Domain-Specific Architecture for IMU Array Data Fusion
Owais Talaat Waheed, Ibrahim M. Elfadel · 2019
To achieve high accuracy at low cost in a navigational system, an array of several low-cost MEMS Inertial Measurement Units (IMU's) may be used rather than one single high-performance but high-cost and power hungry mechanical IMU. To combine and predict the outputs and internal states of the IMU array, signal processing algorithms, such as the Kalman Filter (KF), are used with their prediction accuracy increasing with the number of array elements. While large IMU arrays are beneficial for accurate and precise estimation of linear and angular accelerations, they are detrimental to the KF computations since the underlying matrix dimensions of each KF variable increase drastically with array size. This paper discusses a domain-specific processor architecture implemented on an Artix-7 FPGA that can efficiently support the KF matrix operations and improve the throughput of the KF data fusion component. The processor instruction set, design and firmware are discussed in detail. The processor performance and hardware resource utilization are also fully quantified. To constrain the resource and power requirements of the processor-to-array interface, the kinematic model of the IMU accelerometer is used to devise a model-based approximation technique that reduces the number of sensor interface units to just one. This is then implemented using the time multiplexing of the data from various array sensors. Experimental results show that the RMSE of the estimated linear acceleration remains below 0.3m/s2for a sensor noise standard deviation of less than 0.04m/s2. The proposed combination of the model-based approximation with the domain specific processor results in a compact data fusion processing system with minimal footprint that vastly outperforms a general purpose processor.