Journal
IEEE SYSTEMS JOURNAL
Volume 15, Issue 1, Pages 694-704Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2020.2986020
Keywords
MIMO radar; Resource management; Radar tracking; Target tracking; Covariance matrices; Linear programming; Distributed MIMO radar; nonconvex optimization; PCRLB; power allocation; target assignment; target tracking
Categories
Funding
- National Nature Science Foundations of China [61501505, 61871395]
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The DTFR mode in distributed MIMO radar networks is effective for multitarget tracking, and a TAPA strategy is developed for better system tracking accuracy. The TAPA problem, which involves target-radar assignment and power allocation, is solved using an efficient two-step-based solution. Simulation results demonstrate superior performance and adaptivity compared with existing algorithms.
The defocused transmit-focused receive (DTFR) mode in the distributed multiple-input multiple-output (MIMO) radar network is very effective in multitarget tracking. In this mode, a completely defocused beam is transmitted and a focused receive beam is synthesized so that the MIMO radar is capable of tracking targets independently. A joint target assignment and power allocation (TAPA) strategy is developed for multiple distributed MIMO radar networks in cluttered environment using the DTFR mode. Our aim is to achieve the better system tracking accuracy under the constraints of receive beam direction capability and power budget. We derive the posterior Cramer-Rao lower bound (PCRLB) and adopt it as the objective function, since it quantifies the precision of target state estimates. It is shown that the TAPA problem is a mixed integer programming and NP-hard problem, where two involved parameters, i.e., the target-radar assignment and power allocation, are both coupled in the objective and in the constraints. By introducing an intermediate variable, we propose an efficient two-step-based solution for solving this problem. The simulation results show the superior performance and adaptivity compared with existing algorithms.
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