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For the change of dynamic indoor environment and the effect of time-varying received signal strength on positioning accuracy, a class of indoor positioning algorithms for agricultural wireless sensor networks using kernel adaptive filtering was proposed, which included quantized kernel least mean square (QKLMS) as well as fixed-budget kernel recursive least-squares (FB-KRLS) algorithm. The QKLMS algorithm used a simple vector quantization approach as an alternative of sparsification to curb the growth of the radial basis function structure in kernel adaptive filtering. The FB-KRLS algorithm was an online kernel method by fixed memory budget, which was capable of recursively learning nonlinear mapping and tracking change over time. In contrast to a previous sliding-window based technique, the presented algorithm did not prune the oldest data point in every time instant but it was aimed to prune the least significant data point, thus suppressing the growth of kernel matrix. The kernel adaptive filtering algorithms achieved the indoor positioning for WSNs by building the non-linear mapping relations between the RSS fingerprint information and the physical location. The employed algorithms were applied to different indoor positioning instances in the simulation and physical environments for WSNs, under the same circumstances, compared with other kernel-based learning methods and extreme learning machine (ELM) etc. In the simulation experiment, the average localization error of the two algorithms was respectively 0.746m and 0.443m under three scenarios, and the average localization error of the two algorithms in the physical experiments was respectively 0.547m and 0.282m under two scenarios. Experimental results showed that the proposed adaptive filtering algorithms can improve the positioning accuracy, and its online learning ability made the proposed two localization algorithms all adaptable to the dynamic changes of the environments.