Performance Analysis of Network Sensing in the Distributed MIMO Radar System

This paper investigates the network sensing problem in a distributed multiple-input multiple-output (MIMO) radar system. We first formulate the received signal model in distributed MIMO systems as a function of the target's location. Based on the problem formulation, we derive the Cramér-Rao lower bound (CRLB) of the location estimation error for a single target, whose dependence on the layout of the transmitters (TXs) and receivers (RXs) is revealed. Using the tools from stochastic geometry, we then model the locations of TXs and RXs as homogeneous Poisson Point Process (PPP) and investigate the network-level sensing performance. Particularly, we derive the scaling law for the average estimation error, revealing the impact of various system parameters such as the number of antennas, SNR, TX/RX densities, and path loss exponent. More importantly, we unveil that the estimation error scales with the SNR and the number of antennas to the power of -1, and with the TX/RX densities to the power of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$-\gamma / 2$</tex>, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\gamma$</tex> is the path loss exponent. Our numerical results confirm the accuracy of our theoretical derivations and the correctness of conclusions.