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Integration of radar and communication (IRC) is conducive to improving hardware resource utilization and spectrum utilization. This paper proposes a joint range and velocity estimation method for IRC based on multi‐symbol orthogonal frequency division multiplexing (OFDM) radar pulses. A two‐step parameters estimation method is applied, where estimating the signal parameters via rotational invariance technique (ESPRIT) and weighted subspace fitting (WSF) perform rough and fine estimations. We first build a receiving model for integrated signal based on element‐wise division. The ESPRIT method is then adopted based on the double Vandermonde structure array manifold for a rough estimation. It has relatively high estimation error but low computational complexity. The WSF method based on alternating projection (AP) algorithm is introduced to make the estimation more precise and reduce its error. It uses the estimation results in the first step and reduces the overall computational cost. Simulation results show that the proposed method reduces the computational complexity while improving the estimation accuracy compared with the WSF method.

Target localization plays an important role in the application of radar, sonar, and wireless sensor networks. In order to improve the localization performance using only two stations, a hybrid localization method based on angle of arrival (AOA) and time difference of arrival (TDOA) measurements is proposed in this paper. Firstly, the optimization model for localization based on AOA and TDOA are built, respectively, in the sensor network. Secondly,the majorization-minimization (MM) method is employed to create surrogate functions for solving the multiple objective optimization problem. Next, the hybrid localization problem is solved by the projected gradient decent (PGD) method. Finally, the Cramer–Rao lower bound (CRLB) for the joint AOA and TDOA method is derived for the comparison. Simulations proved that the proposed method has improved localization performance using AOA and TDOA measurements from only two base stations.

A radar and communication integration (RCI) system has great flexibility in allocating antenna resources to guarantee both radar and communication performance. This paper considers the array allocation problems for multiple target localization and multiple platforms communication in an RCI network. The objective of array allocation is to maximize the communication capacity for each channel and to minimize the localization error for each target. In this paper, we firstly build a localization and communication model for array allocation in an RCI network. Minorization maximization (MM) is then applied to create surrogate functions for multiple objective optimization problems. The projected gradient descent (PGD) method is further employed to solve two array allocation problems with and without a certain communication capacity constraint. Computer simulations are conducted to evaluate the performance of the proposed algorithms. The results show that the proposed algorithms have improved localization and communication performance after efficiently allocating the array resource in the RCI network., 31 pages

In order to save the radar resources and obtain the better low probability intercept ability in the network, a novel radar selection method for target tracking based on improved interacting multiple model information filtering (IMM-IF) is presented. Firstly, the relationship model between radar resource and tracking accuracy is built, and the IMM-IF method is presented. Then, the information gain of every radar is predicted according to the IMM-IF, and the radars with larger information gain are selected to track target. Finally, the weight parameters for the tracking fusion are designed after the error covariance prediction of every working radar, in order to improve the IMM-IF. Simulation results show that the proposed algorithm not only saves much more radar resources than other methods but also has excellent tracking accuracy.

Stepped frequency radars have been extensively used to achieve high range resolution. They use inverse discrete Fourier transform (IDFT) for processing a set of different frequency pulses in radar receiver. Due to the usually small number of targets in radar scene, there is a sparsity in the time, frequency and space domain. Thus, compressive sensing (CS) is a promising solution to target detection and estimation. In this paper, we propose a novel sampling scheme using difference set codes in CS-based stepped frequency radars. In the proposed method, the required sampling rate for signal recovery can be far less than the Neuquist rate11By the Neuquist rate, we mean twice the maximum frequency component of signal., Simulation results show that by using difference codes for pulsating pattern, high range resolution can be achieved, and at the same time, a significant reduction in the number of transmitted pulse is obtained, compared to conventional IDFT-based processing method.

In a radar and infrared sensor synergistic tracking system, an improved radar and infrared sensor cooperative tracking algorithm is proposed based on interacting multiple model and unscented Kalman filter (IMMUKF), which can reduce the radar radiation time. Firstly, the tracking model for the radar and infrared sensor is built and IMMUKF is employed as a filter. Secondly, a novel tracking quality factor is designed for control the radar’s radiation. The residual of the new information obtained by comparing the filtering result with the estimated measurement is selected as a criterion. Finally, the working time of radar and infrared sensor is adaptively controlled. And the simulation results show that the proposed method can reduce the radiation time with excellent tracking accuracy.

This paper presents a novel array allocation method for radar and communication integration. Firstly, the math model between the array resource and tracking and communication performance is built and analyzed. The primary objectives of radar and communication are then designed respectively to minimize tracking error and bit error rate in the presence of multiple target tracking and channel communication. Furthermore, we transfer both problems to convex problems which are solved by checking the conditions of Karush-Kuhn-Tucker(KKT). Simulation results on array allocation performance of the proposed method and conventional one are conducted finally, which also prove effectiveness of the proposed method.

This paper considers a joint scheme of array resource allocation and transmit beamforming for radar and communication integration (RCI) capable of multiple target tracking and channel communication. The primary objective is to jointly minimize the Cramer–Rao lower bound (CRLB) of tracking filtering and Bit Error Rate (BER) of the communication subsystem. Firstly, two constrained optimization models are constructed and combined to obtain the RCI. Next, Majorization minimization (MM) is employed to convert the resulting optimization into a surrogate simpler problem, which is then solved by projected gradient descent (PGD). In addition, a transmit beampattern design problem is considered through optimizing the waveform covariance matrix under the shared and separated deployment. An appropriate beampattern is obtained based on array resource constraint, employing the PGD, while the peak power of the transmit beam is guaranteed for each target. Furthermore, iterative shrinkage-thresholding is applied as acceleration to the PGD in each algorithm. Numerical experiments are provided to illustrate the efficient performance of the joint array allocation and transmit beamforming for the RCI system, where the convergence of the algorithms is also confirmed.

Opportunistic array radar is a new radar system that can improve the modern radar performance effectively. In order to improve its radio frequency stealth ability, a novel coordination method of opportunistic array radars in the network for target tracking in clutter is presented. First, the database of radar cross section for targets is built, then the signal-to-noise ratio for netted radars is computed according to the radar cross section and range of target. Then the joint probabilistic data association algorithm of tracking is improved with consideration of emitted power of the opportunistic array radar, which has a main impact on detection probability for tracking in clutter. Finally, with the help of grey relational grade and covariance control, the opportunistic array radar with the minimum radiated power will be selected for better radio frequency stealth performance. Simulation results show that the proposed algorithm not only has excellent tracking accuracy in clutter but also saves much more radiated power comparing with other methods.

With the development of multifunctional radar and radio frequency (RF) stealth technology, modern radar needs to save as much operating time as possible. During the process of radar target tracking, with interacting multiple models (IMM), this paper proposes an adaptive Markov transition matrix to update the last step for existing radar target tracking algorithms. First, we take interacting multiple models as the main algorithm frame. Then, using gray relation and particle swarm optimization (PSO), multiple-target adaptive sampling interval algorithm is adopted. After the PSO process, we study two methods to update a Markov transition matrix in real time. One is with the ratio of likelihood function, and the other is with the compression ratio of estimation error. Simulations illustrate that our method is effective in reducing operating time for radars.

In order to achieve the important tactical requirement of low probability of intercept (LPI) in the radar network, dynamically controlling the emission of the radars is very necessary. Firstly, a data fusion method based on an interacting multiple model and information filtering (IMM-IF) algorithm is presented. Then, the influence of the emitted power is considered in the tracking performance of the radar network, and a novel power scheduling method based IMM-IF is proposed, which controls the power of radar according to predicted covariance matrix during tracking. Finally, taking advantage of the relation model between the emitted power and the tracking performance, the Wind Driven Optimization (WDO) method is used to select the emitted power of the radar network. The tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations. The results are validated through the comparison with other methods.

A novel radar energy control strategy based on an improved Interacting Multiple Model Particle Filter (IMMPF) tracking method is presented in this paper. Firstly, the IMMPF tracking method is improved by increasing the weight of the particle which is close to the system state and updating the model probability of every particle. Based on this improved IMMPF method, an energy control method for Low Probability of Intercept (LPI) is then presented, which controls the emission time and power of radar according to the target's range and radar cross section (RCS), under the condition of constant detection probability. The tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations. The results are validated through the comparisons with other methods.

We present a novel binary version of multiobjective wind driven optimization (WDO) for emitted beamforming of opportunistic array radar, which is assumed as a multiobjective optimization problem. Firstly, the emitted signal model and objective functions of optimization are presented. Then the algorithm proposes a new definition of the position vector of air parcel, and brings a good discretization interpretation of continuous WDO. For multiobjective optimization, the grey relational grade (GRG) is then used to measure the similarity between the best two solutions for these two objectives. The best pressure locations with the maximum GRG will be recorded as the best two candidate solutions to the problem, and a final optimization result will be selected according to the importance of the two objectives. Finally, the proposed improved WDO has been applied for the optimal design of beamforming of the opportunistic antenna array, which needs a trade-off between the 3 dB main beam width and sidelobe level. The simulation results show that the proposed method outperforms conventional particle swarm optimization (PSO) in the optimal beamforming by achieving more reduction in the sidelobe level and saving more runtime.

A novel radar energy control strategy based on an Interacting Multiple Model Particle Filter (IMMPF) tracking method is presented in this paper, which controls the radiated power of radar according to predicted tracking Cramer-Rao Lower Bounds (CRLB). Firstly, the computation model of the CRLB as the relation model between the radiated power and the tracking performance is built. Secondly, the power control method is presented according to the model, and the power which leads the closest result to the desired CRLB will be chosen as the radiated power. The tracking accuracy and Low Probability of Intercept (LPI) performance are demonstrated in the Monte Carlo simulations. The results are validated through the comparison with other methods.

In recent years, the cognitive radar (CR) with waveform diversity has exhibited significant performance improvements over the traditional fixed waveform radar and has become an area of vigorous research and development. A novel waveform selection strategy based on wind driven optimization (WDO) technique is proposed in this paper. Firstly, the predicted tracking Cramer-Rao Lower Bounds (CRLB) model is built based the Interacting Multiple Model Particle Filter (IMMPF) method. Then the waveform selection model between the waveform parameters and the tracking performance is introduced. Finally, the WDO technique is used to minimize the predicted CRLB for the waveform selection. The tracking accuracy is demonstrated in the Monte Carlo simulations. The results are validated through the comparison with other methods.

A new search algorithm for phased array radar which is based on radio frequency stealth is proposed in this paper. When there is no indication information, parameters including the dwelling time and the average power are optimized by sequential quadratic programming (SQP), and a minimum energy cost model is set up by using Grey Relational Grade (GRG). The surveillance area is divided into several subareas according to the elevation angle. The radar can radiate adaptively according to the different priorities. Compared with other search algorithms, the simulation results show that our algorithm can consume less energy and provide better performance in term of radio frequency stealth and detection.

In order to improve the radio frequency stealth ability of phased array radars, a novel resource scheduling method of the radar network for target tracking in clutter is presented. Firstly, the relationship model between radar resource and tracking accuracy is built, and the sampling interval, power, and waveform will influence predicted error covariance matrix through transition matrix and measurement noise. Then, radar resource scheduling algorithm based on Markov decision process which is converted to be a binary optimization problem is proposed, and an improved binary wind-driven optimization method is presented to solve that problem. The radar and its radiation parameters will be selected for better radio frequency stealth performance and tracking accuracy. Simulation results show that the proposed algorithm not only has excellent tracking accuracy in clutter but also has better radio frequency stealth ability comparing with other methods.