Pulse repetition interval(PRI)modulation recognition and pulse sequence search are significant for effective electronic support measures.In modern electromagnetic environments,different types of inter-pulse slide rada...Pulse repetition interval(PRI)modulation recognition and pulse sequence search are significant for effective electronic support measures.In modern electromagnetic environments,different types of inter-pulse slide radars are highly confusing.There are few available training samples in practical situations,which leads to a low recognition accuracy and poor search effect of the pulse sequence.In this paper,an approach based on bi-directional long short-term memory(BiLSTM)networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed.The simulation results demonstrate that the proposed algorithm can recognize unilinear,bilinear,sawtooth,and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.展开更多
【目的】以路径重复率为优化目标解决农业机器人在数字生态农场中的全区域覆盖问题。【方法】首先,将栅格地图中的障碍物进行膨胀处理,在此基础上进行矩形分区以及分区合并操作;然后,通过改进的蚁群算法规划分区间的遍历顺序、通过改进...【目的】以路径重复率为优化目标解决农业机器人在数字生态农场中的全区域覆盖问题。【方法】首先,将栅格地图中的障碍物进行膨胀处理,在此基础上进行矩形分区以及分区合并操作;然后,通过改进的蚁群算法规划分区间的遍历顺序、通过改进的广度优先搜索(Breadth first search, BFS)算法规划分区间终点与起点的衔接路径,从而实现机器人全区域覆盖。2种算法的具体改进方案为:分别通过人工免疫算法与粒子群算法改进遗传算法的选择与交叉算子,并将改进后的选择算子、交叉算子、原遗传算法变异算子与蚁群算法相结合改进传统蚁群算法信息素更新方法;建立动态函数以简化BFS算法规划的路径。【结果】仿真结果表明,改进蚁群算法收敛时的迭代次数较传统蚁群算法减少了83.1%,路径长度相比减少了4.8%;由改进的蚁群算法与改进的BFS算法规划的机器人遍历路径重复率是传统蚁群算法和BFS算法的56%,且农业机器人能实现对农田区域的100%覆盖。【结论】本研究提供了一种农业机器人在复杂环境的数字生态循环农场中进行全遍历覆盖的解决方案。展开更多
To estimate the period of a periodic point process from noisy and incomplete observations, the classical periodogram algorithm is modified. The original periodogram algorithm yields an estimate by performing grid sear...To estimate the period of a periodic point process from noisy and incomplete observations, the classical periodogram algorithm is modified. The original periodogram algorithm yields an estimate by performing grid search of the peak of a spectrum, which is equivalent to the periodogram of the periodic point process, thus its performance is found to be sensitive to the chosen grid spacing. This paper derives a novel grid spacing formula, after finding a lower bound of the width of the spectral mainlobe. By employing this formula, the proposed new estimator can determine an appropriate grid spacing adaptively, and is able to yield approximate maximum likelihood estimate （MLE） with a computational complexity of O（n2）. Experimental results prove that the proposed estimator can achieve better trade-off between statistical accuracy and complexity, as compared to existing methods. Simulations also show that the derived grid spacing formula is also applicable to other estimators that operate similarly by grid search.展开更多
基金supported by the National Natural Science Foundation of China(61801143,61971155)the National Natural Science Foundation of Heilongjiang Province(LH2020F019).
文摘Pulse repetition interval(PRI)modulation recognition and pulse sequence search are significant for effective electronic support measures.In modern electromagnetic environments,different types of inter-pulse slide radars are highly confusing.There are few available training samples in practical situations,which leads to a low recognition accuracy and poor search effect of the pulse sequence.In this paper,an approach based on bi-directional long short-term memory(BiLSTM)networks and the temporal correlation algorithm for PRI modulation recognition and sequence search under the small sample prerequisite is proposed.The simulation results demonstrate that the proposed algorithm can recognize unilinear,bilinear,sawtooth,and sinusoidal PRI modulation types with 91.43% accuracy and complete the pulse sequence search with 30% missing pulses and 50% spurious pulses under the small sample prerequisite.
文摘【目的】以路径重复率为优化目标解决农业机器人在数字生态农场中的全区域覆盖问题。【方法】首先,将栅格地图中的障碍物进行膨胀处理,在此基础上进行矩形分区以及分区合并操作;然后,通过改进的蚁群算法规划分区间的遍历顺序、通过改进的广度优先搜索(Breadth first search, BFS)算法规划分区间终点与起点的衔接路径,从而实现机器人全区域覆盖。2种算法的具体改进方案为:分别通过人工免疫算法与粒子群算法改进遗传算法的选择与交叉算子,并将改进后的选择算子、交叉算子、原遗传算法变异算子与蚁群算法相结合改进传统蚁群算法信息素更新方法;建立动态函数以简化BFS算法规划的路径。【结果】仿真结果表明,改进蚁群算法收敛时的迭代次数较传统蚁群算法减少了83.1%,路径长度相比减少了4.8%;由改进的蚁群算法与改进的BFS算法规划的机器人遍历路径重复率是传统蚁群算法和BFS算法的56%,且农业机器人能实现对农田区域的100%覆盖。【结论】本研究提供了一种农业机器人在复杂环境的数字生态循环农场中进行全遍历覆盖的解决方案。
基金supported by the National Natural Science Foundation of China (No. 61002026)
文摘To estimate the period of a periodic point process from noisy and incomplete observations, the classical periodogram algorithm is modified. The original periodogram algorithm yields an estimate by performing grid search of the peak of a spectrum, which is equivalent to the periodogram of the periodic point process, thus its performance is found to be sensitive to the chosen grid spacing. This paper derives a novel grid spacing formula, after finding a lower bound of the width of the spectral mainlobe. By employing this formula, the proposed new estimator can determine an appropriate grid spacing adaptively, and is able to yield approximate maximum likelihood estimate （MLE） with a computational complexity of O（n2）. Experimental results prove that the proposed estimator can achieve better trade-off between statistical accuracy and complexity, as compared to existing methods. Simulations also show that the derived grid spacing formula is also applicable to other estimators that operate similarly by grid search.
