In the city, terrestrial digital TV network is the most perfect TV network, so the location of terrestrial broadcast signal based on digital TV has become one of the research hots-pots in recent years. Because multi-path in DTV wireless channel will cause serious signal fading, it becomes more difficult to detect DTV signal in the process of positioning, which directly affects the accuracy of positioning. Therefore, how to improve the detection accuracy of DTV signal through parameter estimation has become the focus of this paper. Based on the detailed study of DTV system based on wireless transmission channel characteristics and system structure, through the analysis based on linear least square (LS) channel estimation algorithm, the advantages and disadvantages of interference elimination is given based on the data, a new method of channel estimation, the simulation results verify the algorithm presented in this paper can well improve the channel estimation performance.
This paper mainly studies the positioning based on time of arrival and time difference of arrival. The quantity to be obtained is mainly time. Time of arrival measurement value can be obtained directly through symbolic timing synchronization, and its effective information can be obtained at the same time. Symbolic timing synchronization in digital video broadcast-terrestrial system can be divided into two stages: rough estimation and fine estimation. Rough estimation is based on the maximum likelihood estimation algorithm. The main purpose of rough estimation is to estimate the starting position of orthogonal frequency division multiplexing symbols, so that the starting position falls within the cyclic prefix of orthogonal frequency division multiplexing symbols. The purpose of fine estimation is to determine exactly the starting position of the orthogonal frequency division multiplexing symbol. In this paper, the symbolic timing synchronization algorithm of digital video broadcast-terrestrial system is studied and analyzed, and simulation is carried out for different channel models. Simulation results show that the synchronization algorithm achieves the best performance in additive white Gaussian noise channel and has good estimation performance in slow fading Rayleigh channel.
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