KEYWORDS: Radar, Signal to noise ratio, Detection and tracking algorithms, Antennas, Receivers, Transmitters, Target detection, Electronic filtering, Surveillance, Signal generators
Multiple-Input Multiple-Output (MIMO) radars with widely-separated antennas have attracted much attention
in recent literature. The highly efficient performance of widely-separated MIMO radars in target detection
compared to multistatic radars have been widely studied by researchers. However, multiple target localization
by the enlightened structure has not been sufficiently explored. While Multiple Hypothesis Tracking (MHT)
based methods have been previously applied for target localization, in this paper, the well-known 2-D assignment
method is used instead in order to handle the computational cost of MHT. The assignment based algorithm works
in a signal-level mode. That is, signals in receivers are first matched to different transmitters and, then, outputs
of matched filters are used to find the cost of each combination in the 2-D assignment method. The main benefit
of 2-D assignment is to easily incorporate new targets that are suitable for targets with multiple scatters where a
target may be otherwise unobservable in some pairs. Simulation results justify the capability of 2-D assignment
method in tackling multiple target localization problems, even in relatively low SNRs.
Multiple-Input Multiple-Output (MIMO) radars are a new generation of radar systems that bring with them
many benefits compared to the traditional phased-array radars. This paper discuses localization techniques for
multiple targets when a MIMO radar is used as a measurement tool. A multiple hypotheses-based approach
is used to estimate parameters of targets from raw measurements. Received amplitudes and associated range
bins are taken as raw measurements. The multiple hypothesis-based method is implemented in two steps. First,
hypotheses are initialized using the fist q pairs of transmitters and receivers. Then, a sequential method is
applied to initial hypotheses to find final estimates of targets. A comparison is also made between multistatic
and MIMO radars for target detection and localization via simulations. The effect of putting threshold on raw
data is taken into consideration in both detecting and localizing targets for multistatic radars. Finally, simulation
results confirm the superiority of MIMO radars for multiple target localization.
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