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The problem of optimum sparse array configuration to maximize the beamformer output signal-to-interference plus noise ratio (MaxSINR) in the presence of multiple sources of interest (SOI) has been recently addressed in the literature. In this paper, we consider a shared aperture system where optimum sparse subarrays are allocated to individual SOIs and collectively span the entire full array receiver aperture. Each subarray may have its own antenna type and can comprise a different number of antennas. The optimum joint sparse subarray design for shared aperture based on maximizing the sum of the subarray beamformer SINRs is considered with and without SINR threshold constraints. We utilize Taylor series approximation and sequential convex programming techniques to render the initial nonconvex optimization a convex problem. The simulation results validate the shared aperture design solutions for MaxSINR for both cases where the number of sparse subarray antennas is predefined or left to constitute an optimization variable.

This paper focuses on the frequently occurring issue of automotive radar sensors: ghost detection. Three data-based approaches, namely random forest, convolutional neural network (CNN), and PointNet++, are adopted to identify ghost detection. Evaluated with the same dataset, random forest and PointNet++, with more than 95% accuracy, are evidently better than CNN in not only city but also motorway scenarios. Furthermore, the influence of various features for each classifier is also analyzed.

Automotive radar is a promising technology with regard to path planning, since radar systems offer a comparatively long range and are robust against bad weather conditions. In this paper, we use Convolutional Neural Networks (CNN) to determine the current road course from radar point clouds. For this purpose, we first transform the radar point cloud into various gridmaps, which then serve as an input for the CNN. The quality of the road course estimation is evaluated using a test dataset. Exemplary test results showed an average deviation of less than 91 cm at a range of 100 m between the ground truth and the estimated road course. These excellent results prove that CNN processing of radar measurements is an attractive option for reliable and precise road course estimation.

In this paper, we propose a technique for the joint tracking and labeling of multiple extended targets. To achieve multiple extended target tracking using this technique, models for the target measurement rate, kinematic component, and target extension are defined and jointly propagated in time under the generalized labeled multi-Bernoulli filter framework. In particular, we developed a Poisson mixture variational Bayesian model to simultaneously estimate the measurement rate of multiple extended targets and extended target extension was modeled using B-splines. We evaluated our proposed method with various performance metrics. Results demonstrate the effectiveness of our approach.

This paper addresses the problem of spectrum sensing in multi-antenna cognitive radio system using the support vector machine (SVM) algorithms. First, we formulated the spectrum sensing problem under multiple primary users scenarios as a multiple state signal detection problem. Next, we propose a novel beamformer-aided feature realization strategy for enhancing the capability of the SVM for signal classification under both single and multiple primary users conditions. Then, we investigate the error correcting output codes-based multi-class SVM algorithms and provide a multiple independent model alternative for solving the multiple state spectrum sensing problem. The performance of the proposed detectors is quantified in terms of probability of detection, probability of false alarm, receiver operating characteristics (ROC), area under ROC curves, and overall classification accuracy. Simulation results show that the proposed detectors are robust to both temporal and joint spatio-temporal detection of spectrum holes in cognitive radio networks.

We investigate a game-theoretic power allocation scheme and perform a Nash equilibrium analysis for a multistatic multiple-input multiple-output radar network. We consider a network of radars, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since there is no communication between the distributed clusters, we incorporate convex optimization methods and noncooperative game-theoretic techniques based on the estimate of the signal-to-interference-plus-noise ratio (SINR) to tackle the power adaptation problem. Therefore, each cluster egotistically determines its optimal power allocation in a distributed scheme. Furthermore, we prove that the best response function of each cluster regarding this generalized Nash game belongs to the framework of standard functions. The standard function property together with the proof of the existence of the solution for the game guarantees the uniqueness of the Nash equilibrium. The mathematical analysis based on Karush–Kuhn–Tucker conditions reveals some interesting results in terms of the number of active radars and the number of radars that over satisfy the desired SINRs. Finally, the simulation results confirm the convergence of the algorithm to the unique solution and demonstrate the distributed nature of the system.

A coalitional game is proposed for multicell multi-user downlink beamforming. Each base station intends to minimize its transmission power while aiming to attain a set of target signal-to-interference-plus-noise-ratio (SINR) for its users. To reduce power consumption, base stations have incentive to cooperate with other base stations to mitigate intercell interference. The coalitional game is introduced where base stations are allowed to forge partial cooperation rather than full cooperation. The partition form coalitional game is formulated with the consideration that beamformer design of a coalition depends on the coalition structure outside the considered coalition. We first formulate the beamformer design for a given coalition structure in which base stations in a coalition greedily minimize the total weighted transmit power without considering interference leakage to users in other coalitions. This can be considered as a non-cooperative game with each player as a distinct coalition. By introducing cost for cooperation, the coalition formation game is considered for the power minimization-based beamforming. A merge-regret-based sequential coalition formation algorithm has been developed that is capable of reaching a unique stable coalition structure. Finally, an ${\alpha }$ -Modification algorithm has been proposed to improve the performance of the coalition formation algorithm.

We consider sparse subarray design for multimission radars. The sparse array configuration significantly affects adaptive beamformer performance measured by the output signal-to-interference and noise ratio (SINR). In this paper, the full array is divided into two sparse subarrays, each performs a separate task. When antennas are not shared between the two subarrays due to differences in their properties and patterns, the design of two sparse subarray configurations to maximize the joint SINR must allocate each antenna position to one of the two tasks. We use Taylor series approximation to reformulate the underlying non-convex problem to a sequential convex programming (SCP) problem. The simulation results include examples of highly and weakly source spatial correlations, and they validate the efficiency of the proposed method.

We propose a Bayesian game-theoretic SINR maximization technique for a multistatic radar network. We consider a distributed network of radars, where the primary goal of each radar is to maximize their signal to interference plus noise ratio (SINR), within the constraint of its maximum transmission power. We assume no communication between the radars, hence we utilize a noncooperative game-theoretic approach. The channel gain between a radar and the target is assumed to be private information which characterizes the type of the player, whereas the distribution of the channel gain is common knowledge to every player in the game. Subsequently, we examine and prove the existence and the uniqueness of the Bayesian Nash equilibrium (BNE) for the aforementioned game. The simulation results also confirm the convergence of the algorithm to the unique solution.

We investigate a competitive power allocation problem for a MIMO radar system in the presence of multiple targets equipped with jammers. The main objective of the radar network is to minimize the total power emitted by the radars while achieving a specific detection criterion for each of the targets, while the intelligent jammers have the ability to observe the radar transmission power and consequently decide its jamming power to maximize the interference to the radars. In this context, we incorporate convex optimization methods, noncooperative game theoretic techniques and hypothesis testing to identify jammers and to determine the optimal power allocation. Furthermore, we present a proof on the existence and uniqueness of the Nash Equilibrium (NE). The simulation results confirm the effectiveness of the proposed algorithm and demonstrate the convergence of the game.

We consider the problem of joint transmit waveform optimization and receiver filter design in a signal-dependent clutter environment for a multistatic radar. We devise an optimization procedure that, by maximizing the Signal to Interference Noise Ratio (SINR) under certain constraints on the distortion of the transmitted signals, will iteratively optimize both the orthogonal codes and the receiver filter at the radar with centralized cognition abilities. The waveform design is based on the exploitation of initial radar codes characterized by desired auto and cross-correlation properties. Various performance analyzes demonstrate the effectiveness of the algorithm in providing a significant increase in SINR in a highly reverberating environment.

We investigate the design of joint transmitter and receiver beamformer within the context of multiple-input multiple-output (MIMO) radar employing two-dimensional (2D) arrays of antennas. Specifically, we derive the transmit, waveform diversity and overall transmit-receive beampatterns for the Phased-MIMO radar with fully-overlapped subarrays and compare them with the respective beampatterns for the Phased-array and MIMO radar only schemes. As reported for one-dimensional linear arrays, fully-overlapped 2D subarrays offer substantial improvements in performance as compared with the phased-array and MIMO only radar models. The work considers both the adaptive (convex optimization, CAPON beamformer) and non-adaptive (conventional) beamforming techniques. The simulation results demonstrate the superiority of the fully-overlapped subaperturing in both cases.

We investigate a subaperturing technique for two-dimensional (2D) transmit arrays within the context of multiple-input multiple-output (MIMO) radar. Specifically, we investigate the performance of transmit beamforming using fully overlapped subarrays of a 2D transmit array. As reported for linear array of antennas, this 2D transmit array exploits the advantages of the MIMO radar technology without sacrificing the coherent processing gain at the transmit side provided by the phased-array concept. In order to achieve high coherent processing gain, a weight vector should be designed for each subarray to steer the transmit beam in certain 2D sector in space. This is achieved by solving a convex optimization problem that minimizes the difference between a desired transmit beampattern and the actual beampattern produced by the 2D array of antennas, under a constraint in terms of uniform power allocation across the transmit antennas.

The posterior retroperitoneoscopic adrenalectomy is less popular than the laparoscopic transabdominal method. Due to the direct approach to the adrenal glands, however, the posterior retroperitoneal access is easy to use and may offer advantages not available with other endoscopic procedures for adrenalectomy.Between July 1994 and March 2006, we performed 560 adrenalectomies (right side: n = 258; left side: n = 302) by the posterior retroperitoneoscopic approach in 520 patients (200 male, 320 female; age, 10 to 83 years). Of the 520 patients, 21 suffered from Cushing's disease, 499 patients had adrenal tumors (157 Conn's adenomas, 120 pheochromocytomas [13 bilateral], 110 Cushing's adenomas [6 bilateral], and 112 other tumors). Tumor size ranged from 0.5 to 10 cm (mean, 2.9 +/- 1.7 cm). The procedures were performed with the patients in the prone position usually with 3 trocars.Mortality was zero. Conversions to open or laparoscopic lateral surgery were necessary in 9 patients (1.7%). Major complications occurred in 1.3% of patients, minor complications in 14.4%. Mean operating time was 67 +/- 40 min and declined significantly (P.001) from the early procedures (106 +/- 46 min) to the later operations (40 +/- 15 min).The posterior retroperitoneoscopic adrenalectomy is a safe and fast procedure. In experienced hands, this method represents the ideal approach in adrenal surgery.