In this paper, an iterative algorithm for phase synchronization in a distributed coherent transmission is proposed to achieve phase alignment of multiple transmitter signals at the receiver. In each time slot, each transmitter computes and adjusts its phase offset based on the received feedback and its own phase perturbation. Transmitters get the phase change effect near the best known phase adjustment on the received signal power, and guide the phase adjustment in the direction of increasing the received signal power. The simulation results show that the convergence speed of our algorithm proposed in this paper has been increased compared with the classical 1-bit feedback algorithm, and the convergence accuracy and stability are basically the same as those of the classical 1-bit feedback algorithm.
The unavoidable Doppler offset significantly reduces the correlation peak for code acquisition of the direct sequence spread spectrum minimum shift keying (DSSS-MSK) signal in highly dynamic environment. To eliminate the severe influence caused by the Doppler offset, a novel joint code-Doppler acquisition method based on compressed sensing (CS) is proposed. This acquisition algorithm introduces CS to spread spectrum communication. A sparse dictionary is constructed based on the observed signal, which is then reconstructed in the sparse domain by orthogonal matching pursuit (OMP) to obtain the code delay and Doppler offset simultaneously. However, the computational complexity of this algorithm is very high over a wide searching range. To reduce the computational complexity and improve this algorithm further, a two-stage acquisition method is introduced. Simulation results show that the detection probability of the proposed method reaches 0.9 in a low signal to noise ratio (SNR) at a -20dB environment.
This paper presents a compact bandpass filter based on stacked eighth-mode substrate integrated waveguide (EMSIW) resonators. By properly constructing the feeding and coupling topology of four EMSIW cavities, the proposed filter not only exhibits compact size, but also achieves enhanced selectivity by virtue of its three transmission zeros (TZs). A novel cross-coupled structure which contributes to TZs is constructed without increasing the circuit size compared with the conventional fourth-order cross-coupled model. The filter is designed and fabricated on two lays of substrate, good agreement between simulated and measured results is also obtained.
Constant false alarm rate (CFAR) detectors are widely used in modern radar system to declare the presence of targets. One or more outliers will appear in the reference cell under the multiple strong interferences situation, and the clutter power estimation will increase, which will affect the detection threshold calculation, the detection probability of CFAR detectors decrease and the alarm rates increase significantly. This paper proposes an adaptive weighted truncation statistic CFAR (AWTS-CFAR) algorithm and achieves good performance. By improving the truncation process, the truncated larger value is adaptively weighted with the smaller value in the reference cell. Since AWTS-CFAR makes the larger value in the reference cell also participate in the calculation of the background clutter power estimation, even if the truncation threshold is selected to be smaller, AWTS-CFAR will not cause too much loss of constant false alarm, and will suppress clutter edge effect as much as possible in the clutter edge environment.
In this paper, a convex optimization method has been proposed for synthesizing a cosecant squared pattern(CSP). By taking the autocorrelation function of weight vector and the maximum ripple level as the optimization variables, and changing the non-convex optimization to convex optimization problem through variable substitution, the optimal autocorrelation function can be achieved, and then we can obtain the array weight vector. Some detailed simulations are presented and results show that compared to particle swarm optimization (PSO) method, the proposed method has fewer ripples in the shaped region and lower sidelobe (SLL).
In this paper, a new approach for classifying targets captured by low-resolution Ground Surveillance Radar is proposed. Radar target is detected by the Doppler effect in radar echo signal. Those signals can be disposed in various domains to gain unique features of targets which can be used in radar target classification and enhance its effectiveness. The proposed approach consists of two steps, transforming original signals from 1D to 2D and constructing deep 2D convolution neural networks(CNN). In first step, Toeplitz matrix is made use of reconstructing Radar signal, to build a 2D plane of data. Reconstruction does not change the characteristic distribution of the signal but maps the signal from one to two dimensions in a rearranged method. Whilst,it makes possible of using 2D CNN to train the data. In second step, we take advantage of the "bottleneck" block to create 2D CNN, which guarantee the depth of CNN and ease the problem of vanishing/exploding gradients in back propagation process. method was tested on actual collected database including human and car, which achieve 99.7% accuracy on the original test set and 97% accuracy after adding noise.
In order to improve the performance of direction of arrival (DOA) estimation for sparse array, this paper applies low-rank matrix completion theory to DOA estimation and proposes an improved matrix completion model and optimization solution. The proposed method utilizes the Hermitian and Toeplitz structural information of the observation matrix as a priori information. We split the augmented Lagrangian function and minimize one of the subproblems by Dykstra alternating projection which makes the completion matrices keep the Toeplitz structure and accomplishes the denoising of the known data. The simulation results show that the proposed method can effectively realize the reconstruction of sparse array, the performance of DOA estimation is excellent, and it can be applied to coherent sources.
In this paper, a newly-designed method of ultra-low sidelobe pulse compression filter for linear frequency modulation (LFM) signal is proposed. In the conventional processing of pulse compression, there exists the problem that the ratio of mainlobe to sidelobe is too low. In order to solve this problem, the convex optimization method is used to design the coefficient of the pulse compression filter, and the ratio of mainlobe to sidelobe of the pulse compression output could achieve 60dB or more to be applied in specific engineering applications.
The synchrosqueezing transform(SST), a kind of reassignment method, aims to sharpen the time-frequency representation. In this paper, we consider synchrosqueezing transform based short-time fourier transform with instantaneous frequency rate of change to analyze nonlinear and nonstationary signal, called the adaptive synchrosqueezing transform (ASST). Compared with SST, the window width of ASST is adaptively adjusted with the instantaneous frequency rate estimation which is extracted at the signal ridge. The proposed method can generate a more energy concentrated TF representation for the non-stationary signals with fast-varying frequencies. Simulation results are provided to demonstrate the effectiveness of the proposed method.
Radar communication integration is one of the development directions of multifunctional combat systems. Design of integrated signal that can realize radar and communication functions is the main method to build an integrated system. In this paper, a constant envelope OFDM-Chirp integrated signal based on phase coding (CE-PC-OFDM-Chirp) is proposed. By analyzing the ambiguity function of the signal, the simulation results show that the proposed integrated signal has low distance sidelobe, narrow velocity main lobe and good error performance.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)