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双语推荐：DOA估计

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针对常规矢量传感器MIMO雷达没有利用发射极化信息导致波达方向(DOA)估计精度较差的问题,该文提出一种克拉美罗界(CRB)最小化的发射极化优化算法。首先建立矢量传感器MIMO雷达的接收信号模型;然后分析固定发射极化矢量传感器MIMO雷达DOA估计算法的不足;接着推导任意发射极化状态下的CRB,计算最小CRB对应的极化状态;最后利用该优化极化状态采用固定极化DOA估计算法得到DOA估计。该算法的DOA估计精度高于固定极化DOA估计算法。且该算法的2维DOA估计可自动配对,发射电磁矢量传感天线位置可任意。仿真结果证明了该算法的有效性。
For the issue of the bad Direction Of Arrival (DOA) estimation accuracy entailed by not utilizing the transmitted polarization information in electromagnetic vector sensor MIMO radar, a transmitted polarization optimization algorithm is proposed based on minimizing the Cramér-Rao Bound (CRB). First, the signal model of electromagnetic vector sensor MIMO radar for DOA estimation is proposed. Second, the drawbacks of the existing fixed polarization DOA estimation algorithm are analyzed. Third, the CRB under arbitrary polarization is derived and the polarization state corresponding to the minimum CRB is computed. Finally, with the optimal polarization, the DOA can be estimated by the fixed polarization DOA estimation algorithm. The proposed algorithm can provide better estimation accuracy than the fixed polarization DOA estimation algorithm, and remain the advantages of automatic pairing between the two dimensional DOA estimation and arbitrary placement of the transmitted electromagnetic

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利用经典的2D-MUSIC算法对二维阵列的DOA估计进行了研究,在平面阵列数学模型以及2D-MUSIC算法的DOA估计模型基础上,以均匀平面阵列为例,对3种不同参数的DOA估计进行了计算机仿真,分析了仿真结果.得出了在不同参数变化趋势下DOA估计的相应变化情况。
This paper discussed the performance of classical two-dimensional DOA estimation with 2D-MUSIC, based on the mathematical model of planar array and 2D-MUSIC DOA estimation, Taking uniform planar array for example, comput-er simulation experiment was carried for the effect of three kinds of different parameters on 2-D DOA estimation, and the simulation results were analyzed. And also verification test about the corresponding algorithm performance under the differ-ent parameters was discussed.

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在DOA估计中，往往事先假设麦克风阵列的结构，然后通过改进DOA估计方法来提高定位精度，忽视了麦克风的摆放位置对 DOA 估计性能的影响。基于此，针对二维 DOA 估计，提出改进遗传优化算法，将空时滤波器系数和麦克风阵列结构分开，构造由二维 MUSIC空间谱函数欧式距离和优化后阵元个数共为变量的适应度函数，以 DOA 估计精度为停止条件，对均匀矩形阵、均匀圆形阵和均匀同心圆阵开展优化设计。仿真结果表明，采用所提方法优化后的阵列取得了较好的DOA估计性能。
The studies usually focus on how to give an effective method to improve position accuracy with the known-microphone array configurations for the DOA estimation.However,the position of the microphone influences the overall performance of DOA estimation. An improved genetic optimization algorithm is thus proposed for two-dimensional DOA estimation.Its fitness function consists of the Euclidean distance of the 2-D MUSIC spatial spectrum function and microphone numbers after optimization,which separates the space-time filter coefficients from microphone array configuration.And DOA estimation precision is adopted as stop condition of genetic algorithm. Uniform rectangular array,uniform circular array and uniform concentric circular array are used to carry out optimization design. The simulation results show that effective DOA estimation performances are obtained after optimization with the proposed method.

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针对均匀线阵（uniform linear array，ULA）互耦条件下混合信源的波达方向（direction of arrival， DOA）估计问题，基于联合对角化算法，提出了一种基于3步实现的 DOA 与互耦系数估计新算法。首先利用互耦矩阵的 Toeplitz 结构实现混合信源中独立信源的 DOA 及互耦系数的粗估计；然后结合斜投影及前后向空间平滑，实现混合信源 DOA 估计；最后以广义空间特征矩阵及混合信源 DOA 估计值为基础，提出一种非子空间类互耦系数自校正方法。计算机仿真结果表明，与同类算法相比，所提算法无论在 DOA 及互耦系数估计精度、还是在DOA 估计成功率方面，均具有明显的优势，且对于高斯背景噪声具有普适性。
Based on the joint approximative diagonalization of eigen matrix,a novel algorithm which in-cludes three steps is proposed to estimate the direction-of-arrival (DOA)of mixed signals and the mutual cou-pling coefficient of the uniform linear array (ULA)in presence of the mutual coupling error.Utilizing the To-eplitz structure of the mutual coupling matrix,the coarse estimates of the DOAs of the uncorrelated signals among the mixed signals and mutual coupling coefficients are firstly obtained.Then the DOA estimates of the mixed signals are obtained based on the combination of the oblique projection with forward and backward spatial smoothing methods.Finally,a non-subspace method for mutual coupling self-calibration is presented by utili-zing the estimates of the generalized spatial feature matrix and the estimated mixed signal DOAs.The computer simulation results indicate that the proposed algorithm has much better performance in DOAs and mutual cou-pling coefficient estimation and the s

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宽带波达方向(DOA)估计是宽带阵列信号处理领域的热点问题,当宽带信号的距离分辨率与宽带孔径可比拟时,孔径渡越效应会成为影响宽带波达方向估计精确度的重要因素。基于宽带阵列信号在空域与频域间呈现线性耦合的特点,利用梯形变换进行解耦处理,并对解耦后数据相干积累进行DOA估计。实验结果表明,通过对宽带孔径渡越效应的消除,宽带DOA估计精确度显著提高。该算法具有较低的计算复杂度和较高的DOA估计精确度,是一种有效的宽带DOA估计算法。
Direction Of Arrival(DOA) estimation for wideband signal is one of the hot issues in wideband array signal processing. Aperture fill effect is an important factor to affect the estimation precision of wideband DOA when wideband signal range resolution and wideband aperture are comparable. On the basis of the linear coupling characteristic between spatial domain and frequency domain of wideband array signal, Keystone transform is adopted for decoupling. Then the decoupled data is used for DOA estimation with coherent accumulation method. The results of simulation show that the precision of DOA estimation is improved remarkably by eliminating the effect of aperture fill effect. The proposed algorithm is an effective method due to its lower computational complexity and higher DOA estimation precision.

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对智能天线及波达方向(DOA)估计的应用进行了简要阐述,接着就智能天线中相干信号采用多信号分类算法及其修正、改进方法进行DOA估计研究分析;通过详细论证及相应的模拟仿真实验验证,表明所推介的两种基于经典多信号分类算法的修正和改进算法在DOA估计的相关方面有一定的优越性,从而可以为DOA估计的应用提供参考.
The application of smart antenna and the Direction of Arrival (DOA) estimated a brief summary and introduction,followed by to DOA estimation research and analysis on the smart antenna coherent signals using multiple signal classification algorithm and its amendments,improvements.Through a detailed demonstration and simulation experiments show that the two promote relevant aspects of the DOA estimation algorithm based on the classic multi-signal classification algorithm fixes and improvements have certain advantages,which can provide a reference for DOA estimation application.

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从稀疏信号重建角度提出了一种改进的波达方向（DOA）估计方法。由于最小冗余线阵（MRLA）能以较少的阵元数获得较大的阵列孔径，将MRLA与?1-SVD方法相结合估计信号的DOA。仿真结果表明，经多次实验验证，所提方法是有效的，相比?1-SVD方法可以估计出更多信源的DOA，并且可以用较少的阵元数估计更多的信源DOA，具有信源过载能力。
This paper proposes a modified Direction of Arrival(DOA)estimation method based on Minimum Redundancy Linear Array(MRLA)from the sparse signal reconstruction perspective. According to the structure feature of MRLA that obtaining larger antenna aperture through a smaller number of array sensors, MRLA is combined with ?1-SVD method to estimate signal DOAs. Simulations demonstrate that the proposed method is effective, and compared with ?1-SVD meth-od it can estimate more DOAs of signal source, and it is capable of estimating more DOAs with fewer antenna elements.

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波达方向(direction of arrival,DOA)估计问题是单基地多输入多输出(multiple input multiple output,MIMO)雷达信号处理中的一个关键问题。在低信噪比、低快拍数的情况下,常规DOA估计算法的性能会严重下降。针对此问题,提出一种新的DOA估计算法:降维酉旋转不变性信号参数估计技术算法。该算法首先通过降维变换将MIMO雷达数据变换至低维信号空间,然后在该低维信号空间构造实值旋转不变性方程估计目标的DOA。仿真结果表明该方法能够在低信噪比、低快拍数的环境下获得较常规ESPRIT方法更高的DOA估计精度,同时具有更低的运算量。
Direction of arrival (DOA)estimation is an important issue in monostatic multiple input multiple-output (MIMO)radar signal processing.For conventional DOA estimation methods,performance degradation occurs with low signal-to-noise ratio and small samples.A novel DOA estimation method called reduced-dimen-sional unitary estimation of signal parameters via rotational invariance technique (ESPRIT)is proposed.Firstly,the received data is transformed into a low dimensional signal space via the reduced-dimensional transformation.Then,the real-valued rotational invariance equations for the low dimensional signal subspace are constructed to estimate DOAs. Numerical examples are given to demonstrate that the proposed algorithm can provide increased DOA estimation accu-racy and reduced computational complexity compared with the conventional ESPRIT algorithms.

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传统的DOA估计方法不能有效分辨相干源目标;稀疏重构方法能够处理相干源的DOA估计问题,但现有稀疏重构方法大都是针对无噪声或仅存在观测噪声系统提出的,估计性能有待提高。对于同时存在观测噪声和模型噪声的多观测量模型,提出了一种基于FOCUSS稀疏重构的改进算法,可鲁棒地处理相干源、非相干源的DOA估计问题,有效提高分辨力和估计精度等估计性能。给出了DOA估计的稀疏信号模型以及新算法的推导过程,仿真实验证明了新算法在与其他算法对比时的优越性。
Traditional DOA-estimation method cannot distinguish coherent sources effectively,the methods based on sparse reconstruction can resolve the problem of incoherent -resource DOA estimation,however,due to designed against to free-noise or only measurement-noise system,the estimation performance is improved. An improved algorithm is developed to DOA estimation based on FOCUSS against the Multiple-Measurement-Vectors (MMV) model where measurement noise and model noise both exist. It can deal with DOA estimation of both coherent and incoherent resources robustly,and improve resolution power and estimation accuracy obviously. The sparse signal model of DOA estimation,the derivation of new algorithm are shown,and finally is proved the advantage of our method comparing to other methods through simulation experiments.

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针对声矢量DOA估计问题,根据声矢量阵的特点,结合MVDR算法的思想,本文提出了一种声矢量阵DOA估计新算法。该算法将声矢量阵振速通道的数据协方差矩阵相加得到新的协方差矩阵,然后结合声矢量阵声压通道的数据协方差矩阵,通过类似于V-MVDR算法的角度扫描过程实现目标的DOA估计,该算法无需已知信源数目且不需要特征值分解运算,具有良好的DOA方位估计和分辨性能,计算机仿真结果验证了本文算法的有效性。
Aiming at the problem of acoustic vector DOAs estimation, according to the characters of acoustic vector sensor array and by combining with the MVDR algorithm, this paper puts forward a new algorithm of a-coustic vector sensor array DOAs estimation. This algorithm makes an addition of the data covariance matrix of vi-bration velocity control of acoustic vector sensor array to get a new covariance matrix. Then, by combining with it, and through the angle scanning process similar to V-MVDR algorithm, we realize the DOAs estimation. This algo-rithm does not need to know the amount of signal sources or characteristic value decomposition operation. It owns good DOAs estimation and resolution performance. The computer simulation results demonstrate the effectiveness of the proposed algorithm.

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