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The direct position determination (DPD) algorithms have been shown to outperform conventional two-step methods in localization accuracy under poor observation conditions (e.g., low signal-to-noise ratios or short signal observation durations). In this paper, we consider the distributed DPD of a static source in large-scale sensor networks. Different from the centralized DPD (CDPD) algorithms where all the raw signal samples are transmitted globally to a fusion center to solve for the source position, in our method the signal samples are only exchanged locally between neighboring sensors, and the source position is estimated using the proposed DPD algorithm distributively. The problem-specific cost function, which is different from the cost function of CDPD, is first derived. To obtain the cost function at each sensor, we propose to transmit the low-dimensional sufficient statistics across the sensor networks instead of the original high-dimensional raw signal samples, and to reconstruct the cost function via simple interpolations. Simplification of cost function evaluation is also developed to further reduce complexity. Finally, source position is estimated at each sensor efficiently via the importance sampling algorithm. Simulation results demonstrate the superior performance of the proposed method over existing adaptive distributed DPD as well as the two-step algorithms under poor observation conditions, and its computational efficiency is significantly improved compared with the CDPD. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of estimating with high precision the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) for interleaved pulse trains radiated from multiple sources. The pulse signal parameters of multiple sources are indistinguishable and as a result conventional de-interleaving techniques fail to separate the pulses emitted from each source. By noting that cross-correlating pulses from the same source at their true TDOA and FDOA produces consistent phases, a high-precision joint TDOA-FDOA estimator is proposed. Within the developed technique, a histogram-based clustering procedure is introduced to obtain initial TDOA and FDOA estimates. Then, an expectation maximization (EM)-based method is utilized to refine their estimates, where the unknown source labels of pulse signals are hidden variables and they are estimated together with TDOA and FDOA iteratively. Simulation results reveal the superior performance of the proposed algorithm compared to existing techniques, at the cost of moderate computational complexity increment. [ABSTRACT FROM AUTHOR]

Spatial optical beam-forming network (OBFN) is a superior structure than traditional ones in bandwidth adaptability, system complexity, and so forth. Compared with conventional beam-forming network, the output signal model of OBFN is different, making the previous direction of arrival (DOA) estimation methods improper for this structure. At present, DOA estimation method for this structure has not been sufficiently explored and there is no efficient algorithm. In this paper, the observation model of the network is established first, and then a new DOA estimation method is proposed. The new method makes use of the amplitude distribution of the fiber array to achieve direction finding. Sufficient numerical simulations are carried out to demonstrate the feasibility and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]