摘要
The sequential estimation （SE） algorithm has a poor performance in the environment with a low signalto-noise ratio （SNR） and a high bit error rate （BER）, especially for unknown initial acquisition sequence. This paper summarizes the conventional sequence acquisition model, and discovers its several problems, which are caused by accumulating sequence innovation to all of the received sequences dispersedly. To solve these problems, the paper presents a new algorithm, CSAS-SA （controllable single accumulated state-sequential acquisition）. This algorithm accumulates the sequence innovation to a single appointed sequence state and makes the useful information accumulated effectively. Through simulation, CSAS-SA has a higher probability of success acquisition. When SNR equals -3 dB, the performance can be improved by 70%.
The sequential estimation （SE） algorithm has a poor performance in the environment with a low signalto-noise ratio （SNR） and a high bit error rate （BER）, especially for unknown initial acquisition sequence. This paper summarizes the conventional sequence acquisition model, and discovers its several problems, which are caused by accumulating sequence innovation to all of the received sequences dispersedly. To solve these problems, the paper presents a new algorithm, CSAS-SA （controllable single accumulated state-sequential acquisition）. This algorithm accumulates the sequence innovation to a single appointed sequence state and makes the useful information accumulated effectively. Through simulation, CSAS-SA has a higher probability of success acquisition. When SNR equals -3 dB, the performance can be improved by 70%.
基金
Supported by the Program for New Centery Excellent Talents in University and the National High-Tech Research and Development Program of China (Grant No. 2007AA01Z434)
