Comparison of theoretical and simulated performance of optimal and suboptimal filters in a dense multitarget environment

Singer, Robert A.; Sea, R.; Housewright, K.

doi:10.1109/cdc.1972.269106

Cited by 3 publications

(1 citation statement)

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“…The first type is data association-based MTT algorithms, which operate on the principle of matching sensor-acquired measurements with targets and subsequently updating and estimating the status of each target with good correlation. However, traditional MTT algorithms, including the nearest neighbor (NN) method (Singer et al 1972) and the joint probabilistic data association (JPDA) algorithm (Fortmann et al 1983), rely on highly precise correlation technology; otherwise, correlation errors can lead to inaccurate estimations, greatly increasing the operational cost of the algorithm. Thus, implementing data-association algorithms in complex backgrounds is challenging.…”

Section: Introductionmentioning

confidence: 99%

Modified Gaussian Mixture Probability Hypothesis Density Filtering using Clutter Density Estimation for Multiple Target Tracking

Sun,

Xue,

Gao

2024

J. Aerosp. Technol. Manag.

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Gaussian mixture probability hypothesis density (GM-PHD) filtering often assumes a uniform distribution of clutter in the observation area. However, in practice, clutter is often unknown and non-uniform, necessitating accurate estimation of its spatial distribution, non-uniformity, and temporal variations. To address this problem, we proposed a modified GM-PHD filtering method with clutter density estimation for multiple target tracking. In the proposed method, first, potential target measurements within the tracking gate are eliminated to obtain the clutter measurement set. Next, the clutter density around each target is estimated. Finally, the estimated clutter density is incorporated into GM-PHD filtering, to estimate the target state and clutter density in complex clutter environments. Simulation results demonstrated that the proposed filtering method improves the performance of the GM-PHD filter in multi-target tracking scenarios with unknown clutter density.

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Section: Introductionmentioning

confidence: 99%