Low Power Sensor Localization in 3D using a Recurrent Neural Network

This letter discusses a novel low-power circuit to self-localize a mobile sensor node in the three-dimensional space using a passive optical receiver. Self-localization of sensors, where a sensor node computes its spatial location by itself, reduces transmission demand, and improves real-time conformity of mobile wireless sensor systems. Our approach forms an over-determined system of angles-of-arrival (AoAs) to mobile sensor received from an optical anchor grid. The AoA system is solved with a linear program (LP) solver, which is implemented using a nonlinear feedback recurrent neural network. To solve the primal and dual LP optimization problems in the AoA system, we show a single multifunctional data path that does not require matrix inversions; thereby, enables within-sensor low-power computations to self-localize. Additionally, unlike other optical indoor positioning architectures, our approach does not require measurements of received signal strength and, thereby, is insensitive to power and alignment imbalances in the anchor grid. We show proof-of-concept field programmable gate array (FPGA) and ASIC simulations of our approach and validate its operation under noisy AoA data and for different numbers of anchors. An FPGA implementation in 180 nm CMOS achieves a peak $\sim$ 0.12 mega localization operations per second per Watt (MOPS/W), while ASIC design in 45 nm CMOS shows a peak $\sim$ 7.7 MOPS/W.