Sensor Selection for Distributed Estimation based on Quantized Measurements under Correlated Noise

Puntos clave

• This research aims to optimize sensor selection for estimating unknown parameters amidst quantized measurements affected by correlated noise.
• Developed a greedy algorithm selecting one node at each step for estimation.
• Derived mathematical expressions to minimize average estimation error using QR decomposition.
• Compared performance of the proposed algorithm against existing selection techniques in various correlated noise environments.
• The proposed algorithm shows superior estimation performance despite slightly increased complexity.
• It effectively minimizes estimation error under varying observational conditions.
• Results indicate the advantage of considering both quantization and correlated noise in sensor selection.