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Cramer-rao bounds and coherence performance analysis for next generation radar with pulse trains
Xiaowei Tang1, Jun Tang, Qian He
1Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. sxw02@mails.tsinghua.edu.cn
Sensors (Basel, Switzerland)
|April 25, 2013
Summary
Next generation radar (NGR) using pulse trains significantly improves parameter estimation and coherence performance. This study derives Cramer-Rao bounds for generalized coherence parameters in MIMO mode and SNR gain in coherent mode.
Area of Science:
- Electrical Engineering
- Signal Processing
- Radar Systems
Background:
- Next-generation radar (NGR) systems require enhanced parameter estimation and coherence performance.
- Current NGR architectures often utilize single-pulse processing, limiting performance.
- Extending signal models to pulse trains is crucial for advancing NGR capabilities.
Purpose of the Study:
- To investigate the Cramer-Rao bounds for parameter estimation and coherence performance in NGR.
- To extend the NGR signal model from single pulse to pulse trains for improved performance.
- To derive performance bounds for both MIMO and coherent modes of NGR.
Main Methods:
- Extended the NGR signal model to incorporate pulse trains with spatial and temporal integration.
- Derived closed-form Cramer-Rao bounds (CRB) for generalized coherence parameters (GCPs) in MIMO mode.
- Developed a signal-to-noise ratio (SNR) gain performance bound for coherent mode, considering estimation errors.
Main Results:
- Pulse train utilization in NGR leads to significantly improved estimation accuracy.
- Enhanced coherence performance is achieved through the use of pulse trains.
- Derived CRBs provide theoretical limits for parameter estimation in MIMO mode.
- The SNR gain bound in coherent mode accounts for estimation errors from GCPs.
Conclusions:
- Employing pulse trains in NGR substantially enhances parameter estimation and coherence performance.
- The derived Cramer-Rao bounds and SNR gain performance bounds are validated by numerical examples.
- This work provides a theoretical foundation for optimizing NGR systems using pulse train techniques.
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