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.

Aiming at the difficult problem of the classification between flying bird and rotary-wing drone by radar, a micro-motion feature classification method is proposed in this paper. Using K-band frequency modulated continuous wave (FMCW) radar, data acquisition of five types of rotor drones (SJRC S70 W, DJI Mavic Air 2, DJI Inspire 2, hexacopter, and single-propeller fixed-wing drone) and flying birds is carried out under indoor and outdoor scenes. Then, the feature extraction and parameterization of the corresponding micro-Doppler (m-D) signal are performed using time-frequency (T-F) analysis. In order to increase the number of effective datasets and enhance m-D features, the data augmentation method is designed by setting the amplitude scope displayed in T-F graph and adopting feature fusion of the range-time (modulation periods) graph and T-F graph. A multi-scale convolutional neural network (CNN) is employed and modified, which can extract both the global and local information of the target’s m-D features and reduce the parameter calculation burden. Validation with the measured dataset of different targets using FMCW radar shows that the average correct classification accuracy of drones and flying birds for short and long range experiments of the proposed algorithm is 9.4% and 4.6% higher than the Alexnet- and VGG16-based CNN methods, respectively.

Under the framework of feature-based detection of small targets on sea surface, existing feature extraction methods only use the echo data of current frame while ignoring the influence of historical echo data. Nevertheless, due to the non-stationarity of sea clutter, it may lead to unstable extraction of detection features, and then affect detection performance. To solve this problem, this paper designs a feature extraction method based on a priori information for small target detection. It firstly obtains a priori information from historical echo data by kernel density estimation (KDE) method. Then, the corresponding feature estimation method is utilized to obtain improved feature according to the relationship between current frame data and a priori information. Finally, the feature information of current frame is integrated into a priori information to prepare next feature extraction. Measured data are utilized to verify the performance of proposed method and the results reveal that, this method can effectively improve detection performance especially when sea clutter and target echo have good separability. In addition, the complexity of algorithm is analyzed to prove that proposed method has certain application potential.

The micro‐Doppler (m‐D) signature of a marine target is used for detection within sea clutter in this study. The marine target usually has translational and rotational motions, and the corresponding radar returns can be modelled as a quadratic frequency modulated signal over a period of time. To compensate the range walk and Doppler migration of the micromotion target simultaneously, a novel long‐time coherent integration detection algorithm, that is, Radon‐linear canonical ambiguity function (RLCAF), is proposed. It has been proved that the m‐D signal can be well matched and accumulated in the RLCAF domain using the long‐time instantaneous autocorrelation function and associated with the parameterisation of the linear canonical transform. Furthermore, to suppress sea clutter and improve signal‐to‐clutter ratio, the authors propose a RLCAF spectrum subtraction method using the different properties of RLCAF representations between the target and sea clutter. Finally, experiments with real radar dataset indicate that the proposed method can achieve better integration and detection performance of a marine target with micromotion in case of high sea state.

The frequency diverse array (FDA) radar has drawn great attention due to its range-, angle-, and time-dependent beampattern. However, the FDA radar suffers ambiguities in determining target locations i.e. angle and range of targets are coupled. In this letter, we find that the key to decoupling the target location lies in destroying the mutual dependence of angle and range. Hence, the multiple-frequency continuous wave (MFCW) radar is proposed. The range measurement is obtained by transmitting CWs with multiple frequencies and separating the multiple echoes by digital mixer in the receiving end. The range information exists in the phase difference among the multiple echoes, and this process can be seen as sampling in range by multiple frequencies. Then if a receiving array is used for spatial sampling, angle measurement is obtained and the target location is decoupled. Four modes of the MFCW Radar are derived and the reason for the uncoupled angle-range indication in the beampattern is analyzed in detail. The effectiveness of the proposed scheme is verified by simulation results.