Base Station Placement Optimization for Networked Sensing Exploiting Target Location Distribution

TL;DR

This paper presents a method to optimize the placement of base stations in a networked sensing system to improve 3D target localization accuracy by minimizing the posterior Cramér-Rao bound, leveraging known target location distribution.

Contribution

It derives a closed-form expression for the PCRB in networked sensing and proposes an iterative algorithm for optimal BS placement with convergence guarantees.

Findings

Optimized BS placement reduces localization error significantly.

The proposed algorithm outperforms benchmark placement strategies.

Closed-form PCRB expression facilitates efficient placement optimization.

Abstract

This paper studies a networked sensing system with multiple base stations (BSs), which collaboratively sense the unknown and random three-dimensional (3D) location of a target based on the target-reflected echo signals received at the BSs. Considering a practical scenario where the target location distribution is known a priori for exploitation, we aim to design the placement of the multiple BSs to optimize the networked sensing performance. Firstly, we characterize the posterior Cram\'er-Rao bound (PCRB) of the mean-squared error (MSE) in sensing the target's 3D location. Despite its complex form under networked sensing, we derive its closed-form expression in terms of the BS locations. Next, we formulate the BS placement optimization problem to minimize the sensing PCRB, which is non-convex and difficult to solve. By leveraging a series of equivalent transformations and the iterative inner approximation method, we devise an algorithm with polynomial-time complexity which is guaranteed to converge to a solution satisfying the Karush-Kuhn Tucker (KKT) conditions of the problem. Numerical results show that the proposed placement design significantly outperforms various benchmark designs.