Your question is a bit unclear. I think you are referring to the data association problem where, because you are not sure if different transmitters or receivers are illuminating or measuring the same object, there is ambiguity. You must always assume that the measurements in data fusion relate to the same target and arrange for this in your data processing. This is usually done with windowing or gating or by using multiple hypotheses to cover the different possibilities. If you try to fuse data from measurements relating to different objects, or not relating to the object, then you have committed a data association error.

Hi!

There are other ways for the process of data fusion in Multi-static radar system: 

Xing Tan, William Roberts, Jian Li, and Petre Stoica. Sparse Learning via Iterative Minimization With Application to MIMO Radar Imaging. IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 59, NO. 3, MARCH 2011, p. 1088-1101.

Sincerely, J. Reyzenkind

In the context of multi-static radar systems, data fusion is a critical process that involves integrating information from multiple sensors to enhance target detection, tracking, and environmental perception. The necessity of using data from the same transmitter for fusion in such systems is not explicitly required but can depend on the specific application and system design. The integration of data from different transmitters can be beneficial in certain scenarios, as highlighted by various research findings:

1. Cooperative Fusion for Enhanced Detection: A study on passive multistatic radar systems highlighted the use of cooperative fusion techniques, including both hard and soft fusion, to improve target detection. These techniques do not necessarily require data from the same transmitter but leverage spatial diversity across multiple receivers. The study proposed novel fusion techniques that showed significant detection performance improvements without the need for knowledge of the transmitted signal or channel information [1]

2. Benefit Analysis of Data Fusion: Another research conducted a benefit analysis of data fusion for target tracking in multiple radar systems (MRS). It suggests that data fusion, whether from the same or different transmitters, can enhance tracking performance depending on factors like signal-to-noise ratio, deployment, and resolution of each radar. This implies that the necessity of fusing data from the same transmitter is not a strict requirement but should be considered based on the potential performance enhancement [2]

3. Linear Fusion Framework: A linear fusion framework for target detection in passive multistatic radar systems was proposed to improve detection performance. This framework involves a weighted combination of local test statistics from spatially separated receivers, suggesting that integrating data from different transmitters can be advantageous [3]

4. Multifrequency GPR Data Fusion: In the context of ground-penetrating radar (GPR), a novel method was developed for the fusion of data from antennas operating at different frequency ranges. This approach, focusing on enhancing subsurface imaging, illustrates the benefit of combining data from different sources, analogous to fusing data from different transmitters in a radar context [4]

5. Multi-Target Tracking in Passive Systems: A study on multi-target tracking in passive multi-static radar systems using Doppler-only measurements discusses the advantages of fusing measurements from spatially distributed sensors. It highlights the fusion of data from multiple bistatic links, again indicating that the fusion process can benefit from integrating information from different transmitters [5]

6. Hybrid Radar Fusion: [6] introduces "hybrid radar fusion" within an integrated sensing and communication scenario. It involves a dual-functional radar and communications base station performing as a mono-static radar for sensing in the downlink, while also handling communication tasks. Communication users act as bi-static radar nodes in the uplink. The study focuses on fusing information from different resource bands to estimate angles-of-arrival for multiple targets, proposing efficient algorithms for this purpose and demonstrating their performance through simulations.

References

[1] Asif, Asma, and Sithamparanathan Kandeepan. "Cooperative fusion based passive multistatic radar detection." Sensors 21.9 (2021): 3209.

[2] J. Yan, H. Liu, W. Pu, B. Jiu, Z. Liu and Z. Bao, "Benefit Analysis of Data Fusion for Target Tracking in Multiple Radar System," in IEEE Sensors Journal, vol. 16, no. 16, pp. 6359-6366, Aug.15, 2016, doi: 10.1109/JSEN.2016.2581824.

keywords: {Radar tracking;Target tracking;Data integration;Signal to noise ratio;Bayes methods;Radar measurements;Multiple radar system;target tracking;Bayesian Cramér-Rao lower bound;benefit analysis},

[3] Zhao, Hong-Yan, et al. "Linear fusion for target detection in passive multistatic radar." signal Processing 130 (2017): 175-182.

[4] A. De Coster and S. Lambot, "Fusion of Multifrequency GPR Data Freed From Antenna Effects," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 2, pp. 664-674, Feb. 2018, doi: 10.1109/JSTARS.2018.2790419.

keywords: {Ground penetrating radar;Radar antennas;Data integration;Antenna measurements;Mathematical model;Antenna effect removal;full-wave inversion;Green's functions;ground-penetrating radar (GPR) multifrequency antenna data fusion},

[5] S. Subedi, Y. D. Zhang, M. G. Amin and B. Himed, "Group Sparsity Based Multi-Target Tracking in Passive Multi-Static Radar Systems Using Doppler-Only Measurements," in IEEE Transactions on Signal Processing, vol. 64, no. 14, pp. 3619-3634, 15 July15, 2016, doi: 10.1109/TSP.2016.2552498.

[6] A. Chowdary, A. Bazzi and M. Chafii, "On Hybrid Radar Fusion for Integrated Sensing and Communication," in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2024.3357573.

keywords: {Radar;Sensors;OFDM;Robot sensing systems;Wireless communication;Fuses;Wireless sensor networks;Integrated Sensing and Communication (ISAC);Dual-Functional Radar and Communications (DFRC);radar fusion;hybrid radar;6G},

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