【作者】 张刚兵；

【导师】 刘渝；

【作者基本信息】 南京航空航天大学 ， 信号与信息处理， 2010， 博士

【摘要】 以质点运动学原理为基础,本文研究了利用角度、角度变化率以及多普勒频率变化率进行单站无源定位中的参数估计和非线性滤波等问题,主要内容如下：(1)介绍了利用切向运动和径向运动进行无源测距的原理,对两种无源测距方法做了误差分析,为工程实现指明了系统对各参数精度的要求。(2)研究了基于相位干涉仪阵列的波达角估计方法。先在无模糊范围内确定各双基线干涉仪的所有相位模糊数解,然后利用公共基线的解逐步缩小范围,最后确定各基线的唯一解以实现解相位模糊。对宽带信号,先作FFT变换,选取信号在6dB带宽内的谱线,每根谱线看作个单频信号,采用窄带鉴相方法对各单频信号分别鉴相并估计该频率点上的延时,最后对各延时估计值进行加权平均。证明了信号在各频率点上的能量与频率平方的乘积是最佳加权系数。(3)研究了相参脉冲串频率估计算法。提出了适用于单一重复频率和重频参差相参脉冲串信号的频率估计算法,通过脉内相关积累,提高了信噪比,利用实现相参频率估计的条件推导了信噪比门限的解析表达式,给出了单一重频脉冲串频率估计信噪比门限与信号样本总数、占空比之间的关系。对于重频参差相参脉冲串信号,在脉内相关积累之后,对新序列的相位差按参差重数抽样平均,再利用重频参差比解相位模糊,扩大了频偏允许范围,降低了算法的信噪比门限。给出了重频参差脉冲串频率估计信噪比门限与信号样本总数、参差重数、参差比之间的关系。(4)研究了直接利用相参脉冲串进行多普勒频率变化率估计的算法,选取准最佳算法估计频率,对各脉冲进行脉内相关积累,将相参脉冲串变换成一个调频斜率是多普勒频率变化率的线性调频信号序列,解调该线性调频信号就能获得多普勒频率变化率的估计值。(5)研究了适用于单站无源定位与跟踪的非线性滤波算法。提出了一种新的迭代滤波算法,以加快算法的收敛速度和提高滤波的估计精度。通过反向预测与更新提高了上一时刻状态估计的精度,减小了当前时刻的状态预测误差。利用更准确的初始条件经过正向预测与更新,能得到当前状态更精确的估计值。更多还原

【Abstract】 Based on the particle kinematics theory, the dissertation studies the key technologies of single observer passive location via direction of arrival (DOA), rate of bearing and Doppler frequency rate-of-change and nonlinear filter. The principal contributions are summarized as follows:Firstly, the principles of tangential and radial movement measureing distance were introduced, and the error analysis of the passive ranging was performed.Secondly, the method of estimating the direction of arrival was proposed. The phase ambiguity numbers were obtained within the unambiguous scope of each double-baseline direction finding system and the unique set of phase ambiguity number could be acquired through the commnon baselines between those double-baseline interferometers. A method of wideband signal’s DOA estimation was addressed. The wideband signal was decomposed into multiple single-tone signals through FFT. The spectrums of FFT within 6dB bandwidth were used to estimate the time-delays individually, and then the accurate time-delay estimate was given by weighting these estimates. The optimal weighting coefficients were given.Thirdly, the estimation algorithms for frequency of coherent pulse train were addressed. High accurate frequency estimation algorithms of coherent pulse train with single and staggered pulse repetition frequency (PRF) were presented. The output signal-to-noise ratio (SNR) was enhanced through intrapulse correlation accumulation. The requirement for coherent frequency estimation was analyzed and the close-form of the input SNR threshold was derived. The relationship of the SNR threshold, the total number of samples and the duty cycle was given for single PRF coherent pulse train. As far as staggered PRF coherent pulse train was concerned, resampling and averaging the phase difference of the sequence according to the number of stagger ratio of the coherent pulse train could reduce the equivalent phase noise. Resolving phase ambiguity through the stagger ratio could enlarge the scope of the frequency difference and decrease the SNR threshold. The relationship of the SNR threshold, the total number of samples, the number of stagger ratio and the stagger ratio was presented.Fourthly, an alternative Doppler frequency rate-of-change estimation algorithm for coherent pulse train was proposed. A suboptimal frequency estimation method was applied to estimate the frequency and the error of frequency estimation was reduced. A new chirp signal was obtained after intrapulse correlation accumulation of the coherent pulse train, of which the frequency rate is the Doppler frequency rate-of-change. Accordingly, it is possible to estimate the Doppler frequency rate-of-change precisely under lower SNR.Fifthly, a novel nonlinear filter was proposed to improve convergence speed and estimation accuracy. The backward prediction and the state update procedure improve the estimation accuracy of the last state estimate and reduce the current state prediction error. A more accurate current state estimate could be gotten with more precise initial condition. Simulation results show that the performance of the proposed algorithm outperforms that of the conventional IUKF in both convergence speed and estimation accuracy.更多还原