Tianxian Zhang

Adaptive Bayesian Detection Using MIMO Radar in Spatially Heterogeneous Clutter

This letter considers adaptive target detection problem using multiple-input multiple-output (MIMO) radar in the presence of spatially heterogeneous clutter. The covariances of the primary data and secondary data for the same and different transmit-receive pairs are modeled as different random matrices with partial priori knowledge of the environment. Two-step strategy is employed to design adaptive detector. Specifically, we first obtain the generalized likelihood ratio test (GLRT) detector by assuming the known matrices. Then, we derive the maximum posteriori (MAP) estimator of the covariance matrices by exploiting the priori information, and replace the given covariance matrices in the obtained GLRT with MAP estimates. Finally, we evaluate the proposed adaptive detector via numerical simulations.

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.

Phase-Modulated Waveform Evaluation and Selection Strategy in Compound-Gaussian Clutter

This correspondence introduces a waveform evaluation and selection strategy (ESS) for application of optimized phase-modulated (PM) waveforms to mitigate compound-Gaussian clutter in radar. This strategy addresses the trade-off between potential improvement in detection performance from adaptive design of PM waveforms and the cost this entails in terms of system resources. An evaluation factor is introduced to quantify the performance improvement obtained by design and transmission of an optimized PM waveform in the second sub-dwell of a two-stage clutter mitigation scheme versus using linear frequency modulated (LFM) waveform in a sub-dwell. This evaluation factor provides the basis for the proposed ESS, the efficacy of which is demonstrated in simulations.

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.

MIMO radar moving target detection in compound-Gaussian clutter

This paper considers moving target detection for multiple-input multiple-output (MIMO) radar in compound-Gaussian clutter. A new detector is devised according to a centralized processing scheme for MIMO radar system based on the generalized likelihood ratio test (GLRT) design criterion. Then, an adaptive version of the derived detector is investigated. The fixed point estimation (FPE) strategy is introduced to make the proposed detector fully adaptive. Finally, several numerical simulations of the derived detectors with typical parameters are obtained and discussed.

Constrained transmit beampattern design for colocated MIMO radar

Abstract This paper considers the constrained waveform design for Multiple-Input Multiple-Output (MIMO) radar to synthesize a desired beampattern. Specifically, resorting to SemiDefinite Programming (SDP) related technique, we first minimize Integration Sidelobe Level (ISL) to optimize the waveform covariance matrix enforcing a uniform elemental power restriction as well as a 3dB bandwidth constraint. Then, based on Least Square (LS) approach, we present the existing Cyclic Algorithm (CA) and a new Sequential Iterative Algorithm (SIA) to devise the waveform under a constant modulus constraint, and a similarity constraint to allow the designed waveform sharing the similarity feature with a given reference waveform. In particular, the proposed SIA directly optimizes the objective function and its each iteration turns the multidimensional optimization problem into multiple one-dimensional optimization problems with closed-form solutions. Finally, we assess the effectiveness of the proposed technique through numerical simulations in comparison with CA.

Ambiguity Function design via Accelerated Iterative Sequential Optimization

This paper focuses on the Ambiguity Function design for the unimodular sequence, which is of great interest in radar and communication systems. An Accelerated Iterative Sequential Optimization (AISO) algorithm is proposed to minimize the average value of the weighted integrated side-lobe level (WISL) over specific Doppler bins and range bins of interest. At the analysis stage, we evaluate the effectiveness of the proposed algorithm in terms of the achieved WISL and computation time with respect to the gradient method using numerical simulations.

Constrained MIMO radar waveform design for beampattern formation

This paper considers the constrained waveform design of Multiple-Input Multiple-Output (MIMO) radar to synthesize a desired beampattern. Specifically, resorting to SemiDefinite Programming (SDP) related technique, we first minimize Integrate Sidelobe Level (ISL) to optimize the waveform covariance matrix forcing a uniform elemental power requirement as well as a 3dB bandwidth constraint. Then, based on Least Square (LS) approach, we devise the waveform accounting for constant modulus and similarity constraints by using cyclic algorithm (CA). Finally, we assess the effectiveness of the proposed technique through numerical simulations.

Knowledge-aided Bayesian GLRT design of MIMO radar in heterogeneous clutter

This work addresses the problem of adaptive multiple-input multiple-output (MIMO) radar detection in heterogeneous clutter. We first derive the generalized likelihood ratio test (GLRT) based on the two-step design procedure. Then, considering with the Bayesian framework and the prior knowledge about the clutter, we adopt the Maximum A Posteriori (MAP) estimator of the clutter covariance matrix and extend the knowledge-aided Bayesian technique to MIMO radar detection. Finally, various simulation results and comparison with respect to other conventional technique are presented to demonstrate the effectiveness of the knowledge-aided Bayesian technique, especially in presence of a small amount of secondary data.

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.