Deterministic DTFT Interpolation for Joint Frequency and Chirp-Rate Estimation: Cell-Uniform Efficiency and Threshold Analysis

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Joint estimation of the frequency and chirp rate of a noisy chirp signal arises in radar, sonar, and burst satellite communications. Conventional estimators combine a coarse grid search with fine interpolation; accuracy degrades at the edges of the residual cell (the edge effect) and below the breakdown SNR (the threshold effect). This paper presents a deterministic two-stage estimator that controls both failure modes uniformly over the entire residual cell. The estimator combines a time-centered, zero-padded dechirp-FFT acquisition bank with alternating selectable-$p$ amplitude-interpolation refinements on fractional-bin DTFT samples; in the centered frame, the frequency-chirp-rate cross-term of the Fisher information vanishes. The paper derives a mean-squared-error and threshold characterization across the full SNR range, in closed form except for one calibrated scalar (an effective cell count), to our knowledge the first for the joint problem: the breakdown threshold is governed by the cell count, and its cell-position dependence is dominated by the straddle loss of the coarse FFT, which the padding bounds at 0.4 dB. An asymptotic uniformity analysis over the whole cell, including the corners, gives closed-form fixed-point variance ratios of $1.003$ and $0.998$, analytically free of the residual. A closed-form bias analysis under unmodeled jerk shows the centered chirp-rate estimate is first-order immune. Monte Carlo experiments at $N=256$ (validated at $N=32$-$512$) measure frequency- and chirp-rate-axis efficiencies with median $1.03$ and worst case $1.07$ over $144$ cell positions at $-5$ dB. Threshold predictions hold within $1.0$ dB on four held-out configurations. The dechirp-FFT bank is fully parallel, and each of the four refinement iterations evaluates three DTFT samples per axis; under fixed operating conditions, per-estimate latency is constant at $O(N\log N)$ cost.

Links and sources

• Official / arXiv page
• PDF from original source

Comments