Researchers from Xidian University developed a grey wolf optimization-based track-before-detect (GWO-TBD) method for extended target detection and tracking
Maneuvering weak target detection and tracking is often characterized by difficult issues in modern radar systems. The approach is tasked to detect, track, and identify targets from sequences of measurements and clutter. Radars obtain more than one measurement per time step from different corner reflectors of a single target in order to achieve increased resolution. Moreover, several algorithms help to extend the ability of radars to detect and track objects. These algorithms can be classified in two major sub types: extended target probability hypothesis density (ET-PHD) filters and track-before-detect algorithms.
The Grey Wolf Optimizer (GWO) algorithm impersonators the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. Now, a team of researchers from Xidian University used GWO to track and detect an extended target in a radar system. The algorithm has a few parameters only and is easy to implement, which makes it superior than other algorithms. Due to the versatile properties of the GWO algorithm, attempts have been made to implement GWO to solve the optimization problems. The algorithm identified the optimal association of measurement sets among the multiple scans.
In track-before-detect (TBD), a signal is tracked before declaring it a target. The team found that GWO and TBD can be integrated to effectively detect and track objects. The team also found that the GWO-TBD approach is superior to current ones, particularly when the extended target signal is weak or the target is in motion. Experimental results concluded that the GWO-TBD approach offers efficient performance and is more practical in the real world compared to other approaches. However, the approach also has certain limitations such as the limited ability to cope with one target at a time. In further research, the team plans to develop more methods capable of detecting multiple closely maneuvering extended targets. The research was published in the journal MDPI Sensors on April 1, 2019.
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