Low-complexity TDOA and FDOA localization: A compromise between two-step and DPD methods

Abstract Conventional two-step passive localization methods have poor localization precision when the input signal-to-noise ratio (SNR) is low, although they are usually computationally attractive. The direct position determination (DPD) methods, on the contrary, exhibit stronger robustness to low SNRs at the cost of much higher computational complexity. In this paper, an expectation-maximization (EM)-based computationally inexpensive localization method is proposed, which serves as a compromise between the two-step methods and DPD methods. During each iteration of the proposed method, the constraint that all intermediate parameters correspond to a common source position is taken into account explicitly, and this constraint is used to refine the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. Then the source position is updated using these renewed measurements. The proposed method has superior localization performance under low SNR conditions compared to the two-step methods at the cost of limited increment in computational load, and its computational complexity is much lower than DPD methods at the cost of acceptable accuracy degradation. Moreover, the proposed method is applicable to other localization scenarios as well with appropriate modifications. Simulation results demonstrate the effectiveness of the proposed method.