Real-Time Multiple Input Multiple Output (MIMO) Radar Using Software Defined Radio
Bibliographic record
Abstract
In this paper, commercially-available software-defined radios (SDRs) are used to build a 64-channel, reconfigurable Active Electronically Scanned Arrays (AESA) radar operating in C-band (NATO G-band). The SDRs are used to design and implement a 3-dimensional multi-input and multi-output (MIMO) radar. The flexibility of the SDRs has been harnessed to evaluate the performance of a linear frequency modulated continuous wave (LFMCW) MIMO radar using three different methods of achieving the orthogonality, namely Time Division Multiplexing (TDM), Frequency Division Multiplexing (FDM), and Code Division Multiplexing (CDM). In addition, the radar’s parameters are user-selectable and can be rapidly changed such that the radar can be used in different environments without requiring changes to the hardware. Measurements indicate that the radar is capable of detecting and localizing multiple targets in all 3-dimensions, including bearing, range, and Doppler. The MIMO radar operates in real-time, with a refresh rate of only 3 seconds. Experimental results are generated for the TDM mode of operation with further research reporting on the CDM and FDM modes of operation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".