MIMO radar waveform design based on mutual information and minimum mean-square error estimation

By Yang Yang, Rick S. Blum. IEEE Transactions on Aerospace and Electronic Systems, 2007

610 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/17d2eabe0c1ee8dfca563273c8546c7ba0201ccb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. P. Vaidyanathan
2. Hao He
3. Chun-Yang Chen
4. Jian Li
5. P. Stoica
6. Guolong Cui
7. Hongbin Li
8. M. Rangaswamy

In Artificial Intelligence

1. Guolong Cui
2. P. Vaidyanathan
3. M. Rangaswamy
4. Mathini Sellathurai
5. P. Stoica
6. Chun-Yang Chen
7. Jiaqi Zou
8. Hongbin Li

Citations in the corpus

• Integrated Sensing and Communications: Recent Advances and Ten Open Challenges (2023, methodology): “As for parameter estimation, maximizing the MI between the target impulse response and received echo signals usually leads to minimizing the minimum mean-square error (MMSE) [73], [76].”
• MIMO Radar Waveform Design With Constant Modulus and Similarity Constraints (2014, methodology): “, clutter), in [11], the maximization of the detection probability was studied to design the optimum code matrix; the mutual information between the received waveforms and the target radar signatures was employed to design the transmit waveform for extended target [12], [13].”
• Power Minimization-Based Robust OFDM Radar Waveform Design for Radar and Communication Systems in Coexistence (2018, background): “After that, the research in [22] studies the waveform design for multiple-input multiple-output (MIMO) radar by optimizing MI and minimum mean-square error (MMSE), showing that these two criteria yield the same optimum solution.”
• Automotive Radars: A review of signal processing techniques (2017, background): “The solution to this problem consists of water-filling power allocation, distributing more power to target exhibiting significant scattering [39].”
• Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey (2020, methodology): “For example, two criteria, namely, maximization of the conditional MI and minimization of the minimum mean-square error (MMSE), are studied in [122] to optimize the waveform design for MIMO radar by”
• Designing Unimodular Sequence Sets With Good Correlations—Including an Application to MIMO Radar (2009, background): “In [14]–[16], on the other hand, some prior information is assumed known (e.”
• MIMO Radar Waveform Optimization With Prior Information of the Extended Target and Clutter (2009, methodology): “However, in [15]–[20] the effect of the clutter is ignored.”