Among the currently available chirp scaling algorithms for bi-static SAR, some compromise with approximation in the range model, while some others use the equivalent method by first changing bi-static SAR into mono-st...Among the currently available chirp scaling algorithms for bi-static SAR, some compromise with approximation in the range model, while some others use the equivalent method by first changing bi-static SAR into mono-static SAR then apply chirp scaling algorithm of mono-static SAR. Consequently, as the squint angles get large, the performance of focusing will deteriorate significantly. This paper, however, abandons those traditional solutions to the bi-static imaging problems and introduces a novel method, based on the space model of bi-static platforms. First, a precise range model is established. Then, a new chirp scaling algorithm for bi-static SAR using the precise range model is advanced. It is theoretically proven that this is an analytic solution of the bi-static chirp scaling algorithm. Images can be focused accurately even with large squint angles. At last simulations with large squint angles are made to verify the validity of the algorithm.展开更多
Integrated Sensing and Communication(ISAC)is envisioned as a promising technology for Sixth-Generation(6G)wireless communications,which enables simultaneous high-rate communication and high-precision target localizati...Integrated Sensing and Communication(ISAC)is envisioned as a promising technology for Sixth-Generation(6G)wireless communications,which enables simultaneous high-rate communication and high-precision target localization.Compared to independent sensing and communication modules,dual-function ISAC could leverage the strengths of both communication and sensing in order to achieve cooperative gains.When considering the communication core network,ISAC system facilitates multiple communication devices to collaborate for networked sensing.This paper investigates such kind of cooperative ISAC systems with distributed transmitters and receivers to support non-connected and multi-target localization.Specifically,we introduce a Time of Arrival(TOA)based multi-target localization scheme,which leverages the bi-static range measurements between the transmitter,target,and receiver channels in order to achieve elliptical localization.To obtain the low-complexity localization,a two-stage search-refine localization methodology is proposed.In the first stage,we propose a Successive Greedy Grid-Search(SGGS)algorithm and a Successive-Cancellation-List Grid-Search(SCLGS)algorithm to address the Measurement-to-Target Association(MTA)problem with relatively low computational complexity.In the second stage,a linear approximation refinement algorithm is derived to facilitate high-precision localization.Simulation results are presented to validate the effectiveness and superiority of our proposed multi-target localization method.展开更多
在高斯白噪声环境下,针对双基地多输入多输出雷达点目标相对发射和接收阵列方位角DODDOA(Direction Of Departure-Direction Of Arrival)联合估计问题,提出了一种新方法.首先将点目标所在空间构建为一个关于到达角的二维密集字典,将各...在高斯白噪声环境下,针对双基地多输入多输出雷达点目标相对发射和接收阵列方位角DODDOA(Direction Of Departure-Direction Of Arrival)联合估计问题,提出了一种新方法.首先将点目标所在空间构建为一个关于到达角的二维密集字典,将各个点目标在该密集字典进行投影得到各个点目标在该字典下的稀疏表示.在稀疏性构建的前提下,采用充分挖掘信号稀疏性的加权l1范数最小化约束模型对点目标的角度信息进行求解.为了使该算法在低信噪比情况下能够更稳健地重构各点目标的二位方位角,对其权重进行了改进以达到抑制噪声的效果.展开更多
基金Supported by the National Basic Research Program of China
文摘Among the currently available chirp scaling algorithms for bi-static SAR, some compromise with approximation in the range model, while some others use the equivalent method by first changing bi-static SAR into mono-static SAR then apply chirp scaling algorithm of mono-static SAR. Consequently, as the squint angles get large, the performance of focusing will deteriorate significantly. This paper, however, abandons those traditional solutions to the bi-static imaging problems and introduces a novel method, based on the space model of bi-static platforms. First, a precise range model is established. Then, a new chirp scaling algorithm for bi-static SAR using the precise range model is advanced. It is theoretically proven that this is an analytic solution of the bi-static chirp scaling algorithm. Images can be focused accurately even with large squint angles. At last simulations with large squint angles are made to verify the validity of the algorithm.
文摘Integrated Sensing and Communication(ISAC)is envisioned as a promising technology for Sixth-Generation(6G)wireless communications,which enables simultaneous high-rate communication and high-precision target localization.Compared to independent sensing and communication modules,dual-function ISAC could leverage the strengths of both communication and sensing in order to achieve cooperative gains.When considering the communication core network,ISAC system facilitates multiple communication devices to collaborate for networked sensing.This paper investigates such kind of cooperative ISAC systems with distributed transmitters and receivers to support non-connected and multi-target localization.Specifically,we introduce a Time of Arrival(TOA)based multi-target localization scheme,which leverages the bi-static range measurements between the transmitter,target,and receiver channels in order to achieve elliptical localization.To obtain the low-complexity localization,a two-stage search-refine localization methodology is proposed.In the first stage,we propose a Successive Greedy Grid-Search(SGGS)algorithm and a Successive-Cancellation-List Grid-Search(SCLGS)algorithm to address the Measurement-to-Target Association(MTA)problem with relatively low computational complexity.In the second stage,a linear approximation refinement algorithm is derived to facilitate high-precision localization.Simulation results are presented to validate the effectiveness and superiority of our proposed multi-target localization method.
基金Supported by the National Natural Science Foundation of China(No.61172169)the National Natural Science Foundation for Young Scientist of China(No.61201369,61102109)
文摘在高斯白噪声环境下,针对双基地多输入多输出雷达点目标相对发射和接收阵列方位角DODDOA(Direction Of Departure-Direction Of Arrival)联合估计问题,提出了一种新方法.首先将点目标所在空间构建为一个关于到达角的二维密集字典,将各个点目标在该密集字典进行投影得到各个点目标在该字典下的稀疏表示.在稀疏性构建的前提下,采用充分挖掘信号稀疏性的加权l1范数最小化约束模型对点目标的角度信息进行求解.为了使该算法在低信噪比情况下能够更稳健地重构各点目标的二位方位角,对其权重进行了改进以达到抑制噪声的效果.
