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Register a new userTarget Localization using Bistatic and Multistatic Radar with 5G NR Waveform
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Added by
Ojas Kanhere
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Joint communication and sensing allows the utilization of common spectral resources for communication and localization, reducing the cost of deployment. By using fifth generation (5G) New Radio (NR) (i.e., the 3rd Generation Partnership Project Radio Access Network for 5G) reference signals, conventionally used for communication, this paper shows sub-meter precision localization is possible at millimeter wave frequencies. We derive the geometric dilution of precision of a bistatic radar configuration, a theoretical metric that characterizes how the target location estimation error varies as a function of the bistatic geometry and measurement errors. We develop a 5G NR compliant software test bench to characterize the measurement errors when estimating the time difference of arrival and angle of arrival with 5G NR waveforms. The test bench is further utilized to demonstrate the accuracy of target localization and velocity estimation in several indoor and outdoor bistatic and multistatic configurations and to show that on average, the bistatic configuration can achieve a location accuracy of 10.0 cm over a bistatic range of 25 m, which can be further improved by deploying a multistatic radar configuration.
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We consider an optimization deployment problem of multistatic radar system (MSRS). Through the antenna placing and the transmitted power allocating, we optimally deploy the MSRS for two goals: 1) the first one is to improve the coverage ratio of surveillance region; 2) the second goal is to get a even distribution of signal energy in surveillance region. In two typical working modes of MSRS, we formulate the optimization problem by introducing two objective functions according to the two mentioned goals, respectively. Addressing on two main challenges of applying multi-objective particle swarm optimization (MOPSO) in solving the proposed optimization problem, we propose a deployment algorithm based on multiobjective particle swarm optimization with non-dominated relative crowding distance (MOPSO-NRCD). For the challenge of value difference, we propose a novel selection method with a non-dominated relative crowding distance. For the challenge of particle allocation, a multi-swarm structure of MOPSO is also introduced. Finally, simulation results are given out to prove the advantages and validity of the proposed deployment algorithm. It is shown that with same number of employed particles, the proposed MOPSO-NRCD algorithm can achieve better optimization performance than that of traditional multiobjective particle swarm optimization with crowding distance (MOPSO-CD).
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