A long-time coherent integration could effectively improve the detection ability of radar for maneuvering targets. Nevertheless, the Doppler ambiguity and frequency migration caused by the high speed and acceleration severely degrade the detection performance. In this regard, a novel coherent integration algorithm is proposed, particularly for maneuvering targets with Doppler ambiguity. Specifically, the acceleration is firstly estimated by scaled non-uniform fast Fourier transform (SNuFFT). Then, the scaled periodic discrete Fourier transform (SPDFT), which periodically extends the observable Doppler scope of discrete Fourier transform (DFT), is proposed to estimate the unambiguous Doppler frequency. Finally, the grating lobes are significantly suppressed via product operation, and coherent integration is achieved after phase compensation. To alleviate the computational burden and eliminate the brute force searching procedure, an efficient implementation based on chirp-z transform (CZT) is also derived. Analysis shows that the proposed algorithm achieves a good balance between computational complexity and anti-noise performance. Extensive simulations and real measured radar data are conducted to verify the proposed algorithm.

In recent years, target detection has drawn increasing attention in the field of radar signal processing. In this paper, we address the problem of coherent integration for detecting high-speed maneuvering targets, involving range migration (RM), quadratic RM (QRM), and Doppler frequency migration (DFM) within the coherent processing interval. We propose a novel coherent integration algorithm based on the frequency-domain second-order phase difference (FD-SoPD) approach. First, we use the FD-SoPD operation to reduce the signal from three to two dimensions and simultaneously eliminate the effects of QRM and DFM, which leads to signal-to-noise ratio improvement in the velocity-acceleration domain. Next, we estimate the target motion parameters from the peak position without the need for a search procedure. We show that this algorithm can be easily implemented by using complex multiplications combined with fast Fourier transform (FFT) and inverse FFT (IFFT) operations. We perform comparisons with several representative algorithms and show that the proposed technique can be used to achieve a good trade-off between computational complexity and detection performance. We present both simulated and experimental data to demonstrate the effectiveness of the proposed method.

In this study, a novel algorithm based on three‐dimensional scaled Fourier transform (TDSFT) is proposed for parameter estimation of a quadratic frequency modulated (QFM) signal. To solve the problem of signal‐to‐noise ratio loss caused by constant delay, a variable‐delay instantaneous autocorrelation function is first proposed. After that, two steps TDSFT and addition operation are performed to achieve three‐dimensional decoupling and coherent integration of signal energy. The analysis shows that the proposed algorithm can be efficiently implemented by a fast Fourier transform without any searching procedure. Moreover, it can deal with multi‐component QFM signals with satisfactory suppression of cross terms. Comparisons with three other popular methods, chirp rate‐quadratic chirp rate distribution, generalized scaled Fourier transform and generalized decoupling technique demonstrate that the proposed algorithm has superior anti‐noise performance due to the coherent integration of all signal energies. Extensive numerical simulations validate the effectiveness of the TDSFT‐based algorithm.