The orthogonal frequency division multiplexing (OFDM) sequences are widely used in 4 G, 5 G and integrated sensing and communication (ISAC). In this paper, we address the challenge of designing orthogonal OFDM sequences. The weighted sum of the auto-correlation and cross-correlation is minimized. Considering the base station (BS) hardware constraints, the length of OFDM sequence in time domain and frequency domain is inconsistent. Furthermore, we consider the limitation of peak to average power ratio (PAPR). To solve the non-convex problem, we have developed an efficient alternating direction method of multipliers (ADMM) algorithm. Numerical simulations confirm the effectiveness of the proposed algorithm.

In this paper, we consider the detection of range-spread targets with unknown steering vectors for radar systems. Based on the Rao test and Wald test, we proposed two novel adaptive detectors of range-spread targets with unknown steering vectors. And we exploit the persymmetric property of the noise covariance matrix, which enables the two proposed detectors robust in the situation of limited training data. Moreover, we exploit a series of equivalent transformations to transform the test statistics into the real domain to prove the CFAR property concisely. Finally, the Monte Carlo simulations verify the effective detection performance of the proposed detectors. We found that the novel detectors perform well in a vast number of situations.

This paper deeply explores the detection of a signal belonging to a union of subspace (UoS) in the presence of interference subspace. We propose three detectors based on the generalized likelihood ratio test (GLRT), Rao criterion, and Wald criterion using the presymmetry structure of the received data. By taking advantage of the data’s presymmetric structure, we can detect the signal more effectively. Experimental results show that the proposed detectors perform better than the traditional detectors in the presence of interference subspace, especially when the training data is insufficient. In general, this paper provides an effective method for solving the problem of signal detection with interference subspace.

This paper deals with the target detection problem in nonzero-mean compound Gaussian (CG) sea clutter with the generalized inverse Gaussian (GIG) texture. With the improvement of radar resolution, the CG distribution is adopted to model the sea clutter. Then, considering the characteristics of real sea clutter, the CG model with the GIG texture is applied. Furthermore, sea clutter signals are assumed to be nonzero-mean. A novel adaptive two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) detection algorithm is proposed. Firstly, the test statistic of the proposed detector with known GIG texture, mean vector (MV), and covariance matrix (CM) is derived. Secondly, replacing with the estimates of GIG texture, MV, and CM, the adaptive detector can be acquired. The numerical results indicate the performance of the proposed detector.

This letter deals with the target detection problem for polarimetric marine radar. The sea clutter is modelled as the compound Gaussian (CG) distribution with lognormal texture. We propose three detectors based on the two-step generalized likelihood ratio test (GLRT), the complex value Rao, and Wald tests by utilizing the persymmetric properties of the polarimetric speckle covariance matrix (CM). The lognormal texture component and the speckle CM are estimated by the maximuma posteriori(MAP) criterion and the polarimetric persymmetric fixed-point estimator, respectively. In addition, we provide proof of the constant false alarm rate (CFAR) properties of the designed polarimetric detectors. Moreover, we evaluate the detection performance of the proposed detectors in the simulated data and the measured sea clutter data, and the simulation results show the proposed detector outperforms the competitors more than 1dB in different situations.

This paper addresses the problem of subspace detection in the compound Gaussian sea clutter with lognormal texture and structured interference. We proposed three novel subspace detectors by two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT), the Rao test, and the Wald test. In the first step, we assume the texture component and speckle covariance matrix (CM) are known, and we derive the test statistics of the proposed detectors. Then, in the second step, we substitute the estimated texture component and speckle CM to obtain the adaptive detectors. Further, we exploit the persymmetric property of the speckle CM to improve the detection performance of the proposed detectors. Moreover, we prove the constant false alarm rate (CFAR) properties of the novel subspace detectors with respect to the speckle covariance matrix and the scale parameter of the texture component of the non-Gaussian sea clutter. Besides, we verify the detection performance of the proposed subspace detectors by numerical experiments in both simulated and measured sea clutter. The simulation results show that the novel subspace detectors perform better than the comparison detectors in the case of limited training data, mismatched signals, and structured interference.