Tracking algorithm speed comparisons between MHT and PMHT

Dunham, Darin T.; Dempster, Robert J.; Blackman, Samuel S.

doi:10.1109/icif.2002.1020895

Cited by 2 publications

(2 citation statements)

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“…Unfortunately, solving for the parameters directly from these equations can be problematic. The difficulty arises because P(k j jmn) is a function of all of the unknown parameters fE km , £ k g (see (28)). Thus, if there are Q unknown parameters, it would be required to solve a coupled set of Q nonlinear equations.…”

Section: Appendix a Derivation Of Parameter Estimation Equationsmentioning

confidence: 99%

“…When applied to a benchmark (single-sensor, single-target) tracking problem, it was found that the computational cost of PMHT is roughly the same order of magnitude as the cost of MHT and JPDA [27]. The computational cost of PMHT versus other methods has also been evaluated for multi-target tracking [28,29]. Perlovsky's approach would likely have a computational cost similar to PMHT, since they share a similar structure, and are both based upon EM.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Target/Multi-Sensor Tracking using Only Range and Doppler Measurements

Deming

Schindler

Perlovsky

2009

IEEE Trans. Aerosp. Electron. Syst.

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A new approach is described for combining range and Doppler data from multiple radar platforms to perform multi-target detection and tracking. In particular, azimuthal measurements are assumed to be either coarse or unavailable, so that multiple sensors are required to triangulate target tracks using range and Doppler measurements only. Increasing the number of sensors can cause data association by conventional means to become impractical due to combinatorial complexity, i.e., an exponential increase in the number of mappings between signatures and target models. When the azimuthal resolution is coarse, this problem will be exacerbated by the resulting overlap between signatures from multiple targets and clutter. In the new approach, the data association is performed probabilistically, using a variation of expectation-maximization (EM). Combinatorial complexity is avoided by performing an efficient optimization in the space of all target tracks and mappings between tracks and data. The full, multi-sensor, version of the algorithm is tested on simulated data. The results demonstrate that accurate tracks can be estimated by exploiting spatial diversity in the sensor locations. Also, as a proof-of-concept, a simplified, single-sensor range-only version of the algorithm is tested on experimental radar data acquired with a stretch radar receiver. These results are promising, and demonstrate robustness in the presence of nonhomogeneous clutter.

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Section: Appendix a Derivation Of Parameter Estimation Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%