# Decentralized Multi-target Tracking in Urban Environments: Overview and Challenges

## TL;DR

This paper reviews the current state of mobile sensor control techniques for multi-target tracking in urban environments, emphasizing the challenges and need for interdisciplinary collaboration to improve surveillance performance.

## Contribution

It provides an overview of existing methods, highlights challenges in non-myopic control for mobile sensors, and advocates for collaboration across research communities.

## Key findings

- Mobile sensor control is complex due to platform dynamics.
- High-fidelity control strategies are necessary for effective tracking.
- Interdisciplinary collaboration can advance urban surveillance techniques.

## Abstract

In multi-target tracking, sensor control involves dynamically configuring sensors to achieve improved tracking performance. Many of these techniques focus on sensors with memoryless states (e.g., waveform adaptation, beam scheduling, and sensor selection), lending themselves to computationally efficient control strategies. Mobile sensor control for multi-target tracking, however, is significantly more challenging due to the complexity of the platform state dynamics. This platform complexity necessitates high-fidelity, non-myopic control strategies in order to achieve strong tracking performance while maintaining safe operation. These sensor control techniques are particularly important in non-cooperative urban surveillance applications including person of interest, vehicle, and unauthorized UAV interdiction. In this overview paper, we highlight the current state of the art in mobile sensor control for multi-target tracking in urban environments. We use this application to motivate the need for closer collaboration between the information fusion, tracking, and control research communities across three challenge areas relevant to the urban surveillance problem.