In this paper, the k-partitioning problem with partition matroid constraint is considered. LPT algorithm is modified to fit the problem and its worst-ease performance is analyzed. The lower bounds of optimal solution ...In this paper, the k-partitioning problem with partition matroid constraint is considered. LPT algorithm is modified to fit the problem and its worst-ease performance is analyzed. The lower bounds of optimal solution for the min-max problem are given.展开更多
This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sam...This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.展开更多
Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investiga...Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investigated in this paper. In the first place, the assumptions of prior and posterior probability density function （PDF） with quantized innovations in the previous papers are analyzed. After that, an innovative Gaussian mixture estimator is proposed. On this basis, this paper presents a Gaussian mixture state estimation algorithm based on quantized innovations for WSNs. In order to evaluate and compare the performance of this kind of state estimation algo- rithms for WSNs, the posterior Cram6r-Rao lower bound （CRLB） with quantized innovations is put forward. Performance analysis and simulations show that the proposed Gaussian mixture state estimation algorithm is efficient than the others under the same number of quantization levels and the performance of these algorithms can be benchmarked by the theoretical lower bound.展开更多
The mobile channel is slow fading and time selective, thus the multiplicative and additive noise of the channel will smear the spectral line, or arouse Doppler spread. This spread will make the parameters estimation a...The mobile channel is slow fading and time selective, thus the multiplicative and additive noise of the channel will smear the spectral line, or arouse Doppler spread. This spread will make the parameters estimation accuracy degrade. The goal of this paper is to analytically assess this degradation when Carrier Frequency Offset (CFO) and Doppler shift exist jointly. Then the finite-sample Cramer-Rao Lower Bound (CRLB) is derived and close-form asymptotical expression is given for large-sample CRLB. These expressions give insights into the performance room for frequency estimation. Also the variance of Doppler shift estimator is simulated to illustrate the theoretical results.展开更多
This paper delves into the problem of optimal placement conditions for a group of agents collaboratively localizing a target using range-only or bearing-only measurements.The challenge in this study stems from the unc...This paper delves into the problem of optimal placement conditions for a group of agents collaboratively localizing a target using range-only or bearing-only measurements.The challenge in this study stems from the uncertainty associated with the positions of the agents,which may experience drift or disturbances during the target localization process.Initially,we derive the Cramer-Rao lower bound(CRLB)of the target position as the primary analytical metric.Subsequently,we establish the necessary and sufficient conditions for the optimal placement of agents.Based on these conditions,we analyze the maximal allowable agent position error for an expected mean squared error(MSE),providing valuable guidance for the selection of agent positioning sensors.The analytical findings are further validated through simulation experiments.展开更多
With the rapid development of commercial communications,the research on Radar-Communication Coexistence(RCC)systems is becoming a hot spot.The resource allocation techniques play a crucial role in the RCC systems.A pe...With the rapid development of commercial communications,the research on Radar-Communication Coexistence(RCC)systems is becoming a hot spot.The resource allocation techniques play a crucial role in the RCC systems.A performance-driven Joint Radar-target and Communication-user Assignment,along with Power and Subchannel Allocation(JRCAPSA)strategy,is proposed for an RCC network.The optimization model aims to minimize the sum of weighted Bayesian Cramer-Rao Lower Bounds(BCRLBs)of target state estimates for radar purpose.This is subject to constraints such as the Communication Data Rate(CDR)for communication purpose,the total power budget in each RCC system,assignment relationships,and the number of available subchannels.Considering that such a problem falls into the realm of Mixed Integer Programming(MIP),a Three-stage Iteratively Augment-based Optimization Method(TIAOM)is developed.The Communication-User Assignment(CUA),Communication Subchannel Allocation(SCA),and Radar-Target Assignment(RTA)feasible solution domains are iteratively expanded based on their importance,leading to the efficient acquisition of a suboptimal solution.Simulation results show the outperformance of the proposed JRCAPSA strategy,compared to the other benchmarks and the OPTI toolbox.The results also imply that the Bayesian Cramer-Rao Lower Bound(BCRLB)is a more stringent optimization metric for the achieved Mean Square Error(MSE),compared to Mutual Information(MI)and Signal-to-Interference-Noise Ratio(SINR).展开更多
针对基于到达时间差(Time Difference of Arrival,TDOA)和到达频率差(Frequency Difference of Arrival,FDOA)无源定位中存在的偏差问题,提出一种移动源偏差消除的时频域联合定位算法,即偏差减小的约束加权最小二乘(Bias Reduced-Constr...针对基于到达时间差(Time Difference of Arrival,TDOA)和到达频率差(Frequency Difference of Arrival,FDOA)无源定位中存在的偏差问题,提出一种移动源偏差消除的时频域联合定位算法,即偏差减小的约束加权最小二乘(Bias Reduced-Constrained Weighted Least Squares,BR-CWLS)算法。根据TDOA和FDOA联合测量模型,构建约束加权最小二乘(Constrained Weighted Least Squares,CWLS)问题,再引入二次约束条件,将其转化为具有偏差减小能力的BR-CWLS问题并完成求解。该二次约束条件综合考虑CWLS问题中涉及矩阵的二阶噪声和误差,以有效减小由于测量噪声及加权矩阵的近似估计产生的偏差。仿真结果表明,在噪声功率高达-30 dB时,所提算法的估计精度可以达到克拉美罗下界(Cramér-Rao Lower Bound,CRLB),且位置定位偏差相较于半正定松弛(Semidefinite Relaxation,SDR)算法、CWLS算法及两步加权最小二乘(Two-Step Weighted Least Squares,TSWLS)算法,分别降低了5.3 dB、5.4 dB及14.7 dB。展开更多
The muitipath signal resolution is reviewed in this paper.The problemsexisted and to be studied are pointed out.Theoretical analysis of the performance ofthe resolution for deterministic signal in the cases where the ...The muitipath signal resolution is reviewed in this paper.The problemsexisted and to be studied are pointed out.Theoretical analysis of the performance ofthe resolution for deterministic signal in the cases where the signal known or unknownis made.Their corresponding Cramer-Rao lower bounds(CRLB)are obtained.展开更多
To improve the resolution and accuracy of Direct Position Determination(DPD),this paper investigates the problem of positioning multiple emitters directly with a single moving Rotating Linear Array(RLA).Firstly,the ge...To improve the resolution and accuracy of Direct Position Determination(DPD),this paper investigates the problem of positioning multiple emitters directly with a single moving Rotating Linear Array(RLA).Firstly,the geometry of the RLA is formulated and analysed.According to its geometry,the intercepted noncoherent signals in multiple interception intervals are modeled.Correspondingly,the Multiple SIgnal Classification(MUSIC)based noncoherent DPD approach is proposed.Secondly,the synchronous coherent pulse signals are individually considered and formulated.And the coherent DPD approach which aims for localizing this special type of signal is presented by stacking all array responses at different interception intervals.Besides,we also derive the constrained Cramer-Rao Lower Bound(CRLB)expression for both noncoherent and coherent DPD with RLA under the constraint that the altitudes of the emitters are known.At last,computer simulations are included to examine the performance of the proposed approach.The results demonstrate that the localization accuracy and resolution of DPD with single moving linear array can be significantly improved by the array rotation.In addition,coherent DPD with RLA further improves the resolution and increases the maximum emitter number that can be localized compared with the noncoherent DPD with RLA.展开更多
This paper investigates the problem of target position estimation with a single-observer passive coherent location（PCL） system. An approach that combines angle with time difference of arrival（ATDOA） is used to est...This paper investigates the problem of target position estimation with a single-observer passive coherent location（PCL） system. An approach that combines angle with time difference of arrival（ATDOA） is used to estimate the location of a target. Compared with the TDOA-only method which needs two steps, the proposed method estimates the target position more directly. The constrained total least squares（CTLS） technique is applied in this approach. It achieves the Cramer–Rao lower bound（CRLB） when the parameter measurements are subject to small Gaussian-distributed errors. Performance analysis and the CRLB of this approach are also studied. Theory verifies that the ATDOA method gets a lower CRLB than the TDOA-only method with the same TDOA measuring error. It can also be seen that the position of the target affects estimating precision.At the same time, the locations of transmitters affect the precision and its gradient direction.Compared with the TDOA, the ATDOA method can obtain more precise target position estimation.Furthermore, the proposed method accomplishes target position estimation with a single transmitter,while the TDOA-only method needs at least four transmitters to get the target position. Furthermore,the transmitters＇ position errors also affect precision of estimation regularly.展开更多
The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination （DPD） methods, with combined Time Difference of Arrival （TDOA） and Frequency Difference...The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination （DPD） methods, with combined Time Difference of Arrival （TDOA） and Frequency Difference of Arrival （FDOA）, do not perform well under low Signal-to-Noise Ratio （SNR）, and worse still, the computation cost is difficult to accept when the computational capabilities are limited. To get better positioning performance, we present a new DPD algorithm that proves to be more computationally efficient and more precise for weak signals than the conventional approach. The algorithm partitions the signal received with the same receiver into multiple non-overlapping short-time signal segments, and then uses the TDOA, the FDOA and the coherency among the short-time signals to locate the target. The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load. A secondary but important result is a derivation of closed-form expressions of the Cramer-Rao Lower Bound （CRLB）. The simulation results show that the algorithm proposed in this paper outperforms the traditional DPD algorithms with more accurate results and higher computational efficiency, and especially at low SNR, it is more close to the CRLB.展开更多
A linear-correction least-squares（LCLS） estimation procedure is proposed for geolocation using frequency difference of arrival （FDOA） measurements only. We first analyze the measurements of FDOA, and further deriv...A linear-correction least-squares（LCLS） estimation procedure is proposed for geolocation using frequency difference of arrival （FDOA） measurements only. We first analyze the measurements of FDOA, and further derive the Cramer-Rao lower bound （CRLB） of geoloeation using FDOA measurements. For the localization model is a nonlinear least squares（LS） estimator with a nonlinear constrained, a linearizing method is used to convert the model to a linear least squares estimator with a nonlinear con- strained. The Gauss-Newton iteration method is developed to conquer the source localization problem. From the analysis of solving Lagrange multiplier, the algorithm is a generalization of linear-correction least squares estimation procedure under the condition of geolocation using FDOA measurements only. The algorithm is compared with common least squares estimation. Comparisons of their estimation accuracy and the CRLB are made, and the proposed method attains the CRLB. Simulation re- sults are included to corroborate the theoretical development.展开更多
基金Supported by the National Natural Science Foundation of China(10671177)
文摘In this paper, the k-partitioning problem with partition matroid constraint is considered. LPT algorithm is modified to fit the problem and its worst-ease performance is analyzed. The lower bounds of optimal solution for the min-max problem are given.
基金supported in part by National Natural Science Foundation of China(Nos.61563009 and 61065010)Doctoral Fund of Ministry of Education of China(No.20125201110003)
文摘This work investigates a simple and practical bio-immune optimization approach to solve a kind of chance-constrained programming problem without known noisy attributes, after probing into a lower bound estimate of sample size for any random variable. Such approach mainly consists of sample allocation, evaluation, proliferation and mutation. The former two, depending on a lower bound estimate acquired, not only decide the sample size of random variable and the importance level of each evolving B cell, but also ensure that such B cell is evaluated with low computational cost; the third makes diverse B cells participate in evolution and suppresses the influence of noise; the last, which associates with the information on population diversity and fitness inheritance, creates diverse and high-affinity B cells. Under such approach, three similar immune algorithms are derived after selecting different mutation rules. The experiments, by comparison against two valuable genetic algorithms, have illustrated that these immune algorithms are competitive optimizers capable of effectively executing noisy compensation and searching for the desired optimal reliable solution.
基金jointly supported by the National Natural Science Foundation of China(No.61175008)State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System of China(No.CEMEE2014K0301A)the Natural Science Foundation of Jiangsu Province of China(No.BK20140896)
文摘Since the features of low energy consumption and limited power supply are very impor- tant for wireless sensor networks （WSNs）, the problems of distributed state estimation with quan- tized innovations are investigated in this paper. In the first place, the assumptions of prior and posterior probability density function （PDF） with quantized innovations in the previous papers are analyzed. After that, an innovative Gaussian mixture estimator is proposed. On this basis, this paper presents a Gaussian mixture state estimation algorithm based on quantized innovations for WSNs. In order to evaluate and compare the performance of this kind of state estimation algo- rithms for WSNs, the posterior Cram6r-Rao lower bound （CRLB） with quantized innovations is put forward. Performance analysis and simulations show that the proposed Gaussian mixture state estimation algorithm is efficient than the others under the same number of quantization levels and the performance of these algorithms can be benchmarked by the theoretical lower bound.
文摘The mobile channel is slow fading and time selective, thus the multiplicative and additive noise of the channel will smear the spectral line, or arouse Doppler spread. This spread will make the parameters estimation accuracy degrade. The goal of this paper is to analytically assess this degradation when Carrier Frequency Offset (CFO) and Doppler shift exist jointly. Then the finite-sample Cramer-Rao Lower Bound (CRLB) is derived and close-form asymptotical expression is given for large-sample CRLB. These expressions give insights into the performance room for frequency estimation. Also the variance of Doppler shift estimator is simulated to illustrate the theoretical results.
文摘This paper delves into the problem of optimal placement conditions for a group of agents collaboratively localizing a target using range-only or bearing-only measurements.The challenge in this study stems from the uncertainty associated with the positions of the agents,which may experience drift or disturbances during the target localization process.Initially,we derive the Cramer-Rao lower bound(CRLB)of the target position as the primary analytical metric.Subsequently,we establish the necessary and sufficient conditions for the optimal placement of agents.Based on these conditions,we analyze the maximal allowable agent position error for an expected mean squared error(MSE),providing valuable guidance for the selection of agent positioning sensors.The analytical findings are further validated through simulation experiments.
基金supported by the National Natural Science Foundation of China(Nos.62071482,62471485,62471348)Shaanxi Association of Science and Technology Youth Talent Support Program Project,China(No.20230137)+1 种基金Innovative Talents Cultivate Program for Technology Innovation Team of ShaanXi Province,China(No.2024RS-CXTD-08)Youth Talent Lifting Project of the China Association for Science and Technology(No.2021-JCJQ-QT-018)。
文摘With the rapid development of commercial communications,the research on Radar-Communication Coexistence(RCC)systems is becoming a hot spot.The resource allocation techniques play a crucial role in the RCC systems.A performance-driven Joint Radar-target and Communication-user Assignment,along with Power and Subchannel Allocation(JRCAPSA)strategy,is proposed for an RCC network.The optimization model aims to minimize the sum of weighted Bayesian Cramer-Rao Lower Bounds(BCRLBs)of target state estimates for radar purpose.This is subject to constraints such as the Communication Data Rate(CDR)for communication purpose,the total power budget in each RCC system,assignment relationships,and the number of available subchannels.Considering that such a problem falls into the realm of Mixed Integer Programming(MIP),a Three-stage Iteratively Augment-based Optimization Method(TIAOM)is developed.The Communication-User Assignment(CUA),Communication Subchannel Allocation(SCA),and Radar-Target Assignment(RTA)feasible solution domains are iteratively expanded based on their importance,leading to the efficient acquisition of a suboptimal solution.Simulation results show the outperformance of the proposed JRCAPSA strategy,compared to the other benchmarks and the OPTI toolbox.The results also imply that the Bayesian Cramer-Rao Lower Bound(BCRLB)is a more stringent optimization metric for the achieved Mean Square Error(MSE),compared to Mutual Information(MI)and Signal-to-Interference-Noise Ratio(SINR).
文摘针对基于到达时间差(Time Difference of Arrival,TDOA)和到达频率差(Frequency Difference of Arrival,FDOA)无源定位中存在的偏差问题,提出一种移动源偏差消除的时频域联合定位算法,即偏差减小的约束加权最小二乘(Bias Reduced-Constrained Weighted Least Squares,BR-CWLS)算法。根据TDOA和FDOA联合测量模型,构建约束加权最小二乘(Constrained Weighted Least Squares,CWLS)问题,再引入二次约束条件,将其转化为具有偏差减小能力的BR-CWLS问题并完成求解。该二次约束条件综合考虑CWLS问题中涉及矩阵的二阶噪声和误差,以有效减小由于测量噪声及加权矩阵的近似估计产生的偏差。仿真结果表明,在噪声功率高达-30 dB时,所提算法的估计精度可以达到克拉美罗下界(Cramér-Rao Lower Bound,CRLB),且位置定位偏差相较于半正定松弛(Semidefinite Relaxation,SDR)算法、CWLS算法及两步加权最小二乘(Two-Step Weighted Least Squares,TSWLS)算法,分别降低了5.3 dB、5.4 dB及14.7 dB。
文摘The muitipath signal resolution is reviewed in this paper.The problemsexisted and to be studied are pointed out.Theoretical analysis of the performance ofthe resolution for deterministic signal in the cases where the signal known or unknownis made.Their corresponding Cramer-Rao lower bounds(CRLB)are obtained.
基金funded by the National Defence Science and Technology Project Fund of China(No.3101140)the Shanghai Aerospace Science and Technology Innovation Fund of China(No.SAST2015028)the Equipment Prophecy Fund of China(No.9140A21040115KG01001).
文摘To improve the resolution and accuracy of Direct Position Determination(DPD),this paper investigates the problem of positioning multiple emitters directly with a single moving Rotating Linear Array(RLA).Firstly,the geometry of the RLA is formulated and analysed.According to its geometry,the intercepted noncoherent signals in multiple interception intervals are modeled.Correspondingly,the Multiple SIgnal Classification(MUSIC)based noncoherent DPD approach is proposed.Secondly,the synchronous coherent pulse signals are individually considered and formulated.And the coherent DPD approach which aims for localizing this special type of signal is presented by stacking all array responses at different interception intervals.Besides,we also derive the constrained Cramer-Rao Lower Bound(CRLB)expression for both noncoherent and coherent DPD with RLA under the constraint that the altitudes of the emitters are known.At last,computer simulations are included to examine the performance of the proposed approach.The results demonstrate that the localization accuracy and resolution of DPD with single moving linear array can be significantly improved by the array rotation.In addition,coherent DPD with RLA further improves the resolution and increases the maximum emitter number that can be localized compared with the noncoherent DPD with RLA.
基金supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA7031015)
文摘This paper investigates the problem of target position estimation with a single-observer passive coherent location（PCL） system. An approach that combines angle with time difference of arrival（ATDOA） is used to estimate the location of a target. Compared with the TDOA-only method which needs two steps, the proposed method estimates the target position more directly. The constrained total least squares（CTLS） technique is applied in this approach. It achieves the Cramer–Rao lower bound（CRLB） when the parameter measurements are subject to small Gaussian-distributed errors. Performance analysis and the CRLB of this approach are also studied. Theory verifies that the ATDOA method gets a lower CRLB than the TDOA-only method with the same TDOA measuring error. It can also be seen that the position of the target affects estimating precision.At the same time, the locations of transmitters affect the precision and its gradient direction.Compared with the TDOA, the ATDOA method can obtain more precise target position estimation.Furthermore, the proposed method accomplishes target position estimation with a single transmitter,while the TDOA-only method needs at least four transmitters to get the target position. Furthermore,the transmitters＇ position errors also affect precision of estimation regularly.
基金supported by the National Natural Science Foundation of China(No.61401513)
文摘The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination （DPD） methods, with combined Time Difference of Arrival （TDOA） and Frequency Difference of Arrival （FDOA）, do not perform well under low Signal-to-Noise Ratio （SNR）, and worse still, the computation cost is difficult to accept when the computational capabilities are limited. To get better positioning performance, we present a new DPD algorithm that proves to be more computationally efficient and more precise for weak signals than the conventional approach. The algorithm partitions the signal received with the same receiver into multiple non-overlapping short-time signal segments, and then uses the TDOA, the FDOA and the coherency among the short-time signals to locate the target. The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load. A secondary but important result is a derivation of closed-form expressions of the Cramer-Rao Lower Bound （CRLB）. The simulation results show that the algorithm proposed in this paper outperforms the traditional DPD algorithms with more accurate results and higher computational efficiency, and especially at low SNR, it is more close to the CRLB.
基金National High-tech Research and Development Program of China (2011AA7072043)National Defense Key Laboratory Foundation of China (9140C860304)Innovation Fund of Graduate School of NUDT (B120406)
文摘A linear-correction least-squares（LCLS） estimation procedure is proposed for geolocation using frequency difference of arrival （FDOA） measurements only. We first analyze the measurements of FDOA, and further derive the Cramer-Rao lower bound （CRLB） of geoloeation using FDOA measurements. For the localization model is a nonlinear least squares（LS） estimator with a nonlinear constrained, a linearizing method is used to convert the model to a linear least squares estimator with a nonlinear con- strained. The Gauss-Newton iteration method is developed to conquer the source localization problem. From the analysis of solving Lagrange multiplier, the algorithm is a generalization of linear-correction least squares estimation procedure under the condition of geolocation using FDOA measurements only. The algorithm is compared with common least squares estimation. Comparisons of their estimation accuracy and the CRLB are made, and the proposed method attains the CRLB. Simulation re- sults are included to corroborate the theoretical development.
