Kalman Filter-based Doppler Tracking and Channel Estimation for AUV Implementation
Bibliographic record
Abstract
Proposed, are computationally light weight Kalman filter (KF) implementations of Doppler tracking and channel estimation for underwater acoustic (UWA) channels. This targets autonomous underwater vehicles (AUV) communicating with stationary or mobile sensors. The Doppler tracker reduces the computational load, as a precursor to Doppler compensation, and filters the Mach number estimates at the output of the Doppler estimator. The channel estimator observes the multipath effects that affected the signal during transmission as well as generate the taps required for channel equalization. The UWA channel is assumed to be doubly spread, i.e. it is both time-varying and frequency-selective. The paper assumes that the receiver is equipped with a Doppler estimator, which yields a Mach number estimate at every sample, and a Doppler compensator, which eliminates the Doppler effect from the received signal (although residual Doppler effects can still persist at the output). For Doppler tracking, the Kalman filter (KF) operates as an extended Kalman filter (EKF) that smooths, in a low pass and statistical sense, the Mach number estimates. The Mach number is assumed to possess high and low sinusoidal components. The Mach number tracks the low-frequency components and is modelled as a sinusoid with random amplitude, phase and frequency. This significantly reduces the computational complexity of Doppler compensators, who rely on resampling, since the rapid variations occurring over relatively small time intervals are ignored. For channel estimation, since the time variations are significantly suppressed by the compensator, the linear KF is used to estimate and track the channel impulse response (CIR). Noise accumulation introduced by the filters is mitigated with a Rauch-Tung-Striebel (RTS) smoother. The proposed filters are verified by simulations in synthetic UWA channels with the mean square error (MSE) as the performance metric. The proposed method is comparable or outperforms other methods in terms of MSE. It is also capable of tracking more severe motion scenarios and is computationally more efficient for AUV applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".