在瑞利衰落信道下,分析了跳频通信中噪声跟踪干扰的检测性能.应用认知无线电中协作频谱感知的思想,研究了AWGN(Additive White Gaussian Noise)信道下单跳信号存在干扰的检测性能,在此基础上,推导得到了瑞利衰落信道下的检测概率和虚警...在瑞利衰落信道下,分析了跳频通信中噪声跟踪干扰的检测性能.应用认知无线电中协作频谱感知的思想,研究了AWGN(Additive White Gaussian Noise)信道下单跳信号存在干扰的检测性能,在此基础上,推导得到了瑞利衰落信道下的检测概率和虚警概率,应用硬判决中的"k out of n"准则,得到了瑞利衰落信道下噪声跟踪干扰的检测概率.仿真结果验证了理论分析的正确性.展开更多
基于认知无线电中协作频谱感知的思想,提出了一种跳频通信中噪声跟踪干扰的检测算法。首先采用能量检测算法,研究了干扰条件下单跳信号的检测性能。在此基础上,应用"k out of n"准则,在决策融合模块实现对噪声跟踪干扰的检测...基于认知无线电中协作频谱感知的思想,提出了一种跳频通信中噪声跟踪干扰的检测算法。首先采用能量检测算法,研究了干扰条件下单跳信号的检测性能。在此基础上,应用"k out of n"准则,在决策融合模块实现对噪声跟踪干扰的检测,推导了AWGN信道下噪声跟踪干扰的检测性能。理论分析和仿真结果表明,随着信干噪比的逐渐减小,对于单跳信号和噪声跟踪干扰的检测概率均逐渐增大;而在相同信干噪比情况下,噪声跟踪干扰的检测概率较单跳信号的检测概率得到了提高。展开更多
针对慢跳频通信中噪声跟踪干扰的检测问题,提出了一种新的噪声跟踪干扰检测算法.该检测算法应用认知无线电中协作频谱感知方法,分析了接收信号的条件概率密度函数,研究了加性高斯白噪声信道下单跳信号存在干扰的检测性能.在此基础上,推...针对慢跳频通信中噪声跟踪干扰的检测问题,提出了一种新的噪声跟踪干扰检测算法.该检测算法应用认知无线电中协作频谱感知方法,分析了接收信号的条件概率密度函数,研究了加性高斯白噪声信道下单跳信号存在干扰的检测性能.在此基础上,推导了Nakagami衰落信道下单跳信号存在干扰的检测概率和虚警概率,通过对检测概率和虚警概率中的多重积分进行化简,得到了检测概率和虚警概率的级数表达式.单跳信号检测后,把检测结果上报到融合中心,应用协作频谱感知中的"k out of n"准则分析了噪声跟踪干扰的检测性能.仿真结果验证了理论分析的正确性.展开更多
To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance....To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.展开更多
Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.C...Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors...Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear measurements and partially unknown input, which is generated by an exogenous system. In the worst case where the input is completely unknown, the exogenous dynamics is reduced to the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.展开更多
文摘在瑞利衰落信道下,分析了跳频通信中噪声跟踪干扰的检测性能.应用认知无线电中协作频谱感知的思想,研究了AWGN(Additive White Gaussian Noise)信道下单跳信号存在干扰的检测性能,在此基础上,推导得到了瑞利衰落信道下的检测概率和虚警概率,应用硬判决中的"k out of n"准则,得到了瑞利衰落信道下噪声跟踪干扰的检测概率.仿真结果验证了理论分析的正确性.
文摘基于认知无线电中协作频谱感知的思想,提出了一种跳频通信中噪声跟踪干扰的检测算法。首先采用能量检测算法,研究了干扰条件下单跳信号的检测性能。在此基础上,应用"k out of n"准则,在决策融合模块实现对噪声跟踪干扰的检测,推导了AWGN信道下噪声跟踪干扰的检测性能。理论分析和仿真结果表明,随着信干噪比的逐渐减小,对于单跳信号和噪声跟踪干扰的检测概率均逐渐增大;而在相同信干噪比情况下,噪声跟踪干扰的检测概率较单跳信号的检测概率得到了提高。
文摘针对慢跳频通信中噪声跟踪干扰的检测问题,提出了一种新的噪声跟踪干扰检测算法.该检测算法应用认知无线电中协作频谱感知方法,分析了接收信号的条件概率密度函数,研究了加性高斯白噪声信道下单跳信号存在干扰的检测性能.在此基础上,推导了Nakagami衰落信道下单跳信号存在干扰的检测概率和虚警概率,通过对检测概率和虚警概率中的多重积分进行化简,得到了检测概率和虚警概率的级数表达式.单跳信号检测后,把检测结果上报到融合中心,应用协作频谱感知中的"k out of n"准则分析了噪声跟踪干扰的检测性能.仿真结果验证了理论分析的正确性.
文摘To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.
基金Supported by the National Natural Science Foundation of China(No.61300214)the National Natural Science Foundation of Henan Province(No.132300410148)+1 种基金the Post-doctoral Science Foundation of China(No.2014M551999)the Funding Scheme of Young Key Teacher ofHenan Province Universities(No.2013GGJS-026)
文摘Aiming at the effective realization of particle filter for maneuvering target tracking in multi-sensor measurements,a novel multi-sensor multiple model particle filtering algorithm with correlated noises is proposed.Combined with the kinetic evolution equation of target state,a multi-sensor multiple model particle filter is firstly constructed,which is also used as the basic framework of a new algorithm.In the new algorithm,in order to weaken the adverse influence from random measurement noises in the measuring process of particle weight,a weight optimization strategy is introduced to improve the reliability and stability of particle weight.In addition,considering the correlated noise existing in the practical engineering,a decoupling method of correlated noise is given by the rearrangement and transformation of the state transition equation and measurement equation.Since the weight optimization strategy and noise decoupling method adopt respectively the center fusion structure and the off-line way,it improves the adverse effect effectively on computational complexity for increasing state dimension and sensor number.Finally,the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
文摘Target tracking using distributed sensor network is in general a challenging problem because it always needs to deal with real-time processing of noisy information. In this paper the problem of using nonlinear sensors such as distance and direction sensors for estimating a moving target is studied. The problem is formulated as a prudent design of nonlinear filters for a linear system subject to noisy nonlinear measurements and partially unknown input, which is generated by an exogenous system. In the worst case where the input is completely unknown, the exogenous dynamics is reduced to the random walk model. It can be shown that the nonlinear filter will have optimal convergence if the number of the sensors are large enough and the convergence rate will be highly improved if the sensors are deployed appropriately. This actually raises an interesting issue on active sensing： how to optimally move the sensors if they are considered as mobile multi-agent systems？ Finally, a simulation example is given to illustrate and validate the construction of our filter.
