Passive Localization Using Time Difference of Arrival and Frequency Difference of ArrivalWC

In order to improve the accuracy and engineering feasibility of four-Satellite localization system, the frequency difference measurement is introduced to the four-Satellite TDOA (Time Difference of Arrival) localization algorithm. The TDOA/FDOA (Frequency Difference of Arrival) localization algorithm is used to optimize the GDOP (geometric dilution of precision) of four-Satellite localization. The simulation results show that the absolute position measurement accuracy has little influence on TDOA/FDOA localization accuracy as compared with TDOA localization. Under the same conditions, TDOA/FDOA localization has better accuracy and its GDOP shows more uniform distribution in diamond configuration case. The localization accuracy of four-Satellite TDOA/FDOA is better than the localization accuracy of four-Satellite TDOA.

Localization in modified polar representation: hybrid measurements and closed-form solution

Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) unifies near-field and farfield models, alleviating the thresholding effect. Current localization methods in MPR based on the angle of arrival (AOA) and time difference of arrival (TDOA) measurements resort to semidefinite relaxation (SDR) and Gauss-Newton iteration, which are computationally complex and face the possible diverge problem. This paper formulates a pseudo linear equation between the measurements and the unknown MPR position,which leads to a closed-form solution for the hybrid TDOA-AOA localization problem, namely hybrid constrained optimization(HCO). HCO attains Cramér-Rao bound (CRB)-level accuracy for mild Gaussian noise. Compared with the existing closed-form solutions for the hybrid TDOA-AOA case, HCO provides comparable performance to the hybrid generalized trust region subproblem (HGTRS) solution and is better than the hybrid successive unconstrained minimization (HSUM) solution in large noise region. Its computational complexity is lower than that of HGTRS. Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator (MLE) attains if the noise is small, but the MLE deviates from CRB earlier.

Aviation multi-station collaborative detecting based on time-frequency correlation of data-link

As an important application research topic of the intelligent aviation multi-station, collaborative detecting must overcome the problem of scouting measurement with status of 'fragmentation', and the NP-hardness problem of matching association between target and measurement in the process of scouting to data-link, which has complicated technical architecture of network construction. In this paper, taking advantage of cooperation mechanism on signal level in the aviation multi-station sympathetic network, a method of obtaining target time difference of arrival (TDOA) measurement using multi-station collaborative detecting based on time-frequency association is proposed. The method can not only achieve matching between target and its measurement, but also obtain TDOA measurement by further evolutionary transaction through refreshing sequential pulse time of arrival (TOA) measurement matrix for matching and correlating. Simulation results show that the accuracy of TDOA measurement has significant superiority over TOA, and detection probability of false TDOA measurement introduced by noise and fake measurement can be reduced effectively.

Passive Localization of Signal Source Based on UAVs in Complex Environment

For the influence caused by multipath fading and non-line-of-sight(NLOS)transmission,it is challenging to accurately localize a moving signal source in complex environment by using the wireless sensor network(WSN)on the ground.In this paper,we establish a special WSN in the sky to address this challenge,where each sensor is loaded on an unmanned aerial vehicle(UAV)and the operation center of all the UAVs is fixed on the ground.Based on the analyzing of the optimal distribution and the position error calibration of all the sensors,we formulate the localization scheme to estimate the position of the target source,which combines the time difference of arrival(TDOA)method and the frequency difference of arrival(FDOA)method.Then by employing the semidefinite programming approach,we accurately obtain the position and velocity of the signal source.In the simulation,the validity of the proposed method is verified through the performance comparison.

Adaptive Cascaded High-Resolution Source Localization Based on Collaboration of Multi-UAVs

Effective information fusion is very important in hybrid source localization. In this paper, the performance analysis of conventional joint direction of arrival(DOA) and time difference of arrival(TDOA) system is derived and it is shown that this hybrid system may inferior to the single system when the ratio of angular measurements error to distance measurements error exceeds a threshold. To avoid this problem, an effective DOA/TDOA adaptive cascaded(DTAC) technique is presented. The rotation feature of UAVs and spatial filtering technique are applied to gain the signal-to-noise ratio(SNR), which leads to more accurate estimation of time delay by using DOAs. Nevertheless, the time delay estimation precision is still limited by the sampling frequency, which is constrained by the finite load of UAV. To break through the limitation, an enhanced self-delay-compensation(SDC) method is proposed, which aims at detecting the overlooked time delay within the sampling interval by adding a tiny time delay. Finally, the position of the source is estimated by the Chan algorithm. Compared to DOA-only algorithm, TDOA-only algorithm and joint DOA/TDOA(JDT) algorithm, the proposed method shows better localization accuracy regardless of different SNRs and sampling frequencies. Numerical simulations are presented to validate the effectiveness and robustness of the proposed algorithm.

Non-Linear Localization Algorithm Based on Newton Iterations

In order to improve the performance of time difference of arrival(TDOA)localization,a nonlinear least squares algorithm is proposed in this paper.Firstly,based on the criterion of the minimized sum of square error of time difference of arrival,the location estimation is expressed as an optimal problem of a non-linear programming.Then,an initial point is obtained using the semi-definite programming.And finally,the location is extracted from the local optimal solution acquired by Newton iterations.Simulation results show that when the number of anchor nodes is large,the performance of the proposed algorithm will be significantly better than that of semi-definite programming approach with the increase of measurement noise.

Novel method for radial velocity difference estimation of moving targets with wideband signals

For the frequency difference of arrival （FDOA） esti-mation in passive location, this paper transforms the frequency difference estimation into the radial velocity difference estimation, which is difficult to achieve a high accuracy due to the mismatch between the sampling period and the pulse repetition interval. The proposed algorithm firstly estimates the point-in-time that each pulse arrives at two receivers accurately. Secondly two time of arrival （TOA） sequences are subtracted. And final y the radial ve-locity difference of a target relative to two stations with the least square method is estimated. This algorithm only needs accurate estimation of the time delay between pulses and is not influenced by parameters such as frequency and modulation mode. It avoids transmitting a large amount of data between two stations in real time. Simulation results corroborate that the performance is bet-ter than the arithmetic average of the Cramer-Rao lower bound （CRLB） for monopulse under suitable conditions.

Semidefinite programming approach for TDOA/GROA based source localization

Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.

A newself-localization method for wireless sensor networks

Many applications of wireless sensor networks can benefit from fine-grained localization. In this paper, we proposed an accurate, distributed localization method based on the time difference between radio signal and sound wave. In a trilateration, each node adaptively chooses a neighborhood of sensors and updates its position estimate with trilateration, and then passes this update to neighboring sensors. Application examples demonstrate that the proposed method is more robust and accurate in localizing node than the previous proposals and it can achieve comparable results using much fewer anchor nodes than the previous methods.

无需先验测量误差的定位节点选择方法

针对现有定位节点选择算法依赖先验测量误差的问题,研究了一种不依赖先验测量误差的时差与频差无源定位节点选择方法,该方法将两步加权最小二乘定位算法中目标估计误差协方差作为目标函数。在给定可用定位节点数目的情况下,通过引入一组布尔量构建关于目标函数为带约束的最小估计误差寻优问题,通过半定松弛技术将非凸问题转为半定规划问题进行求解,依据最小化问题特性,使得所提算法不依赖先验测量误差。仿真结果表明,不依赖测量误差的方法与穷举搜索法相比在定位性能上无太大差别;相比于穷尽搜索算法,所提方法复杂度低、实时性好。同时,仿真结果进一步表明在对运动目标定位过程中,及时调整定位节点组合的必要性。

基于广义互相关时延估计(TDOA)算法的声源定位跟踪系统设计与实现

为实现在二维平面内对声源进行实时定位和动态跟踪,运用时延估计(TDOA)互相关算法设计一种原声监听头阵列的CC-TDOA声源定位跟踪系统,实现对多个原声监听头进行同步采样,再进行信号放大及运算处理,运用LCD屏实时显示目标声源的距离和方位角度,同时运用二维云台控制激光笔对准声源,并持续动态跟踪声源。模拟仿真及真实环境实验测试表明,采用基于到达时间差的互相关定位算法,计算量小,精度较高,测试角度误差小于2o,距离误差小于1.2%,可以满足多种智能应用场合中声源实时定位与跟踪的要求。

基于频域阈值处理广义互相关和空间筛选的多源局部放电定位方法

该文针对电力设备的多源局部放电定位问题,提出了基于频域阈值处理广义互相关(frequency domain threshold processing generalized cross-correlation,FDTP-GCC)和空间筛选的多源局部放电定位方法。为实现多源局部放电超声信号的同源时间差提取问题,该文提出了FDTP-GCC算法,该算法直接应用于超声传感设备采集的原始信号,在频域内通过设置幅度谱阈值排除干扰频段,幅度谱归一化突出多源信号时间差的冲激函数特征,直接得到对应同源信号的时间差。通过仿真证明了FDTP-GCC算法进行时间差估计的高准确性、高稳定性、高抗噪能力。为解决FDTP-GCC算法带来的同源时间差分离的问题,该文提出了基于空间筛选的多源局部放电定位方法,并分析了采用基于解析解的算法和智能算法进行空间筛选的定位性能。最后采用双局部放电源进行了定位试验,试验结果表明该文方法提取的时间差误差均在1μs以内,两个局部放电源的定位误差为20.53cm和4.93cm。

基于银膜的自准直光纤法布里-珀罗腔设计及声源定位研究

提出一种基于银膜的自准直外腔式光纤法布里-珀罗(EFPI)干涉仪声传感器阵列,用于中低频声信号的检测以及二维平面声源定位。传感器阵列由三个相同结构的EFPI组成,结构简单、制作简便。银膜的应用和准直器的引入提高了传感器的声压灵敏度,扩大了声源定位范围。实验结果表明:单个传感器声压灵敏度为185 mV/Pa,最小可探测声压为52.7μPa/Hz^(1/2)@500 Hz。在声源指向性实验中,声源设置在传感器正前方时,其声压灵敏度达到185 mV/Pa,声源设置在传感器侧面(90°,270°)时,其声压灵敏度衰减仅为4.5%。传感器阵列结合到达时间差技术,成功实现高精度的声源定位,系统的理论空间分辨率为0.71 cm,最大定位误差不超过2.8 cm,定位范围为200 cm×200 cm,具有成本低、实用性高等优点。

未知信号传播速度的TDOA定位方法研究

研究了未知信号传播速度时的到达时间差(Time-Difference-Of-Arrival,TDOA)定位问题,提出两种联合估计信号传播速度和目标位置的定位方法。第一种方法为两步加权最小二乘方法。在该方法中,首先不考虑变量之间关系,得到一个关于未知变量的初始加权最小二乘估计。为改进第一步估计的性能,第二步考虑第一步估计中变量之间的关系,将其转换为一个标准的广义信赖域子问题,最终获得更高精度的估计性能。第二种方法为半正定松弛方法,通过构建非线性非凸加权最小二乘问题,然后利用半正定松弛技术将其松弛为凸的半正定规划问题,容易求解。仿真结果表明,所提两种方法均能够在高斯噪声下,且噪声不太大时达到克拉美-罗界。

传感器位置误差下TDOA-DOA水下目标被动定位算法

针对水下目标被动定位中传感器位置误差带来的定位精度不高的问题,提出了一种基于两步最小二乘的到达时间差波达方向(time difference of arrival-direction of arrival,TDOA-DOA)目标定位算法。首先,构建TDOA-DOA理想化无误差模型,并利用最小二乘算法对目标位置进行粗估计。其次,考虑测量误差和传感器位置误差,构建目标定位误差和传感器位置的联合方程,并利用加权最小二乘求解。最后,利用目标定位误差对目标位置粗估计值进行修正,得到更精确的定位结果。仿真实验表明,所提算法可对目标位置和传感器位置进行联合估计,相较于已有算法具有更高的定位精度,更适用于传感器位置存在误差情况下的水下目标定位。

已知波形信号的时差频差估计方法研究与性能分析

借助深入分析和长期积累,非合作定位系统可以获得关于电视、广播、导航等电磁信号波形的丰富先验信息,对这些信息加以利用有望大幅提高对信号参数的估计精度。该文针对导航、通信等欺骗干扰源及非法用户的无源定位需求,在观测站与辐射源之间存在相对运动的场景下,围绕已知波形信号开展时差频差估计问题研究。首先,通过引入已知的信号波形信息对观测站接收数据进行建模,直观呈现了观测数据与不同观测站信号相对于原始信号时延、频移的关系。随后,充分利用入射信号波形已知这一有利条件,通过估计两个观测站各自的时延和频移,并对不同观测站上的估计结果进行差分,提出了一种针对已知波形信号的双站时差、频差估计方法。在此基础上,该文分析了已知波形信号时频差估计精度的理论下界,揭示了时频差估计精度受观测站接收信号幅度等因素的影响情况,并进一步分析了双站时频差估计精度与单通道时延、频移估计精度之间的关系。最后,借助仿真实验验证了论文所提出的时频差估计方法在不同环境中的参数估计性能,以及信号波形先验信息的利用给时频差参数估计精度带来的提升情况。仿真结果表明,在所设定的信号环境中,该文所提出的方法对低信噪比的适应能力比基于互模糊函数的传统方法增强了15 dB左右;当该方法的参数估计性能达到收敛之后,其时差频差估计精度与CRLB非常吻合。

一种改进组合加权的TDOA室内二维定位算法

针对现有组合加权算法对定位区域边缘的目标定位时精度较低的问题,在现有组合加权算法的基础上提出了一种改进算法。首先,将基站分组,以到达时差算法得到目标位置的多个估计结果;其次,计算各估计结果之间距离值并排序,以滑动窗口法判断是否存在基站组出现异常定位估计;最后,当任意基站组的定位结果发生异常时,使用目标位置估计结果及其估计克拉美罗下界值设计两个加权步骤的权值,通过二步组合加权算法得到最终定位结果。仿真结果表明,所提算法有效减少了原组合加权算法对定位区域边缘的目标定位时的误差,当测量噪声标准差为0.8 m时,所提算法相较于原算法在正方形边缘区域定位均方根误差减小了0.35 m;在定位狭窄矩形区域时,所提算法平均定位均方根误差减小了0.11 m。

基于多波束/TDOA技术的双星干扰源定位

单星多波束干扰源定位方法需要在3个以上的同频波束都能接收到干扰源信号的情况下才能实现定位。由于同频波束的数量有限以及干扰源位置的随机性,往往会出现能接收到干扰源信号的同频波束少于3个,或接收增益过低影响定位精度的情况。【目的】针对只有两个可用波束的定位问题以及进一步提高干扰源的定位精度,提出了通过邻星与主星建立基于多波束天线与到达时间差(TDOA)技术的双星联合定位模型。【方法】通过建立双星联合定位模型,并联立出定位方程组证明了该模型的可行性。同时,基于定位几何稀疏精度因子(GDOP)对定位模型的接收增益误差、波束指向误差、到达时间差误差以及位置预测误差4个方面进行了定位误差分析,并使用Matlab进行仿真对比。【结果】仿真实验表明,基于多波束天线与TDOA技术的双星联合定位方法的定位精度明显优于传统的单星多波束定位方法。【结论】基于多波束天线与TDOA技术的双星联合定位方法利用了TDOA对定位精度影响较小的优势,使其代替部分波束参与到卫星定位中,不仅减少了对定位波束数量的需求,而且减少了测量增益的误差与波束指向的误差,大大提高了定位的精度并提升了可靠性,较单星多波束定位更具优势。

基于GA-BP神经网络的多无人机协同目标定位

无人机作为高机动平台,往往存在较大的自身位置误差。针对该情况下特定区域内的辐射源目标,提出了一种遗传算法优化反向传播(genetic algorithm optimized back-propagation,GA-BP)神经网络的实时定位方法。首先从特定区域中获取不同已知目标的位置信息作为网络的期望输出,并计算它们的到达时间差作为网络的输入,构建训练数据集;然后利用遗传算法的自适应性,优化BP神经网络初始权重和阈值,使其能够快速跳出局部最优解,实现高精度定位。通过训练得到相应的GA-BP网络模型,未知目标可以通过该模型进行实时定位。仿真实验将该算法与两步加权最小二乘算法以及BP神经网络的定位结果进行对比,结果表明所提的方法定位精度更接近克拉美罗界。

矿山微震震源定位精度分析

定量研究初至拾取精度和速度模型对矿山微震震源定位精度的影响直接关系到矿山开采质量评估和矿山风险预警。由于矿山微地震监测主要接收岩石破裂激发的P波,故往往采用基于P波到时的同型波时差法进行震源定位,基于数值模拟方法研究了速度模型和初至拾取精度对同型波时差法矿山微震震源定位精度的影响。结果表明:反演速度模型偏离准确速度模型的误差逐渐增大时,震源定位精度表现出降低的趋势;初至拾取精度降低时,震源定位的精度随之降低;当速度模型误差和初至拾取误差分别控制在200 m/s和10 ms以内时,同型波时差法在矿山微震震源定位中的误差整体控制在20 m以内,可以满足矿山微震质量控制和风险预警的要求。