Misc. Information on Multi-Target Tracking: Algorithm Comparisons
Any information on IMM-SD?
- Theoretically IMM-SD and IMM-MHT should provide equivalent performance in tracking
- Can be considered as an implementation of IMM-MHT
- However, simulation shows that IMM-SD performs better.
How can IMM-SD achieve better performance than IMM-MHT?
- IMM-SD finds the global optimal where IMM-MHT finds current optimum (as I could understand, however, further verification is required)
- Global optimum through the enumeration of current IMM-MHT
What else does IMM-SD use to find the global optimum?
- Lagrange relaxation method is used to get approximate optimal global hypothesis
References:
Academic Press Library in Signal Processing: Communications and Radar Signals
What is IMM-JIPDA?
- Interactive multiple model – Joint integrated Probabilistic Data Association
What are some other related algorithms to IMM-JIPDA?
- IMM-IPDA, IMM-LMIPDA, PDA is the common concept here
What is the common approach that all of the above algorithms use?
- Integrates IMM to PDA
- Each of them models a posterior state estimation – PDF using a single Gaussian pdf.
- Recursively updates the probability of target existence – IMM style
- Can be used for false track discrimination
Does IMM-PDA track multiple targets?
- No, it is to track one target
What are the multi-target tracking algorithms among the names mentioned before?
- IMM-JIPDA, IMM-LMIPDA
How does IMM-JIPDA work?
- Calculates a posterior probabilities to all measurements to track allocations
- The number of measurement to track allocations grows exponentially
- Hence, IMM-JIPDA can be used for small number of crossing targets
- Also in low clutter measurement density
How does IMM-LMIPDA work?
- Integrates linear multi-target method methods with IMM-IPDA
- When one track is updated with LM
- The other tracks modulate the clutter measurement density
- And are subsequently ignored
References:
A research paper. I could not locate the paper at this time. Will update, if I come across again
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