A high sidelobes suppression method is proposed for millimeter wave SAR images, which reduces the "edge effect" of SAR images by high azimuth sidelobes suppression and strong point sidelobes suppression. Firstly, noise threshold is calculated by the clear area of SAR images; Secondly, the noise threshold is applied for calculating the position of high sidelobes in the azimuth direction and a template image with numerical suppression is obtained, then the high sidelobes in the azimuth of SAR images can be suppressed; Thirdly, the position of strong point is calculated by the noise threshold, and a template image with numerical suppression is obtained to suppress the strong point sidelobes in SAR images. The method proposed in this paper is to improve the quality of SAR images through image post-processing when signal processing is difficult to solve, which provides a good data source for subsequent image processing. The effectiveness of the method has been verified by measured data.

We present a scheme to efficiently implement adaptive digital beamfoming (ADBF) on large size element-level digital array (ELDA) radar, reducing demands for both computation and instantaneous bandwidths (IBWs). In this scheme, we calculate the covariance matrix according to the interference power and direction, which are obtained by conducting fast Fourier transform (FFT) on the array observations. In this manner, the minimum required number for independent identically distributed (iid) samples is greatly reduced and thus the demand for downlink IBWs is relaxed. Further, to avoid the covariance matrix inverse, which yields ($\boldsymbol{N}^{\mathbf{3}}$) complexity, we recast the minimum variance distortionless response (MVDR) problem into an unconstrained optimization and solve it using conjugate gradient method (CGM). Especially, we derive a way to employ fast Fourier transform (FFT) and nonuniform FFT (NuFFT) in calculating the objective function and gradient, such that the problem can be solved in $\mathcal{O}(\kappa / \log (N))$ complexity with $\kappa$ being a scalar determined by iteration number.

In order to enable the distributed coherent aperture radar (DCAR) to enter the cooperative mode of interferometry, it is necessary to calibrate each unit in the space, time and frequency domains, etc. The existing spatial baseline calibration method still has shortcomings in convenience, accuracy and flexibility. Aiming at the problem that the spatial baseline calibration mode of distributed coherent radar using signal phase-frequency information is not accurate enough to satisfy the system entering the fully coherent working mode, based on the cooperative unmanned aerial vehicles (UAV) as the external radiation source, the hovering position of the UAV is independently planned to solve the problem of low accuracy and poor stability of baseline calibration by processing signals from multi-view observation. The simulation and data processing show that this method can further reduce the error of baseline solution and realize the coherent mode of distributed radar in airspace.

Cross-eye jamming exhibits significant threat against current monopulse radars. In this work, a novel method based on orbital angular momentum evaluation is proposed for robust monopulse angle estimation against cross-eye jamming. The direction of arrival of the incident electromagnetic field is estimated with the spatial gradients of the orbital angular momentum topological charge distribution in the radar plane. The mathematical modeling as well as the numerical simulations are performed. According to the results, the angular estimation errors with our proposal is much lower compared with the traditional approach based on amplitude comparison in presence of cross-eye jamming.

This paper investigates the low-altitude air traffic control under communication constraints in integrated sensing and communication (ISAC) networks. Small base stations (S-BSs) can perform both sensing and communication operations by sensing nearby unmanned aerial vehicles (UAVs) for low-altitude air traffic control. Meanwhile, SBSs transmit requested information to nearby user equipments (UEs) for satisfying their requirements. We formulate the control problem as a joint optimization problem of SBS sensing and communication within each scheduling period. The joint optimization problem is a mixed integer programming problem and the goal is to maximize the network sensing profit obtained from sensing UAVs by SBSs. We propose a combined relaxation and greedy-based nearest neighbor quantization algorithm to solve it efficiently. By analyzing the property of the problem, we first reformulate it as a continuous optimization problem and prove its convexity. The optimal solution of the convex optimization problem can be obtained, which is the performance upper bound of the original problem. Then we propose a greedy-based nearest neighbor quantization algorithm to refine the optimal but infeasible solution into a feasible and near-optimal solution. Simulation results show that our proposed algorithm performs close to the performance upper bound with different system parameter settings.

In the case of target location using multistatic radar in the far-field region, the linear superimposed cross-location method tends to generate more false alarms and misses, resulting in lower location accuracy. To solve this problem, a method of multistatic radar working together for sparse target location is proposed. Exploiting the sparse characteristics of targets mainly existing within specific ranges, a high-precision cooperative location method based on sparse recovery for multistatic radar is introduced. By employing sparse algorithms and nonlinear processing, the traditional cross-location method for multistatic radar is improved, thereby enhancing the location accuracy of the multistatic system. Simulation results show that this method can accurately locate the actual position of the target, achieving high-precision location of cooperative multistatic radar in far-field region.

In this paper, we propose a target high-precision positioning method based on heterogeneous image registration, including target longitude, latitude and altitude. The longitude and latitude are obtained by registering video SAR images with optical map, and the positioning information on the optical map is precisely known. Based on the registration of video SAR images and optical video images, the target height can be solved according to the projection direction difference of the height target on the two types of images, and the target altitude is calculated by adding the ground altitude. The video SAR and optical video images come from the integrated sensor, and the high frame rate imaging of video SAR can simultaneously satisfy the positioning of moving and stationary targets. Finally, the positioning results of this method is verified by measured data.

This paper investigates the low-altitude air traffic control in joint radar and communication (JRC) networks. Small base stations (SBSs) apply JRC technology to serve unmanned aerial vehicles (UAVs) by sensing UAVs and transmitting the sensing information to the macro base station (MBS), which assists for low-altitude air traffic control. We formulate this control problem as a service strategy optimization problem, where SBSs need to perform multiple sense and communication operations within each scheduling period. The goal is to maximize the network service profit, which is obtained from serving UAVs by SBSs. We prove that the objective function of the optimization problem is submodular and then propose a greedy-based service algorithm to solve it efficiently with low complexity. The proposed algorithm provably achieves at least $1 - \frac{1}{{\sqrt e }}$ of the optimum value. Simulation results show that our proposed algorithm performs close to the optimal solution with different system parameters.

This paper investigates the resource scheduling problem in unmanned aerial vehicles (UAV) networks. By equipping different types of sensors on UAVs, they can execute different kinds of task in the network. The mobility and flexibility of UAVs can adapt to the dynamic and complex environment better compared to the sensors with fixed locations. Each UAV can cover multiple tasks due to its mobility and finite working range that depends on its equipped sensor. Meanwhile, each task can be covered by multiple UAVs. Due to the limited resource, overlapped coverage area and equipped sensor type of UAVs, they need to schedule their resource carefully for executing more tasks with higher profit. We formulate the resource scheduling problem of UAVs as an association problem between UAVs and tasks. The goal is to maximize the network profit obtained from task execution by optimizing the association strategy. The optimization problem is proven to be NP-hard. We propose an $\varepsilon$ -greedy modified particle swarm optimization (PSO) algorithm to solve the association algorithm. Based on PSO algorithm, we propose an $\varepsilon$ -greedy modification algorithm to balance the exploration and exploitation of particle movements. Simulation results demonstrate that our proposed algorithm can increase the performance by 2%-25% and convergence speed by 60% compared to PSO algorithm. Meanwhile, our proposed algorithm is shown to outperform existing algorithms.

A novel method based on momentum estimation is proposed for monopulse angle measurement in radar systems. The direction of arrival (DOA) is estimated with orbital angular momentum (OAM) topological charge distribution in the radar array plane. Our proposal is compatible for field with non-plane phase-front. The instances of sphere phase-front, vortex phasefront and interference pattern induced by phase shifted transmitters are investigated. According to the results, the proposed method exhibits superiority in estimating the DOA of electromagnetic field with non-plane phase-front compared with the amplitude comparison method.

Binary weighted beamforming aims to achieve the optimal radiation pattern with only part of the array elements. The corresponding applications include sparse synthesis, management of aperture resources and targets allocation. However, due to the discrete weighting values as well as the corresponding non-deterministic polynomial computational complexity, most existing adaptive beamforming approaches are not suitable. Here, an algorithm based on simulated annealing optimization is proposed and the performances of binary weighted beamforming with one-dimensional and two-dimensional random arrays are numerically evaluated. According to the results with number of array elements ranging from 17 to 151, the side lobes can be reduced by 4.43 dB in average utilizing only around 66.7% of the total array elements while the width of the main lobe has increased by about 30%. For adaptive beamforming circumstances, an extra depth of up to 20 dB is achieved in presence of a preset jammer. Furthermore, a proof-of-principle optical experiment employing the spatial light modulator device is designed and studied through Huygens-Fresnel simulation.

Unmanned aerial vehicle (UAV) equipped with a software defined radio (SDR) device could serve as a flexible and inexpensive receiver element, while the whole swarm forms a reconfigurable antenna array. However, it is a challenging task to perform coherent processing on signals collected by the distributed UAVs swarm due to the high mobility of the array structure as well as the inherent time, frequency and phase uncertainties between each individual SDR receivers. Here, a concise and cost-effective method for positioning and synchronization of UAVs swarm is proposed. This method is based on post processing of received white noise signals from asynchronous ground beacons. With this approach, the only payload onboard is an SDR device while there is no need for GNSS modules or distribution of local oscillator (LO) signals. The architecture of the receiving mode of the UAVs swarm is mathematically modeled. The received baseband signals are generated with swarm size of 6 and instantaneous bandwidth of 24MHz during simulation. Positioning, synchronization and coherent signal processing are performed. Furthermore, angle of arrival (AoA) estimation and target localization are investigated with the proposed distributed coherent signal reception scheme.

Sensors with multiple functions and working modes constitute the nodes of the multi-mode sensor network. In this work, the mathematical model of task assignment problem in multi-mode sensor network is established. The model consists of task priorities, capability matching relationships, costs of tasks, resources of sensors and mode constraints. A two-stage scheme composed of joint mode selection and task assignment is proposed and evaluated. First, the cooperative working modes of sensors are determined with quantum-behaved particle swarm optimization (QPSO). Next, task assignment is performed with modified Hungarian algorithm under market-based assumption. Our proposed two-stage approach exhibits higher flexibility and achieves better tasks completion ratio compared with the benchmark of greedy policy, while the time complexity is much less than the conventional assignment scheme with PSO algorithm. According to the Monte Carlo simulation, the assignment of 20 sensors and 1000 tasks is completed in 0.225 seconds with the proposed strategy, while the completion percentage of tasks is 7% higher than those achieved with the greedy algorithm.

This paper studies the collaborative unmanned aerial vehicle (UAV) sensing in integrated sensing and communication (ISAC) networks. By equipping sensing and communication units on UAVs, they can execute sensing tasks and transmit the sensing information to the base station (BS) for environment sensing. Due to the mobility and dense deployment of UAVs, they can sense the environment with much lower cost compared to the BS sensing. We aim to minimize the network sensing cost by optimizing the UAV deployment and task assignment collaboratively. For this joint optimization problem, we propose an iterative mechanism to optimize the UAV deployment and task assignment iteratively. UAV deployment problem is modeled as a cluster problem and we utilize a K-means cluster algorithm to solve it efficiently. For task assignment problem, we propose a greedy algorithm to solve it with low complexity. Simulation results validate the effectiveness of our proposed method in different scenarios.

Unmanned Aerial Vehicle(UAVs) swarm has great advantage over traditional equipment in cooperative detection scenario for its easy-maneuverability, no human injury and low cost, etc. As a representative task in cooperative detection, region coverage has widely applications in environmental monitoring, search and rescue, etc. In UAV cooperative detection tasks, the most critical step is task planning, which has direct impact on the overall detection performance. The target of task planning is to generate planned actions and flight route for UAVs to complete specific detection task according to UAV swarm locations, sensor ability, task region, etc. However, traditional task planning methods for UAV cooperative detection that based on evolutionary computing or reinforcement learning always need plenty of time for getting planning results. In this paper, we proposed a top-down task planning algorithm based on greedy policy to tackle this problem. The core idea of the proposed method lies in that we choose optimal detection trace from all trace candidates during each planning step in a greedy manner via a predefined performance indicator. Moreover, we also proposed a simple but effective procedure for generate detection trace candidates by corner points and nearest border points extraction. To evaluate the effectiveness of the proposed method, we conducted comprehensive experiments for the representative swarm detection task region coverage. Experiment results demonstrated the effectiveness of the proposed method and superiority over traditional methods on task planning speed.

In order to meet the demand for passive ranging of sound sources in shallow water, a normal mode separation and ranging method based on a large-aperture horizontal array is proposed. This method deals with the problem of modal separation that caused by the bending of the modal curve in the frequency-wavenumber domain. Under the condition that the cut-off frequency of each order normal mode does not change with the signal frequency, a alignment method based on wavenumber scaling is presented to realize the effective separation of normal modes. The energy focusing of normal modes is realized by nonlinear phase compensation, and the passive ranging of the sound source is realized by combining distance traversal and peak extraction. This method can effectively achieve gains in space and frequency domains, and obtain multi-modal energy accumulation, which provides a new approach for the distance estimation of weak sound sources. The effectiveness of the method is verified by simulation data.

Simultaneously multi-beam steering techniques are capable for multi-target illuminating, tracking and jamming. The opportunistic array of UAVs proves to be a flexible and less vulnerable platform for these tasks due to the high mobility of its structure. However, most of the existed pattern synthesis and task allocation algorithms developed for opportunistic arrays are heavily time-consumed and thus are not suitable for such a dynamic system of UAVs. Here, a concise and analytical algorithm to determine the probabilities of UAV operating modes and to achieve reconfigurable multi-beam steering is proposed. This algorithm is non-iterative and takes lower computational complexity. With our scheme, task allocation and multi-beam steering are performed with swarm size of up to 2000. According to numerical simulation, the fidelity and efficiency values of the multi-beam steering could reach 0.97 and 0.8, respectively.

This paper investigates the collaborative unmanned aerial vehicle (UAV) sensing in multi-UAV networks. By equipping sensors, communication units and computation units on UAVs, they can sense the environment by executing surveillance tasks and computation tasks. UAVs execute surveillance tasks by mon-itoring the environment and computation tasks by processing the sensing information offloaded from ground terminal nodes. We formulate the collaborative multi-DAV sensing problem as a joint optimization problem of UAV deployment and task allocation. The goal is to maximize the sensing utility, which is defined as the weighted sum of utility for executing surveillance and computation tasks. Note that UAV deployment and task allocation are highly coupled, we divide the joint optimization problem into two sub-problems and then propose an iterative mechanism by optimizing two sub-problems iteratively. UAV deployment optimization problem is non-convex, and thus we utilize a differential evolution (DE) algorithm to solve it with high efficiency and simple implementation. Task allocation optimization problem is proven to be NP-hard, and thus we propose a particle swarm optimization method with greedy-based reconstruction (PSOGR) to solve this complex combinatorial optimization problem efficiently. Simulation results demonstrate the benefits of our proposed method compared with existing algorithms for different system parameters.