Distributed geolocation algorithm in mobile ad hoc networks using received signal strength differences
Bibliographic record
Abstract
Future military wireless communication in a battlefield will be mobile ad hoc in nature. The ability to geolocate and track both friendly forces and enemies is very important in military command and control operations. However, current mobile ad hoc networks (MANET) have no capabilities to geolocate radio emitters that belong to enemy mobile ad hoc networks. This paper presents a distributed geolocation algorithm using received signal strength differences to geolocate enemy radio emitters by leveraging friendly force MANET infrastructure, and proposes a communication protocol for radio emitter geolocation applications. An enemy's radio emitter signal is detected, and its signal strength is measured by the nodes in a friendly mobile ad hoc network. The identity of the enemy radio emitter is extracted from the decoded message header of the medium access control layer. By correlating and associating the enemy's radio emitter identity with its received signal strength, the enemy radio emitter is identified. The enemy's radio emitter identity and its received signal strength are distributed and shared among friendly mobile ad hoc nodes. Using received signal strength differences, a master friendly node can calculate the enemy's radio emitter geolocation, and build a recognized MANET picture (RMP). This MANET picture is then distributed to all friendly nodes for effective command and control operations. An advantage of this method is that mobile ad hoc nodes do not need special RF antennas to geolocate the enemy radio emitter as conventional electronic warfare techniques do. MATLAB-based simulations are presented to evaluate the accuracy and reliability of the proposed distributed geolocation algorithm under different MANET placements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".