Yichuan Yang

Coherent Integration Algorithm for a Maneuvering Target With High-Order Range Migration

This paper considers the coherent integration problem for a maneuvering target with complex motions, where the velocity, acceleration, and jerk result in respectively the first-order range migration (FRM), second-order range migration (SRM), and third-order range migration (TRM) within the coherent pulse interval. A new coherent integration algorithm based on generalized keystone transform (KT) and second-order dechirp process is proposed, which employs the third-order KT, six-order KT, second-order KT, and fold factor searching to correct the TRM, SRM, and FRM, respectively. The range migration change during each step and computational complexity are also theoretically analyzed. Compared with the generalized Radon Fourier transform (GRFT) algorithm, the presented method can avoid the blind speed sidelobe (BSSL) and acquire close integration performance but with much lower computational cost. Simulations are provided to demonstrate the effectiveness. Finally, a generalized method, named generalized KT and generalized dechirp process (GKTGDP), is also introduced for the maneuvering target with arbitrary high-order range migration.

ISAR imaging of maneuvering target with complex motions based on ACCF-LVD

This paper considers the inverse synthetic aperture radar (ISAR) imaging problem for a maneuvering target with complex motions, involving range migration (RM) and Doppler frequency migration (DFM) within the coherent integration period of radar imaging, which will degrade the imaging quality. A nonsearching ISAR imaging algorithm based on adjacent cross correlation function (ACCF) and Lvs distribution (LVD), i.e., ACCF-LVD, is proposed, where the ACCF is applied to correct the RM and reduce the order of DFM. Then the motion parameters are estimated via LVD and Fourier transform. With the estimated motion parameters, high quality ISAR images can be achieved. The advantage of the presented method is that it can estimate the motion parameters under low signal-to-noise ratio (SNR) without searching procedures. Finally, several simulation examples are shown to confirm the validity of the proposed algorithm.

Fast Optimal Antenna Placement for Distributed MIMO Radar with Surveillance Performance

In this letter, we demonstrate an optimization problem of antenna placement of distributed multi-input multi- output (MIMO) radar. To evaluate the surveillance performance of the radar system, a coverage ratio is proposed as a criterion. Since the problem is of extremely huge computational complexity due to its complicated objective function and high dimensionality, we propose a solution that contains two parts: 1) a low- complexity method to simplify the objective function; 2) a placement algorithm based on particle swarm optimization (PSO) to deal with the challenge of high dimensionality. We also analyse the computational complexity of our solution. Simulation results verify the validity and advantage in computational complexity of our solution. Our contributions include a novel optimization placement model of distributed MIMO radar and a computational efficient solution to establish the optimal positions of antennas.

Antenna deployment method for multistatic radar under the situation of multiple regions for interference

Abstract In this paper, considering multiple regions for interference simultaneously, an optimal antenna deployment problem for distributed multistatic radar is investigated. The optimal antenna deployment problem is solved by proposing an antenna deployment method based on Multi-Objective Particle Swarm Optimization (MOPSO). Firstly, we construct a multi-objective optimization problem for multistatic radar antenna deployment by choosing the interference power densities of different regions as objective functions. Then, to obtain the optimal deployment result without wasting time and computational resources, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the MOPSO optimization process efficiently when the optimal antenna deployment algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.

Distributed MIMO radar detection with channel evaluation and selection strategy

We consider a moving target detection problem using distributed multiple-input multiple-out (MIMO) radar, where the Signal-to-Noise Ratios (SNRs) in each transmit-receive (T-R) channels are different. We propose two knowledge-based detectors based on the generalized likelihood ratio test (GLRT) rule, both of which require the knowledge of the SNRs relationship among T-R channels. Finally, we evaluate the performance of the derived detectors via computer simulations, and the results illustrate that they outperform conventional detection algorithm.

An efficient antenna placement method for MIMO radar under the situation of multiple interference regions

In this paper, under the situation of multiple interference regions, an optimal antenna placement problem for a distributed Multi-Input Multi-Output (MIMO) radar is studied. Considering multiple interference regions, we solve the antenna placement problem by utilizing antenna placement method based on Multi-Objective Particle Swarm Optimization (MOPSO). However, it is not clear when to stop the iteration for which no knowledge about the optimum result is available. Hence, computational resource may be wasted over iterations. Nevertheless, time and computational resource is limited in real application. Therefore, to obtain the optimal placement result with limited time and computational resource, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the optimization process efficiently when the optimal antenna placement algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.

Fast dynamic deployment of mobile sensor networks for the changing monitoring requirements

Nowadays, there have been a lot of applications such as industrial diagnostics, environmental monitoring, and battlefield surveillance in sensor networks. One of the most important performance metrics for sensor networks is coverage, because it reflects how well a surveillance region is monitored. In this paper, we demonstrate a maximum coverage deployment problem in multistatic radar system (MSRS) which is one kind of sensor networks. Given the changing monitoring requirements, we consider dynamically deploying MSRS. A coverage ratio is utilized as the objective function to build the optimization problem and evaluate the monitor performance. The key mechanism in this problem is to fast dynamic deploy the sensor networks to achieve maximum coverage. Since it is needed to jointly consider the positions of all sensors and the objective function is complicated, the optimization problem is of huge computational load, and thus we propose an algorithm based on genetic algorithm (GA) to deal with it. Numerical results verify the validity of the proposed algorithm and its superiority in computational complexity. Our contributions include a novel model of sensor networks dynamic deployment and an efficient solution to the proposed problem.

Preferential optimal antenna placement for MIMO radar under the situation of multiple surveillance regions

In this paper, a preferential optimization problem of antenna placement for a distributed Multi-Input Multi-Output (MIMO) radar is studied. Considering multiple surveillance regions, the radar performances of preferential regions need to be improved and more choices of placement schemes for desired performance need to be provided. Therefore, we use the Particle Swarm Optimiztion (PSO) with reference point to solve the antenna placement problems. By setting reference points, the performance values of preferential surveillance regions will converge to reference points and reach our requirement. Then, we analyse the performance of proposed algorithm. Finally, numerical results are provided to verify the validity of the proposed algorithm.

Performance prediction of noncoherent detector for independent and nonidentically distributed NCG fluctuating targets

We address the performance prediction of the noncoherent detector (i.e., squared-law plus integrator) for independent but possibly nonidentically distributed (non-id) noncentral Gamma (NCG) fluctuating target model, which is more general and can describe more complex targets. Initially, we obtain the probability density function (pdf) of the sum of independent and non-id NCG random variables in terms of an infinite sum of Gamma pdfs. Furthermore, we develop an analytic expression of the detection probability in terms of converging series based on the generalized Marcum Q function. Finally, at the analysis stage, we first study the truncation error of the derived expression and then evaluate the impacts on the detection performance of the target fluctuating parameters via numerical simulations.