Speakers
Description
The design of space-borne signal processing payloads faces a critical challenge: maintaining geolocation accuracy and interference mitigation while the platform—whether a CubeSat, UAV, or aircraft—operates under dynamic attitude regimes. Traditional simulation environments often decouple orbital dynamics from digital signal processing (DSP) chains, failing to account for the impact of real-time attitude variations on array manifold steering vectors. This paper presents an integrated simulation framework that bridges NASA’s General Mission Analysis Tool (GMAT) with the GNU Radio ecosystem to simulate, validate, and optimize high-resolution array processing in realistic dynamic scenarios.
The proposed architecture establishes a co-simulation pipeline where GMAT provides high-fidelity orbital and attitude ephemerides, which are ingested into a custom GNU Radio block. This block performs real-time 3D rotation matrix transformations on the antenna array’s steering vector, effectively compensating for Roll, Pitch, and Yaw perturbations. By aligning the array’s reference frame with the inertial frame in the DSP domain, the system ensures consistent DOA estimation regardless of platform instability.
The processing chain implements the MUSIC algorithm for super-resolution Direction of Arrival (DOA) estimation, cascaded with an MVDR (Capon) beamformer for adaptive null steering. To ensure simulation fidelity, the framework incorporates a comprehensive RF channel model, accounting for thermal noise, insertion losses, and signal degradation inherent to components such as LNAs and impedance mismatches. These parameters are shown to have a non-trivial impact on the stability of the noise subspace estimation, a crucial factor for the precision of MUSIC-based geolocation.
This framework demonstrates that high-performance adaptive beamforming can be achieved in software-defined radio environments using entirely open-source tools. We provide the complete source code and implementation details, enabling researchers to replicate these results and test custom algorithms against dynamic aerospace disturbances. This work contributes to the advancement of affordable, robust, and verifiable space-payload design, bridging the gap between theoretical signal processing and the physical constraints of dynamic flight.
| Talk Length | 15 Minutes |
|---|---|
| Link to Open Source Code | https://github.com/rafaelsscruz/Adaptive-Array-Attitude-Integration |
| Acknowledge | Acknowledge In-Person |