Historical overview and current research on noise radar
Bibliographic record
Abstract
The concept of noise radar with coherent reception of radar returns starts its history as early as in 1950s. The first papers on range-measuring radar based upon noise signals were published by Richard Bourret in 1957 who applied CW noise waveform along with its correlation reception and by B.M. Horton in 1959 who also used CW noise signal, but being used for frequency modulation along with so called anticorrelation method for signal processing. Horton recognized that one way to eliminate range and Doppler ambiguities was to use random noise as the modulating function and perform range determination by cross-correlating the return signal with a time-delayed replica of the transmit waveform. In his seminal paper in the May 1959 issue of the Proceedings of the IRE entitled “Noise-modulated distance measuring systems” on pages 821–828, he derived the fundamental concepts and proposed several implementations. Further papers on that subject were published in the period of 1960s and 1970s which may be characterized as a period of initial studies and performance analyses by a handful of researchers. Relatively little development took place in 1980s, though in that new methods for efficient generation of chaotic signals in millimeter wave electron devices have been suggested and autodyne effect in chaotic oscillators has been revealed in 1987 in Ukraine. Advance system development and demonstration by several groups all over the world were done in 1990s and 2000s: Several research groups around the world have developed new applications for noise radar and made significant contributions towards detection, surveillance, tracking, and imaging of targets. Noise radars have the unique property that allows them to achieve high resolution in both range and Doppler which can be independently controlled by varying the bandwidth and integration time respectively. Noise radars satisfy important requirements for military systems, such as low probability of intercept (LPI) and low probability of detection (LPD), owing to the featureless characteristics of its waveform. They also have excellent resistance to jamming and interference. Another advantage of noise radars is their ability to efficiently share the frequency spectrum, thereby allowing a number of noise radars to operate over the same frequency band with minimal cross-interference. This spectrally parsimonious feature can be used to integrate several noise radars to a network centric platform. Much of the early development was hampered by the lack of suitable critical components for fully operational use. However, in recent years, noise radar systems are finding increasing applications in several conventional as well as new areas (e.g., through-wall imaging, multi-static sensing, radar networking) owing to significant advances in RF, digital, and signal processing technologies. Figure 1. shows coarse grain classification of signal processing methods applicable and being used in Noise Radar developed by different research groups. The most popular of them is correlation reception of noise radar returns which has been applied in the very first experiments in Noise Radar. At the same time, so called spectral interferometry method, or its simplified option, double spectral processing, and autodyne (self-mixing) effect may be also applied in case of short range operation. Double spectral processing was pioneered by L Arkhipov in USSR (1961) and independently by J.L. Poirier in US (1968). Nowadays, intensive research in noise radar is ongoing in the U.S., Ukraine, U.K., France, Canada, Germany, Italy, Russia, Poland, Sweden, Norway, China, South Korea and India. Researchers are making many attempts to investigate and experimentally approve capabilities of Noise Radar Technology via design of major, if not all, types of radars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".