The widespread use of radio maps has provided a seamless locating solution for urban users. However, the spatial ambiguity of received signal strength (RSS), influenced by the urban environment, limited carrier frequencies, and RSS errors, severely reduces the accuracy of positioning methods based on radio maps. To address this challenge, we propose a spatial ambiguity-aware particle filter (SAPF) to exploit the correlation properties of both spatial ambiguity and the robust localization system that fuses radio maps and inertial measurement unit (IMU). In the SAPF, a specialized kernel function is formulated to integrate Jensen-Shannon (JS) divergence derived from Gaussian Process Regression (GPR) mapping uncertainty into the likelihood function, thus reducing filter estimation errors from particles with similar RSS. In addition, a spatial ambiguity-driven particle upper threshold is designed to optimize the position error estimation of IMU, enhancing the system’s robustness and continuous localization accuracy. Simulations and real-world experiments clearly demonstrate the effectiveness and advantages of the proposed method. Specifically, compared to other competitive algorithms reported in the literature that utilize radio maps and IMU, the positioning root mean square error of SAPF is reduced by 14%–62%.

Accurate radio environment maps (REMs) can enhance the performance of wireless networks and optimize spectrum utilization efficiency. However, in rugged terrain environments, radio propagation is significantly affected by terrain variations, resulting in spatial heterogeneity in received signal strength (RSS) and impairing the accuracy of REM construction. To address these challenges, a Gaussian Process Regression method incorporating terrain (GPRT) is proposed to exploit both spatial and terrain correlation properties. In GPRT, a specialized kernel function is designed to integrate digital elevation data into the Gaussian process framework, capturing anisotropic spatial correlation and terrain effects. In addition, an Adaptive Moment Estimation (Adam) optimization algorithm is utilized for efficient hyperparameter tuning, enhancing convergence speed and parameter accuracy. Simulations with varying numbers of emitters and field experiments in real-world terrain demonstrate the superiority and effectiveness of the proposed GPRT over competing methods in terms of robustness and accuracy. Specifically, GPRT outperformed the best comparative approaches by 20% to 33% in simulations and by up to 20% in the field experiment.

•Overview •Microarchitecture design •Agile development platform •Applications in industry & academia •Summary

While research has shown that the agile chip design methodology is promising to sustain the scaling of computing performance in a more efficient way, it is still of limited usage in actual applications due to two major obstacles: 1) Lack of tool-chain and developing framework supporting agile chip design, especially for large-scale modern processors. 2) The conventional verification methods are less agile and become a major bottleneck of the entire process. To tackle both issues, we propose MINJIE, an open-source platform supporting agile processor development flow. MINJIE integrates a broad set of tools for logic design, functional verification, performance modelling, pre-silicon validation and debugging for better development efficiency of state-of-the-art processor designs. We demonstrate the usage and effectiveness of MINJIE by building two generations of an open-source superscalar out-of-order RISC-V processor code-named XIANGSHAN using agile methodologies. We quantify the performance of XIANGSHAN using SPEC CPU2006 benchmarks and demonstrate that XIANGSHAN achieves industry-competitive performance.