AI 中文总结
该研究针对低成本IoT发射源被动定位的似然函数闭式表达难题，应用并验证了拒绝采样ABC、序贯蒙特卡洛ABC两种方法的性能。
AI 中文摘要
我们解决的问题是利用低轨（LEO）卫星在观测窗口内收集的多普勒频率测量值，对静止的地面物联网（IoT）无线电发射源进行被动定位。该问题具有挑战性，因为低成本IoT设备的无线电发射会受到多种复合测量误差源的影响，这些误差源共同导致似然函数无法以闭式形式表达。因此，我们应用并研究近似贝叶斯计算（ABC）方法在此任务中的性能。数值结果展示了两种ABC方法——拒绝采样ABC和序贯蒙特卡洛ABC的统计与计算性能。
英文摘要
We address the problem of passive localisation of a stationary, ground-level IoT radio emitter using Doppler frequency measurements collected by low-Earth orbit (LEO) satellites during an observation window. The problem is challenging because radio emission from low-cost IoT devices is affected by various compounding sources of measurement error, that collectively render the likelihood function intractable in a closed form. Hence, we apply and investigate the performance of Approximate Bayesian Computation (ABC) methods for this task. Numerical results demonstrate the statistical and computational performance of two ABC methods, rejection sampling ABC and sequential Monte Carlo ABC.
Comments6 pages, 2 figures
