An Improved Adaptive Sparrow Search Algorithm for TDOA-Based Localization

The Ultra-Wideband (UWB) indoor positioning method is widely used in areas where no satellite signals are available. However, during the measurement process of UWB, collected data contain random errors. To alleviate effect errors on accuracy, an improved adaptive sparrow search algorithm (IASSA) based (SSA) proposed this paper by introducing three strategies, namely, two-step weighted least squares algorithm, adjustment boundary, and producer–scrounger quantity adjustment. simulation field test results indicate that IASSA achieves significantly higher localization accuracy than previous methods. Meanwhile, requires fewer iterations, which overcomes problem long computation time swarm intelligence optimization algorithm. Therefore, has advantages robustness performance.