Aerospace engineering

Pedagogy

Computer network

Mathematics

Assignment problem

Algorithm

Filter (signal processing)

Mathematical optimization

Bernoulli's principle

Engineering

Artificial intelligence

Psychology

Wireless sensor network

Set (abstract data type)

Tracking (education)

Programming language

Radar tracker

Computer science

Object (grammar)

Computer vision

Radar

Telecommunications

IEEE Transactions on Signal Processing

This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. Like its single-sensor counterpart, such implementation requires truncating the GLMB sum. However the single-sensor case requires solving 2-D ranked assignment problems whereas the multi-sensor case require solving multi-dimensional ranked assignment problems, which are NP-hard. The proposed implementation exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has a quadratic complexity in the number of hypothesized objects and linear in the total number of measurements from all sensors.

Multi-Sensor Multi-Object Tracking With the Generalized Labeled Multi-Bernoulli Filter