Over-the-horizon radar (OTHR) exploits skywave propagation
of high-frequency signals to detect and track targets,
which are different from the conventional radar. It
has received wide attention because of its wide area surveillance,
long detection range, strong anti-stealth ability,
the capability of the long early warning time, and so on.
In OTHR, a significant problem is the effect of multipath
propagation, which causes multiple detections via
different propagation paths for a target with missed detections
and false alarms at the receiver [1–6]. Nevertheless,
the conventional tracking algorithms, such as
probabilistic data association (PDA) [7–9], presume that
a single-measurement per target, it may consider the
other measurements of the same target as clutter, and
multiple tracks are produced when a single target is
present. Therefore, these methods cannot effectively
solve the multipath propagation problem.(Conventional multitarget tracking systems presume that each target can produce at most one measurement
per scan. Due to the multiple ionospheric propagation paths in over-the-horizon radar (OTHR), this assumption is
not valid. To solve this problem, this paper proposes a novel tracking algorithm based on the theory of finite set
statistics (FISST) called the multipath probability hypothesis density (MP-PHD) filter in cluttered environments.
First, the FISST is used to derive the update equation, and then Gaussian mixture (GM) is introduced to derive
the closed-form solution of the MP-PHD filter. Moreover, the extended Kalman filter (EKF) is presented to deal
with the nonlinear problem of the measurement model in OTHR. Eventually, the simulation results are provided
to demonstrate the effectiveness of the proposed filter.)