Radar target probing and measurement are challenging tasks for Radio Frequency Simulation（RFS） with pulse radar signal. Due to the long-time duration of pulse radar signal and the limited space of anechoic chamber, ...Radar target probing and measurement are challenging tasks for Radio Frequency Simulation（RFS） with pulse radar signal. Due to the long-time duration of pulse radar signal and the limited space of anechoic chamber, the reflected signal returns before pulse radar signal is fully transmitted in RFS. As a consequence, the transmitted and reflected signals are coupled at the receiver. To handle this problem, the Interrupted Transmitting and Receiving（ITR） experiment system is constructed in this paper by dividing the pulse radar signal into sub-pulses. The target echo can be obtained by transmitting and receiving the sub-pulses intermittently. Furthermore, the principles of ITR are discussed and the target probing experiments are performed with the ITR system. It is demonstrated that the ITR system can overcome the coupling between the reflected and transmitted signals. Based on the target probing results, the performance of pulse radar target probing and measurement can be verified in RFS with the ITR system.展开更多
The probability hypothesis density （PHD） propagates the posterior intensity in place of the poste- rior probability density of the multi-target state. The cardinalized PHD （CPHD） recursion is a generalization of P...The probability hypothesis density （PHD） propagates the posterior intensity in place of the poste- rior probability density of the multi-target state. The cardinalized PHD （CPHD） recursion is a generalization of PHD recursion, which jointly propagates the posterior intensity function and posterior cardinality distribution. A number of sequential Monte Carlo （SMC） implementations of PHD and CPHD filters （also known as SMC- PHD and SMC-CPHD filters, respectively） for general non-linear non-Gaussian models have been proposed. However, these approaches encounter the limitations when the observation variable is analytically unknown or the observation noise is null or too small. In this paper, we propose a convolution kernel approach in the SMC-CPHD filter. The simuIation results show the performance of the proposed filter on several simulated case studies when compared to the SMC-CPHD filter.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.61101180,61401491 and 61490692)
文摘Radar target probing and measurement are challenging tasks for Radio Frequency Simulation（RFS） with pulse radar signal. Due to the long-time duration of pulse radar signal and the limited space of anechoic chamber, the reflected signal returns before pulse radar signal is fully transmitted in RFS. As a consequence, the transmitted and reflected signals are coupled at the receiver. To handle this problem, the Interrupted Transmitting and Receiving（ITR） experiment system is constructed in this paper by dividing the pulse radar signal into sub-pulses. The target echo can be obtained by transmitting and receiving the sub-pulses intermittently. Furthermore, the principles of ITR are discussed and the target probing experiments are performed with the ITR system. It is demonstrated that the ITR system can overcome the coupling between the reflected and transmitted signals. Based on the target probing results, the performance of pulse radar target probing and measurement can be verified in RFS with the ITR system.
基金Supported in Part by the Foundation of the Excellent State Key Laboratory under Grant 40523005,and the Ministry of Education of China
文摘The probability hypothesis density （PHD） propagates the posterior intensity in place of the poste- rior probability density of the multi-target state. The cardinalized PHD （CPHD） recursion is a generalization of PHD recursion, which jointly propagates the posterior intensity function and posterior cardinality distribution. A number of sequential Monte Carlo （SMC） implementations of PHD and CPHD filters （also known as SMC- PHD and SMC-CPHD filters, respectively） for general non-linear non-Gaussian models have been proposed. However, these approaches encounter the limitations when the observation variable is analytically unknown or the observation noise is null or too small. In this paper, we propose a convolution kernel approach in the SMC-CPHD filter. The simuIation results show the performance of the proposed filter on several simulated case studies when compared to the SMC-CPHD filter.
