A multiple-input multiple-output (MIMO) radarwith widely separated antennas on moving platforms suffers from the effects of platform deviations on the target parameter estimation since the trajectories of the moving platforms are sensitive to environmental factors, such as strong wind. This article addresses the joint estimation of multiple target positions and velocities as well as radar system deviations to minimize the impact of platform deviations. The proposed algorithm can also be regarded as a self-calibration technique. First, the grid search dimensions for parameter estimation are reduced via a generalized maximum likelihood (GML) algorithm. Second, the adaptive gradient (AdaGrad) method is used to implement the GML estimation for multitarget echo delays and Doppler shifts. Finally, to address the nonlinear estimation problem of interest, the iterative least squares method is used to estimate the multiple target positions and velocities as well as radar system deviations based on the estimated delays and Doppler shifts. Prior information, such as target positions or the probability distribution of the echo coefficient, is not needed in the proposed method. The parameter identifiability is also derived in this article. Numerical simulations show that compared to a method without the estimation of system deviations, the proposed method is more efficient with respect to the derived Cramér–Rao bound.

Synthesis of unequally spaced arrays with uniform excitation is considered in this paper. An iterative method based on Second-order Cone Programming is developed to search the geometry with local optimum Peak Side Lobe Level (PSLL), which is valid for arrays of different scale regardless of geometry constraints like centrosymmetry and minimum separation. By statistical analysis and comparison with other methods, it is verified that the computational complexity of the proposed method is only related to the dimension of optimization variables, and it can further exploit the potential of unequally spaced arrays with uniform excitation under acceptable complexity.

Integrated radar and communications (IRC) technology has become very important for civil and military applications in recent years, and IRC waveform design is a major challenge for IRC development. In this paper, we focus on the IRC waveform design based on the multi-symbol orthogonal frequency division multiplexing (OFDM) technique. In view of the defects resulting from high peak-to-mean envelope power ratios (PMEPRs) and high range sidelobes in IRC systems, an intelligent and effective IRC waveform design method jointly optimized with the PMEPR and peak-to-sidelobe ratio (PSLR) is proposed. Firstly, a flexible tone reservation (TR)-based IRC waveform structure is applied in both temporal and frequency domains, i.e., multi-symbol OFDM waveform. Secondly, the optimization problem considering PMEPR and PSLR and extending them to the $L_p$-norm form is reformulated. Then, the conjugate gradient of the objective function is analytically derived and the conjugate gradient algorithm (CGA) is presented to simultaneously improve the PMEPR and PSLR. Finally, the simulation results show that the proposed algorithm can efficiently generate IRC waveforms with an excellent PMEPR, PSLR, radar signal-to-noise ratio (SNR), and bit error rate (BER) performance.

The increasing accessibility of unmanned aerial vehicles (UAVs) drives the demand for reliable, easy-to-deploy surveillance systems to consolidate public security. This paper employs passive bistatic radar (PBR) based on a digital audio broadcast (DAB) satellite for UAV monitoring in applications with power density limitations on electromagnetic radiation. An advanced version of the extensive cancellation algorithm (ECA) based on data segmentation and coefficients filtering is designed to improve the efficiency of multipath clutter suppression while retaining robustness, for which the effectiveness is verified by theoretical derivation and simulation. The detectability of small UAVs with DAB satellite-based PBR is validated with experimental results, with which the influence of target altitude and bistatic geometry are also analyzed.

Airborne distributed coherent aperture radar is of great significance for expanding the detection capability of the system. However, the extra observation dimension introduced by its sparse configuration also deteriorates the performance of traditional adaptive processing in a non-uniform environment. This paper focuses on moving target detection when the system works in a clutter–jamming-coexisting environment. In order to make full use of the specific low-rank structure to reduce the requirement for training data, this paper proposes a two-stage adaptive scheme that cancels jamming and clutter separately. The proposed suppression scheme first excludes the mainlobe jamming component from the training data based on the prior clutter subspace projection and performs intra-node clutter suppression. Then, the remaining jamming is jointly canceled based on the covariance obtained with its inter-pulse mixture model. Numerical examples show that this scheme can effectively reduce the blocking effect of main lobe jamming on high-speed targets but, due to the inaccuracy of the prior subspace, there is a certain additional loss of signal-to-noise ratio for near stationary targets. The simulation also shows that the proposed scheme is equally applicable to systems with a time-varying distributed geometry.

The tone reservation (TR) approach is mainly adopted to reduce the peak-to-mean envelope power ratio (PMEPR) of orthogonal frequency division multiplexing (OFDM) waveform with low TR ratio (TRR) in classic 4G communication systems. However, for the OFDM integrated radar and communication waveform, high TRR is necessary to simultaneously maintain the radar detection performance as well as communication performance. For cases with high TRR, the traditional waveform optimization algorithms have low execution efficiency and a poor PMEPR convergence level, and thus a new algorithm is needed. This paper proposes a new PMEPR optimization algorithm based on conjugate gradient. Firstly, by introducing the concept of $L_p$-norm, the PMEPR of the OFDM waveform is accurately remodeled as the objective function of the waveform optimization problem. Secondly, the conjugate gradient of the objective function is analytically derived to form the basis of the efficient PMEPR optimization. Finally, a PMEPR optimization algorithm based on the Polak–Ribière–Polyak (PRP) conjugate gradient is proposed. The simulation results verified the proposed algorithm in terms of optimization efficiency, as well as convergence level, and the initial experimental results suggested the practicality of the proposed algorithm.