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Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.

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In real system application, the interacting multiple model (IMM)-based uncertain target tracking system operates with the following problems: it requires less computing resources as well as a robust performance with respect to the maneuvering such as a sub-model mismatched case, and further, it requires an easy design procedure related to its structures and parameters. To solve these problems, an adaptive fuzzy IMM (AFIMM) algorithm, which is based on well-defined basis sub-models and well-adjusted mode transition probabilities (MTPs), is proposed. The basis sub-models are defined by the detailed analysis in terms of kinematic models as well as the maneuvering property and the MTPs are adjusted by the fuzzy adaptor as well as the fuzzy decision maker. To verify the performance of the proposed algorithm, an airborne target tracking is performed. Simulation results show that the AFIMM effectively solves the problems experienced in the uncertain target tracking system online.