Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characte...Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.展开更多
A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization f...A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.展开更多
相较于传统的两步法,动力学一步法能充分利用观测数据的原始信息,理论上可获得更合理的时变重力场产品,同时也因其涉及的参数维度更多样、函数模型更复杂,一直是当前研究的热点和难点.本文研究并实现了动力学一步法恢复时变重力场,给出...相较于传统的两步法,动力学一步法能充分利用观测数据的原始信息,理论上可获得更合理的时变重力场产品,同时也因其涉及的参数维度更多样、函数模型更复杂,一直是当前研究的热点和难点.本文研究并实现了动力学一步法恢复时变重力场,给出了合理的数据处理策略,而后基于GRACE-FO(GRACE Follow-On)星载GPS数据和KBR(K/Ka Band Ranging)距离变率数据反演了2021—2022年60阶全球月时变重力场模型.对于一步法中诸多技术细节,本文重点分析了先验权和经验参数对轨道确定和模型反演的影响,研究表明:当采用30 s采样率的GPS数据时,需适当对GPS数据降权,以免引入过多噪声,码伪距、载波相位和KBR距离变率数据的先验权比应为1:104:1014;为了保证轨道和模型质量,在反演过程中有必要引入经验参数以吸收残余的摄动力误差,相较其他经验参数(分段周期经验加速度、几何经验参数),分段常经验加速度在保证定轨精度的同时可更有效地吸收模型中的噪声.此外,在采用相同动力学参数配置时,动力学一步法反演的时变重力场模型无论是与官方模型的一致性还是内符合精度均优于两步法.最后,综合评估了整个时间跨度的轨道和时变重力场模型质量,结果显示,动力学一步法确定的轨道可满足厘米级需求,双星的卫星激光测距残差标准差均为1.6 cm,重力场模型与官方机构CSR(Center for Space Research)、JPL(Jet Propulsion Laboratory)、GFZ(GeoForschungsZentrum Potsdam)最新发布的RL06.1模型一致性较好,在保留完整时变信号特征的前提下,噪声表现与CSR模型相当,优于JPL、GFZ模型.展开更多
基金Projects(41204079,41504086)supported by the National Natural Science Foundation of ChinaProject(20160101281JC)supported by the Natural Science Foundation of Jilin Province,ChinaProjects(2016M590258,2015T80301)supported by the Postdoctoral Science Foundation of China
文摘Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.
基金supported by the National Natural Science Foundation of China(61571131 11604055)
文摘A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.
文摘相较于传统的两步法,动力学一步法能充分利用观测数据的原始信息,理论上可获得更合理的时变重力场产品,同时也因其涉及的参数维度更多样、函数模型更复杂,一直是当前研究的热点和难点.本文研究并实现了动力学一步法恢复时变重力场,给出了合理的数据处理策略,而后基于GRACE-FO(GRACE Follow-On)星载GPS数据和KBR(K/Ka Band Ranging)距离变率数据反演了2021—2022年60阶全球月时变重力场模型.对于一步法中诸多技术细节,本文重点分析了先验权和经验参数对轨道确定和模型反演的影响,研究表明:当采用30 s采样率的GPS数据时,需适当对GPS数据降权,以免引入过多噪声,码伪距、载波相位和KBR距离变率数据的先验权比应为1:104:1014;为了保证轨道和模型质量,在反演过程中有必要引入经验参数以吸收残余的摄动力误差,相较其他经验参数(分段周期经验加速度、几何经验参数),分段常经验加速度在保证定轨精度的同时可更有效地吸收模型中的噪声.此外,在采用相同动力学参数配置时,动力学一步法反演的时变重力场模型无论是与官方模型的一致性还是内符合精度均优于两步法.最后,综合评估了整个时间跨度的轨道和时变重力场模型质量,结果显示,动力学一步法确定的轨道可满足厘米级需求,双星的卫星激光测距残差标准差均为1.6 cm,重力场模型与官方机构CSR(Center for Space Research)、JPL(Jet Propulsion Laboratory)、GFZ(GeoForschungsZentrum Potsdam)最新发布的RL06.1模型一致性较好,在保留完整时变信号特征的前提下,噪声表现与CSR模型相当,优于JPL、GFZ模型.
