A collaborative positioning algorithm for mobile target using multisensor data integration in enclosed environments

The accurate and robust positioning for mobile target is an important technology in enclosed environments. The noisy distance measurement makes the positioning performance of mobile target decline. We propose a collaborative positioning algorithm based on time differences of arrival (TDOA) and angle of arrival (AOA) measurements, combined with three-axis accelerometers (TAA). A coarse position result is estimated using an interacting multiple model (IMM) method. We further improve the positioning performance by utilising the TDOA and AOA values integrated with initial results of the IMM method. To evaluate and verify the proposed method, the Cramer–Rao lower bound (CRLB) is derived by multisensor data measurements. We implement the collaborative positioning algorithm and the CRLB estimation as well as the experiments. The results show that the proposed method can eliminate the cumulative positioning error caused by the TAA approach and reduce large positioning errors caused by the TDOA/AOA approaches. Hence, the proposed method can make up for their shortcomings of noisy measurements and perform highly accurate and robust positioning. Moreover, the proposed method is able to achieve CRLB accuracy in positioning performance analysis.