMC-PHD filter?
Fig. 7 clearly shows that the proposed filter outperforms the SMC-PHD filter as it maintains an average OSPA distance of less than 51 m up to clutter intensity of κk = 8 × 10−3 (radm)−1 due to the particle state correction technique in their approach while the SMC-PHD filter starts to exhibit breakdown from about κk = 6.4 × 10−3 (radm)−1 (i.e., λ = 40).
Q14. What is the reason why the KG-SMCPHD filter outperformed all other filters?
in Table V, it can be observed that with just 500 particles per existing track, the KG-SMCPHD filter outperformed all other filters by having lower OSPA distance.
Q15. What is the reason why the authors have discussed the filter limitations?
The authors now discuss the filter limitations in terms of OSPA distance and number of clutter points, number of particles and CT as well as general filter performance.
Q16. How many particles are required to achieve an OSPA distance of less than 50 m?
In terms of miss-distance, Fig. 8 also suggests that the proposed filter is more efficient as only few a particles (less than 1000) are required to achieve an OSPA distance of less than 50 m while the SMC-PHD filter requires about 10 000 particles.
Q17. What is the PHD of the spawned targets?
respectively, asx̃lk|k−1 ≈ { qk(·|x̃lk−1, Żk), l = 1, . . . , Lk−1 pk(·|Żk), l = Lk−1 + 1, . . . ,Lk (30)with corresponding weightsw̃lk|k−1 =⎧ ⎪⎪⎪⎨⎪⎪⎪⎩φk|k−1(x̃lk, x̃ l k−1)qk(x̃k|k−1|x̃lk−1,Zk) wlk−1, l = 1, . . . , Lk−1γk(x̃lk) Jkpk(x̃k|k−1|Zk) , l=Lk−1 +1, . . . ,Lk(31) with the termφk|k−1(xk, xk−1) = pS(xk−1)fk|k−1(xk, xk−1) + bk|k−1(xk, xk−1)where Lk = Lk−1 + Jk , qk(·|·) and pk(·|·) denote the proposal distributions for persistent and newborn targets, respectively; γk(·) is the PHD of the spontaneous birth, pS(·) is the probability of target survival, fk|k−1(xk, xk−1) is the single target motion model, and bk|k−1(xk, xk−1) is the PHD of spawned targets;
Q18. Why is the EPF used as the importance sampling function for both filters?
This is because both EPF and UPF helps us to place generated samples “under” measurements as soon as measurements become available.