Ningbo Liu

Maneuvering Target Detection via Radon-Fractional Fourier Transform-Based Long-Time Coherent Integration

Long-time coherent integration technique is one of the most important methods for the improvement of radar detection ability of a weak maneuvering target, whereas the integration performance may be greatly influenced by the across range unit (ARU) and Doppler frequency migration (DFM) effects. In this paper, a novel representation known as Radon-fractional Fourier transform (RFRFT) is proposed and investigated to solve the above problems simultaneously. It can not only eliminate the effect of DFM by selecting a proper rotation angle but also achieve long-time coherent integration without ARU effect. The RFRFT can be regarded as a special Doppler filter bank composed of filters with different rotation angles, which indicates a generalization of the traditional moving target detection (MTD) and FRFT methods. Some useful properties and the likelihood ratio test detector of RFRFT are derived for maneuvering target detection. Finally, numerical experiments of aerial target and marine target detection are carried out using simulated and real radar datasets. The results demonstrate that for integration gain and detection ability, the proposed method is superior to MTD, FRFT, and Radon-Fourier transform under low signal-to-clutter/noise ratio (SCR/SNR) environments. Moreover, the trajectory of target can be easily obtained via RFRFT as well.

Detection of a Low Observable Sea-Surface Target With Micromotion via the Radon-Linear Canonical Transform

In this letter, a novel long-time coherent integration method, known as the Radon-linear canonical transform (RLCT), is proposed for detection of a low observable moving target in sea clutter. The micro-Doppler (m-D) of a sea-surface target is studied and modeled as multiple linear-frequency-modulated signals, which result from the accelerated and 3-D rotated movements. The RLCT-based algorithm employs m-D as a useful signature for target detection and can simultaneously compensate the range and Doppler migrations during long observation time, which simplifies the operational procedure. By searching along the moving trajectory and using extra three degrees of freedom, the observation values of m-D signals can be well matched and accumulated as peaks in the RLCT domain. Then, the target can be declared by comparing the peak value with an adaptive threshold. The definition of the RLCT demonstrates that it is the generalization of the popular moving target detection, Radon-Fourier transform, fractional Fourier transform, and linear canonical transform methods. Finally, experiments using a real sea clutter data set show that the proposed method can achieve high integration gain and detection probability of a micromotion target in heavy sea clutter.

Radon-Linear Canonical Ambiguity Function-Based Detection and Estimation Method for Marine Target With Micromotion

Robust and effective detection of a marine target is a challenging task due to the complex sea environment and targets motion. A long-time coherent integration technique is one of the most useful methods for the improvement of radar detection ability, whereas it would easily run into the across range unit (ARU) and Doppler frequency migration (DFM) effects resulting distributed energy in the time and frequency domain. In this paper, the micro-Doppler (m-D) signature of a marine target is employed for detection and modeled as a quadratic frequency-modulated signal. Furthermore, a novel long-time coherent integration method, i.e., Radon-linear canonical ambiguity function (RLCAF), is proposed to detect and estimate the m-D signal without the ARU and DFM effects. The observation values of a micromotion target are first extracted by searching along the moving trajectory. Then these values are carried out with the long-time instantaneous autocorrelation function for reduction of the signal order, and well matched and accumulated in the RLCAF domain using extra three degrees of freedom. It can be verified that the proposed RLCAF can be regarded as a generalization of the popular ambiguity function, fractional Fourier transform, fractional ambiguity function, and Radon-linear canonical transform. Experiments with simulated and real radar data sets indicate that the RLCAF can achieve higher integration gain and detection probability of a marine target in a low signal-to-clutter ratio environment.

Radon-fractional ambiguity function-based detection method of low-observable maneuvering target

Based on the quadratic frequency modulated (FM) signal of a maneuvering target, a novel long-time coherent integration method, i.e., Radon-fractional ambiguity function (RFRAF), is proposed to compensate the range and Doppler migrations simultaneously. By the long-time instantaneous autocorrelation function and rotating the time-frequency plane, the observation values of a maneuvering target can be well matched and accumulated as a peak in the RFRAF domain. Results of experiments with simulated and real data prove its effectiveness.

Fractal-based variable step-size least mean square algorithm for radar target detection in sea clutter

This paper introduces fractal-based variable step-size least mean square(FB-VSLMS) algorithm and proposes a model for radar target detection in sea clutter. FB-VSLMS algorithm deals with a specific class of fractal signals and except one of the step-size parameters requiring time-varying constraints, the constraints on the remaining parameters are time-invariant. And the step-size matrix is determined completely with the knowledge of the deterministic Hurst exponent. The model based on this algorithm is suited for tracking signals from the family of fractal signals that are inherently nonstationary. In the end, the performance of the novel model is analyzed. By the verification of X-band real sea clutter, the model is shown to be effective for point target detection in sea clutter.

Time-frequency entropy of Hilbert-Huang transformation for detecting weak target in sea clutter

In this paper, Hilbert-Huang transformation is adopted for analyzing the sea clutter with the fixed weak target. Its found that the fixed target only affects the low frequency component of the sea clutter. So the time-frequency entropy of the low frequency component is applied for the weak target detection. Compared with another weak target detection method directly using the box dimension, the method proposed in this paper improves the effect of the fixed target on the sea clutter. And the detection performance of the fixed weak target is improved distinctly.

Sea surface micromotion target detection based on Radon-fractional ambiguity function

The micro-Doppler (m-D) signature of a sea surface target is employed for detection and modeled as a quadratic frequency modulated (QFM) signal. Furthermore, a novel long-time coherent integration method, i.e., Radon-fractional ambiguity function (RFRAF), is proposed to detect the m-D signal, which can compensate the range and Doppler migrations simultaneously. The m-D signal can be well matched and accumulated in the RFRAF domain by the long-time instantaneous autocorrelation function (LIACF) and the transform angle. Experiments with real radar dataset indicate that the RFRAF can achieve better integration and detection performance of a sea surface target with micromotion in case of high sea state.

Fuzzy fractal algorithm for low-observable target detection within sea clutter

In this paper fuzzy fractal algorithm for discrete signal processing is introduced. The two major concepts of the algorithm are fuzzy fractal dimension and grade of fractality, which merge fuzzy theory and fractal theory. The fuzzy concept of fractality in discrete time series can be reconstructed as a kind of fuzzy set and the objective short time series can be used by a new membership function. In order to process long time series, sliding measurement is adopted. In the end, the local grade of fractality is applied to X-band real sea clutter and the performance of the detection algorithm is analyzed. (4 pages)