  In smart city development, the automatic detection of structures and vehicles
within urban or suburban areas via array radar (airborne or vehicle platforms)
becomes crucial. However, the inescapable multipath effect adversely affects
the radar's capability to detect and track targets. Frequency Diversity Array
(FDA)-MIMO radar offers innovative solutions in mitigating multipath due to its
frequency flexibility and waveform diversity traits amongst array elements.
Hence, utilizing FDA-MIMO radar, this research proposes a multipath
discrimination and suppression strategy to augment target detection and
suppress false alarms. The primary advancement is the transformation of
conventional multipath suppression into a multipath recognition issue, thereby
enabling multipath components from single-frame echo data to be separated
without prior knowledge. By offsetting the distance steering vectors of
different objects to be detected, the accurate spectral information
corresponding to the current distance unit can be extracted during spatial
spectrum estimation. The direct and multipath components are differentiated
depending on whether the transmitting and receiving angles match. Additionally,
to mitigate high-order multipath, the echo intensity of multipath components is
reduced via joint optimization of array transmit weighting and frequency
increment. The numerical results show that the proposed algorithm can identify
multipath at different distances in both single-target and multi-target
scenarios, which is superior to the general MIMO radar.
