| How to efficiently and reliably receive the weak signal is one of major problems andchallenges for communication technology. Due to the limitations of the technical level, it’s verydifficult to further increase the antenna aperture or reduce the receiver noise. An effectivesolution is to use multiple antennas to joint receive the source signal. Received signals arecombined in order to improve the signal quality. The multi-antenna signal may be combined toimprove the effective aperture of the antenna. This is an effective method to solve thecontradiction between the low signal-to-noise ratio and high-speed communications needs.Multiple signals combining technology of random distribution antennas has the advantages offlexible, low cost and robust performance. It can be applied in many fields. Currently, thetechnology is mainly researched and used in the special environment of deep space exploration.Promotion and application of the technology in a more general application environment is facingmany difficulties such as irregular distribution of antennas, the individual differences of thereceiver and fading channel problems. To solve the problems under more general applicationenvironments is the key to further promote the application of the technology that must beaddressed.The researching works of this paper around the key technologies of signal waveformcombining on randomly distribution antennas. The time delay estimation and compensation, thefrequency difference estimation and compensation, the optimal combining weight estimation andcombining technology of multipath fading channel are discussed and analyzed in this paper. Themain work and innovations of the thesis can be summarized as following aspects.1. The classical combining algorithms under the criteria of maximum outputsignal-to-noise ratio and the maximum output power have the problems of estimating noisevariance and the biased ness of estimation. This paper presents and proves a new objectfunction, the combined signal autocorrelation coefficient, which is equivalent to thesignal-to-noise ratio in combining. The criterion of maximum combined signalautocorrelation coefficient is proposed for optimal signal combining. This paper alsoproposed combining weight estimation algorithms for two signals and multiple signalsrespectively according to the new criterion. For conventional combining weight estimation,algorithms using the combined signal SNR as the object function fail in some applicationscenarios which are very different to estimate the noise variance. The estimating algorithmsusing the combined signal power as the object function is biased with not uniform noisevariances. To solve these problems, this paper proposed and proved that the combined signalautocorrelation coefficient can be used as the object function in signal combining, which is equivalent to the combined signal SNR. Using this object function, the paper proposed a simplealgorithm for2signals combining. Using the eigenvalue decomposition method, the paperproposed the AC EIGEN algorithm for multi-signal combining weight estimation. Using a linearcombination of a set of signal correlation matrices instead the correlation matrix, the MACEIGEN algorithm is proposed. And the combining performance under low SNR environment isfurther enhanced. These algorithms do not need timing synchronization, has nothing to do withthe signal modulation, the signal bandwidth, no need to estimate the noise variance of signal, canalso be applied to scenarios of non-uniform noise power and related noise. Algorithms are withexcellent performance and high universality. Simulation results show that proposed algorithmsoutperform classical algorithms under low SNR environment.2. The equivalence between combined signal normalized kurtosis and SNR is proved.Using the new criterion, an iterative algorithm is proposed. The simulation results showthat iteration can get a lower variance than ACE algorithm. This paper found that statisticssuch as the combined signal normalized kurtosis, normalized mean square error of signalinstantaneous power are equivalent to the combined signal SNR in signal combining as theobject function. According to the difference of statistical properties of signal and noise, aniterative algorithm is proposed based on it. These algorithms do not need timing synchronization,has nothing to do with the signal modulation, the signal bandwidth, no need to estimate the noisevariance of signal, can also be applied to scenarios of non-uniform noise power and related noise.Simulation results show that proposed algorithms outperform the ACE algorithms with iteration.3. The paper proposed a joint adaptive estimation and compensation algorithm basedon signal combining for poor performance of traditional adaptive time delay estimationalgorithm in multi-antenna signal delay alignment. The paper theoretically proves theconvergence of the algorithm and the progressive unbiasedness of the estimation. Thepaper gives the theoretical analysis of variance performance. Theoretical analysis andsimulation results show that the new algorithm effectively reduces the variance of theadaptive time delay estimation, and it improves the alignment performance ofmulti-antenna signals compared with the conventional adaptive algorithm. The traditionaladaptive time delay estimation algorithms estimate and compensate difference of time delaybetween two signals. There are greater than or equal to3signal delay differences needs to beestimated in applications such as multi-antenna signal combining, targeting locating. Theproposed algorithm uses the combined signal as the common reference signal for joint adaptivetime delay estimate. The algorithm adaptive adjusts the filtering parameters of each signalrespectively based on the error with the reference signal. Ultimately, the algorithm achieves theestimate of time delays and the alignment of all signals through the iterative. Performance derivation and numerical simulation results show that the algorithm is effective to reduce thetime delay estimation variance, and to improve signal alignment performance.4. Frequency offset between the multi-antenna signals is transformed into lineartime-varying phase shift. This paper derives an asymptotic unbiased constraint adaptivephase shift estimation and compensation algorithm. The algorithm is extended to thefrequency difference estimation and compensation. Two frequency difference estimationalgorithms with variance performance close to the CRLB are proposed. According to theWiener solution in the form of phase shift signals, the paper proposes an asymptotic unbiasedconstraint adaptive phase shift estimation and compensation algorithm. Based on estimationresults of the phase shift estimation, this paper proposed two frequency difference estimationalgorithms using the time-averaged method and linear fitting method. The simulation resultsshow that the frequency difference estimation algorithm based on the average method is anasymptotic unbiased algorithm, with the variance lower than the CRLB. And the algorithm basedon the linear fit method is an unbiased algorithm with the variance close to the CRLB.5. Maximum ratio combining diversity algorithm in multipath fading channels can notmaximize the full-band signal-to-noise ratio of the combined signal. According to theoptimal combining theory of multipath fading signals, this paper proposed a sub-bandcombining algorithm using the classical cosine-modulated filter banks. Simulation resultsindicate that the demodulation performance of sub-band combined signal outperforms themaximum ratio combining diversity signal. According to the optimal combining model of thefading channel, the cosine modulated filter banks were introduced into the signal combining.And a sub-band combining algorithm is proposed. The classical cosine modulated analysis filterbanks are used to narrow band the received signals. And the corresponding sub-band signals arecombined with the criterion of maximum ratio combining. The algorithm is transparent to thesignal modulation method, without timing synchronization, makes the combined signal tomaximize the signal-to-noise ratio, and improves the quality of signal demodulation. Simulationresults indicate that the demodulation performance of sub-band combined algorithm outperformsthe maximum ratio combining diversity algorithm. |
PDF Full Text Request