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.

In order to effectively detect moving targets in heavy sea clutter, the micro-Doppler (m-D) effect is studied and an effective algorithm based on short-time fractional Fourier transform (STFRFT) is proposed for target detection and m-D signal extraction. Firstly, the mathematical model of target with micromotion at sea, including translation and rotation movement, is established, which can be approximated as the sum of linear-frequency-modulated signals within a short time. Then, due to the high-power, time-varying, and target-like properties of sea spikes, which may result in poor detection performance, sea spikes are identified and eliminated before target detection to improve signal-to-clutter ratio (SCR). By taking the absolute amplitude of signals in the best STFRFT domain (STFRFD) as the test statistic, and comparing it with the threshold determined by a constant false alarm rate detector, micromotion target can be declared or not. STFRFT with Gaussian window is employed to provide time-frequency distribution of m-D signals, and the instantaneous frequency of each component can be extracted and estimated precisely by STFRFD filtering. In the end, datasets from the intelligent pixel processing radar with HH and VV polarizations are used to verify the validity of this proposed algorithm. Two shore-based experiments are also conducted using an X-band sea search radar and an S-band sea surveillance radar, respectively. The results demonstrate that the proposed method not only achieves high detection probability in a low-SCR environment but also outperforms the short-time Fourier transform-based method.

Due to the influence of the complex marine environment, the marine target detection based on statistical theory is difficult to achieve high-performance. Moreover, due to various targets' motion characteristics, only using a single feature for detection is unreliable. In this paper, from the perspective of feature extraction and classification, marine target and sea clutter are classified by deep learning methods. To achieve the required false alarm rate, the dual-channel convolutional neural networks (DCCNN) and false-alarm-controllable classifier (FACC)-based marine target detection method is proposed. Firstly, the measured sea clutter and the target signal are preprocessed to obtain the time-Doppler spectrum and amplitude information. The Marine-DCCNN (MDCCNN) is then constructed for features extraction and fusion, and the feature vectors of the signals are obtained. The performance of different feature extraction models is tested and compared. Finally, the FACC is used as a detector to classify the feature vectors into two categories and the control of the false alarm rate is realized. The detection performances were verified by two popular public radar datasets, i.e., IPIX radar dataset (floating target) and CSIR dataset (maneuvering marine target). The results show that compared with single-channel CNN and histogram of oriented gradient support vector machine (Hog-SVM) classification, a combination of MDCCNN feature extraction model and softmax classifier can achieve higher performance and controllable false alarm rate. Moreover, HH polarization and mixed training datasets under different sea states can help improve detection performance.

Robust and effective detection of a marine target is a challenging task due to the complex sea environment and target's 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.

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.

The frequency diverse array (FDA) radar has drawn great attention due to the periodicity of the beampattern in range, angle and time. In this letter we restudy the recent work that designed a time-invariant beampattern of the frequency diverse array (FDA) radar which can focus the transmit energy in a desired position. We reanalyze the derivation of the FDA beampattern synthesis and point out the neglected constraint condition, which leads the research of the FDA beampattern synthesis to an impractical direction. By comparing the replication of one of the prior work with our result, we draw a conclusion that it’s impossible to obtain a beampattern merely focusing in some specific spatial positions and lasting for some specific time.