This work falls within the scope of indoor localization, more precisely the fingerprinting-based indoor localization. 

Your task will be to investigate the potential and outcomes of opting for a multi-layer reference map during the "Offline phase", which represents a potential improvement idea that has never been adopted or tested in current state-of-the-art fingerprinting schemes.

The aim here is to achieve a better trade-off between both performance and costs, yielding a better localization method. 

Prerequisites

The Chair of Media Technology at the Technical University of Munich (TUM) is offering a Master's/Bachelor's Thesis (or guided research) opportunity in the context of model-based deep learning for indoor localization, combining classical Kalman filtering theory with modern neural state estimation.

Indoor localization using WiFi RSSI and inertial sensors (IMU) is a sequential state-estimation problem naturally suited to Kalman filtering, where the IMU drives the motion model and WiFi RSSI provides the correction. However, the relationship between RSSI and position is highly nonlinear and building-specific, motivating a hybrid model- and data-driven approach. KalmanNet replaces the analytically-derived Kalman gain with a GRU-based recurrent network learned from data, retaining the interpretability and low-data efficiency of the classical filter while implicitly learning complex, unmodeled dynamics.

The thesis investigates KalmanNet on the IMUWiFine dataset (220 per-AP RSSI values, 9-channel IMU, fine-grained ground-truth trajectories across a multi-floor building) along a complexity ladder: from a fully active system with complete per-access-point observability, through intermediate degradation stages (AP reduction, sectorization), to a fully passive distributed antenna system (DAS), where only a single aggregated RSSI value is available per time step.

Selection and Reproduction of Baseline

As KalmanNet has not previously been applied to this dataset, the first part of the thesis involves reproducing a KalmanNet baseline and validating it against classical filters (EKF, UKF) and the existing LSTM-based end-to-end baseline. The implementation should support:

• Per-AP RSSI and per-antenna observation vectors

• IMU-driven state-evolution modeling

• GRU-based Kalman gain estimation, trained end-to-end

Degradation Analysis and Passive DAS Reformulation

Building upon the baseline above, the second part focuses on systematically degrading observability and identifying what must change for KalmanNet to remain viable as the system moves toward a passive

DAS. This involves:

• Reducing and sectorizing the AP/antenna observation set

• Investigating the non-invertibility and dimensionality collapse of aggregated RSSI observations

• Synthesizing aggregated RSSI from existing per-AP data to emulate the passive DAS setting

• Increasing reliance on IMU-based state evolution to compensate for degraded observability

Prerequisites