Real-time, high-accuracy frequency-phase estimation is the critical mission of Doppler tracking, which is a primary technique for deep space spacecraft navigation and planetary radio science experiments. Usually, the analog intermediate frequency signal is digitalized and converted to baseband by signal processing hardware platforms called digital back-ends (DBEs) and parameter estimation is performed by extra high performance computers. In this paper, a novel real-time, high-accuracy parameter estimator called a hardware-based integrated parameter estimator (HIPE) is proposed and implemented inside DBEs. An adaptive frequency tracker is proposed to make the initial signal detection, frequency tracking, and data reduction. Then a parameter estimation is sequentially obtained by a modified dechirp technique and a high-resolution spectral analysis technique called spec-zooming. Further, a folding architecture is designed to save hardware resources when realizing spec-zooming in a field programmable gate array (FPGA). An example design is deployed on a DBE with Xilinx Virtex-6 FPGA and an ARM processor. The performance is verified by X-band observations of Mars Express (MEX) and New Horizons (NH). Under an integration time of 1 s, HIPE only takes 2.2 ms to process single-channel baseband data and provides frequency accuracies of 7 mHz and 30 mHz for the tested MEX and NH data. HIPE is implemented inside DBE, so the extra computer is no longer required and the pressure of data transmission or storage is greatly relieved. It could easily be extended to parallel multi-channel, real-time processing and would be a powerful method for Doppler measurement in deep space exploration missions, such as the Chinese mission to Mars to be undertaken by 2020.

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Very-Long-Baseline interferometry (VLBI) is a powerful tool in radio astronomy, geodesy, and deep space exploration. Priori predicted delay models are needed to make interferometry fringes, but in some cases they would be difficult to get. This paper proposes an effective algorithm named CAF-W algorithm to search fringes from the raw data in a large search range without priori predicted delay models. The cross-ambiguity function (CAF) is used to make a time-frequency correlation in the delay-delay rate plane. The wavelet boosting algorithm is used to eliminate interference and enhance the CAF peak, whose position would give the delay and delay rate estimations. Incoherent averaging and sliding search window techniques are used to overcome the wide search range and the poor signal-to-noise ratio in VLBI observations. The CAF-W algorithm could be performed with fast algorithms so the computation burden is affordable. This algorithm has successfully achieved VLBI fringes from the raw data without priori predicted delay models in VLBI observations.

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