Le Zheng

Moving Target Detection Using Colocated MIMO Radar on Multiple Distributed Moving Platforms

In this paper, we consider the problem of moving target detection, and a novel radar system with multiple moving platforms is proposed. Each moving platform is equipped with multiple colocated antennas and serves as a transmitter or a receiver. Thus, this system possesses the advantages of both distributed and colocated multiple-input multiple-output radars. To exploit the clutter sparsity in the surveillance area, a novel compressed sensing (CS)-based model is proposed. Since the clutter cannot exactly reside on discretized grids often employed by most CS approaches, a novel two-step algorithm which extends the orthogonal matching pursuit is proposed to reconstruct the off-grid clutter. Then, a fusion center combines all received signals by using a generalized likelihood ratio test to detect the moving target. To further improve the detection performance, a novel online waveform optimization algorithm is developed to maximize the signal-to-clutter-and-noise ratios of each transmitter platform. Extensive simulation results are provided to demonstrate the effectiveness of the proposed radar system and algorithms.

A Cooperative SWIPT Scheme for Wirelessly Powered Sensor Networks

Wireless power transfer (WPT) provides a novel solution to the painstaking power-charging issue in wireless sensor networks. However, due to the propagation loss, the fast attenuation in energy transfer efficiency over the transmission distance is the main impediment to the WPT application. In this paper, we apply the simultaneous wireless information and power transfer (SWIPT) to a wirelessly powered sensor network, where each node has two circuits, which operate on energy harvesting mode and information decoding mode separately. We propose a novel cooperative SWIPT scheme (CSS) for this system. First, we present a conflict-free schedule initialization algorithm for CSS. For a given conflict-free schedule, we formulate a resource allocation problem to maximize the network energy efficiency, which is then transformed to an equivalent convex optimization problem and resolved via dual decomposition. Finally, a heuristic algorithm is presented to achieve the transmission schedule with the maximum energy efficiency and the corresponding resource assignment policy. Simulation results indicate that the CSS can significantly improve the energy efficiency of the wirelessly powered sensor network.

High-Accuracy Compressed Sensing Decoder Based on Adaptive

Compressed sensing (CS) allows a signal that is sparse in certain domain to be acquired and reconstructed accurately with only a small number of samples. In this paper, we propose an adaptive (ℓ0, ℓ1) complex approximate message passing (CAMP) algorithm and its hardware implementation for complex-valued sparse signal recovery. Compared with the existing CAMP algorithm which solves ℓ1-regularized least squares problems, our proposed algorithm adaptively switches between ℓ0 and ℓ1-regularized least squares and therefore significantly outperforms the original CAMP. We implement the architecture in a medium-sized field-programmable gate array (FPGA) chip. For a sparse stepped frequency waveform radar application, we perform experiments on the simulated data and the data collected by a real radar system. According to the result, the decoder design achieves 7.2 dB improvement over the conventional CAMP architecture with less than 27.4% extra hardware cost.

(\ell_{0},\ell_{1})

Compressed sensing exploits the sparsity of the signal to reduce the sampling rate while keeping the resolution fixed, and has been widely used. In this paper we propose a new algorithm called adaptive p-CAMP and show its application in the sparse stepped frequency radar signal processing. Our algorithm is inspired by the complex approximate message passing algorithm (CAMP) that solves complex-valued LASSO. The following properties of the proposed algorithm make it superior to existing algorithms: (1) All the parameters of the algorithm are tuned dynamically and optimally. The algorithm does not require any information about the signal and is still capable of tuning the parameters as well as an oracle that has all the signal information. (2) Adaptive p-CAMP is designed to solve the complex-valued p-regularized least squares for 0p1. Hence, it can outperform CAMP. The performance of the proposed algorithm is verified by simulations and the data collected by a real radar system. HighlightsA new compressed sensing algorithm called adaptive lp-CAMP is proposed.The proposed algorithm can be applied to the sparse stepped frequency radar signal processing.The performance of the proposed algorithm is verified by simulations and real data.

Complex Approximate Message Passing: Cross-layer Design

The focus of this paper is on coexistence between a communication system and a pulsed radar sharing the same bandwidth. Based on the fact that the interference generated by the radar onto the communication receiver is intermittent and depends on the density of scattering objects (such as, e.g., targets), we first show that the communication system is equivalent to a set of independent parallel channels, whereby precoding on each channel can be introduced as a new degree of freedom. We introduce a new figure of merit, named the compound rate, which is a convex combination of rates with and without interference, to be optimized under constraints concerning the signal-to-interference-plus-noise ratio (including signal-dependent interference due to clutter) experienced by the radar and obviously the powers emitted by the two systems: the degrees of freedom are the radar waveform and the aforementioned encoding matrix for the communication symbols. We provide closed-form solutions for the optimum transmit policies for both systems under two basic models for the scattering produced by the radar onto the communication receiver, and account for possible correlation of the signal-independent fraction of the interference impinging on the radar. We also discuss the region of the achievable communication rates with and without interference. A thorough performance assessment shows the potentials and the limitations of the proposed co-existing architecture.

p-Based complex approximate message passing with application to sparse stepped frequency radar

Multicast beamforming is a key technology for next-generation wireless cellular networks to support high-rate content distribution services. In this letter, the coordinated downlink multicast beamforming design in multicell networks is considered. The goal is to maximize the minimum signal-to-interference-plus-noise ratio of all users under individual base station power constraints. We exploit the fractional form of the objective function and geometric properties of the constraints to reformulate the problem as a parametric manifold optimization program. Afterwards we propose a low-complexity Dinkelbach-type algorithm combined with adaptive exponential smoothing and Riemannian conjugate gradient iteration, which is guaranteed to converge. Numerical experiments show that the proposed algorithm outperforms the existing SDP-based method and DC-programming-based method and achieves near-optimal performance.

Joint Design of Overlaid Communication Systems and Pulsed Radars

Most existing approaches to coexisting communication/radar systems assume that the radar and communication systems are coordinated, i.e., they share information, such as relative position, transmitted waveforms, and channel state. In this paper, we consider an uncoordinated scenario where a communication receiver is to operate in the presence of a number of radars, of which only a subset may be active, which poses the problem of estimating the active waveforms and the relevant parameters thereof, so as to cancel them prior to demodulation. Two algorithms are proposed for such a joint waveform estimation/data demodulation problem, both exploiting sparsity of a proper representation of the interference and of the vector containing the errors of the data block, so as to implement an iterative joint interference removal/data demodulation process. The former algorithm is based on classical on-grid compressed sensing, whereas the latter forces an atomic norm (AN) constraint: In both cases the radar parameters and the communication demodulation errors can be estimated by solving a convex problem. We also propose a way to improve the efficiency of the AN-based algorithm. The performance of these algorithms are demonstrated through extensive simulations, taking into account a variety of conditions concerning both the interferers and the respective channel states.

Coordinated Multicell Multicast Beamforming Based on Manifold Optimization

Radar scientists have recently explored the application of compressed sensing for generating high resolution range profiles (HRRPs) from a limited number of measurements. The last decade has witnessed a surge of algorithms for this purpose. Among these algorithms complex-valued approximate message passing (CAMP) has attracted attention for the following reasons: (i) it converges very fast, (ii) its mean-squared-error can be accurately predicted theoretically at every iteration, (iii) it is straightforward to control the false alarm rate and optimize for the best probability of detection. Despite its nice features, the recovery performance of CAMP is similar to ℓ1-minimization and hence is expected to be improved. The goal of this paper is to first show how the algorithm can be extended to solve non-convex optimization problems. Based on our framework we develop a new algorithm called adaptive ℓp-CAMP that not only has all the nice properties of CAMP, but also provably outperforms it. We explore the performance of our algorithm on a real radar data and show that our new algorithm generates SNRs that are up to 6dB better than those of the other existing algorithms including the original CAMP.

Adaptive Interference Removal for Un-coordinated Radar/Communication Co-existence

The monopulse angle measuring technique is widely adopted in radar systems due to its simplicity and speed in accurately acquiring a target’s angle. However, in a spatial adaptive array, beam distortion, due to adaptive beamforming, can result in serious deterioration of monopulse performance. In this paper, a novel constrained monopulse angle measuring algorithm is proposed for spatial adaptive arrays. This algorithm maintains the ability to suppress the unwanted signals without suffering from beam distortion. Compared with conventional adaptive monopulse methods, the proposed algorithm adopts a new form of constraint in forming the difference beam with the merit that it is more robust in most practical situations. At the same time, it also exhibits the simplicity of one-dimension monopulse, helping to make this algorithm even more appealing to use in adaptive planar arrays. The theoretical mean and variance of the proposed monopulse estimator is derived for theoretical analysis. Mathematical simulations are formulated to demonstrate the effectiveness and advantages of the proposed algorithm. Both theoretical analysis and simulation results show that the proposed algorithm can outperform the conventional adaptive monopulse methods in the presence of severe interference near the mainlobe.

An lp-based reconstruction algorithm for compressed sensing radar imaging

The monopulse angle measuring technique is widely adopted in radar systems due to its simplicity and speed in accurately acquiring a target angle. However, in a spatial adaptive array, the beam distortion incurred by adaptive beamforming, can result in severe performance deterioration. A novel constrained monopulse angle measuring algorithm is proposed in this paper for spatial adaptive arrays. This algorithm is able to suppress the interference without suffering from beam distortion. Compared with previous constrained algorithms, the proposed algorithm adopts a new form of constraint in forming the difference beam with the merit that it is more robust in most practical situations. At the same time it also exhibits the simplicity of traditional monopulse, helping to make this algorithm even more appealing to use in adaptive arrays. In addition, the theoretical mean and variance of the monopulse estimator is derived for theoretical analysis. Mathematical simulations are formulated to demonstrate the effectiveness and advantages of the proposed algorithm.