A Novel NLOS Target Localization Method with a Synthetic Bistatic MMW Radar

Millimeter Wave (MMW) radars are currently used as range measuring devices in applications such as automotive driving aids (Langer and Jochem, 1996), (Rohling and Mende, 1996), the mapping of mines (Brooker et al., 2005) and autonomous field robotics (Brooker, 2001), (Langer, 1996). This recent interest is largely due to the advantages MMW radars offer over other range measuring sensors, as their performance is less affected by dust, fog, rain or snow and ambient lighting conditions. MMW radars can provide received signal strength values, at all discrete range intervals, within the working range of the radar (Clark and Durrant-Whyte, 1997), (Scheding et al., 2002). The received power versus range spectra hence contain useful range to target information, but are also corrupted by noise. User defined stochastic algorithms can then be implemented, which exploit this rich data to improve object detection and mapping performance. This is in contrast to many other range measuring devices which typically internally threshold received signals, to provide single hard decisions only, on the estimated range to objects (Mullane, 2007). This paper addresses the issues of predicting the power-range spectra from MMW radars which use the Frequency Modulated Continuous Wave (FMCW) range estimation technique. This is important for two reasons. First, in automotive and autonomous robotic applications, such sensors are used in conjunction with vehicle navigation and map state estimation filters. This is so that (typically uncertain) vehicle motion knowledge can be optimally fused with the noisy sensor information, to infer estimates of the state of interest (typically, the vehicle's pose (position and orientation) and/or information of the surrounding object locations). Hence, it is essential that predicted power versus range spectra can be computed, to apply a Bayesian recursive estimation framework, based on previous measurements, and uncertain vehicle motion information. Second, it is extremely useful to be able to simulate MMW radar data, given certain environmental configurations. This aids the development of reliable object detection algorithms, based on theoretical sensor and noise models, which can then be applied more effectively to real MMW radar data.

Environmental perception is a key technology for Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles, and thus a crucial prerequisite for traffic situation assessment and reasonable decision making. Sensor fusion can make full use of the characteristics of multi-sensors to achieve complementary advantages to improve the target recognition precision in various weather conditions. In this paper, a robust target detection algorithm based on millimeter wave (MMW) radar and camera fusion is proposed. Firstly, the images captured by the camera with low visibility in foggy weather are defogged. Then, the effective target is filtered by MMW radar, and mapped to the camera image to obtain the corresponding regions of interest (ROIs). Finally, the method of weighted is used to combine camera vision network recognition results with the radar target estimation results, and obtain the final result of the ROIs. The simulation results indicates that the detection precision through fusion of MMW radar and camera significantly outperforms that of single sensor.

This paper deals with the problem that how to use the distance information of millimeter wave (MMW) radar in the target-tracking of forward looking infrared (FLIR) image sequences. Many conventional image based trackers use timing updating to update template, but how to confirm an appropriate updating period is a difficult problem. However, the presence of noise and image magnification as the camera approaches the target may cause a tracked template to drift. In a MMW/FLIR dual-mode seeking guidance system, MMW radar can provide the precise distance between the missile and the target. The distance information can be used to direct how to update template. Firstly, a distance updating strategy is confirmed after image blowup's influence on pertinence is analyzed. Secondly, magnified template is used to direct template updating after images are magnified by distance information. Lastly, an adaptive template updating algorithm using distance information is presented. Results acquired for real FLIR images obtained from an aircraft are presented. Experiments results show that the higher tracking accuracy and the stronger stability are obtained in the algorithm proposed than those in other methods.

This paper summarizes a research effort to explore the use of neural networks for fusing information from multiple sensors in an automatic target recognition (ATR) scenario. Data from two sensors, forward-looking infrared (FLIR) and millimeter wave (MMW) radar, were collected on three ground targets: tank, truck, and armored personnel carrier (APC). Two different types of classifiers were developed and compared: a conventional statistical quadratic and a neural network consisting of a multilayer perceptron with a backpropagation of error training algorithm. Single sensor classifiers were developed for both FLIR and MMW radar data. In addition, multisensor classifiers were developed using merged FLIR and MMW radar features. The experimental results indicate that a backpropagation neural network (BPN) outperformed the conventional quadratic classifier. Neural networks may provide better solutions to the varying clutter problem found in ATR environments because neural networks can describe both linear and nonlinear boundaries required for complex decision surfaces.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Millimeter wave (MMW) radar systems are now demonstrating the ability to image airport environments in fog. The authors discuss the use of MMW radar technology to provide pilots with enhanced vision and enhanced situation awareness. One of the first steps in providing an autonomous aircraft is the Enhanced Vision System (EVS). This system consists of a heads-up display (HUD) and an imaging MMW radar that provides the pilot with runway imagery on the HUD. Hughes Aircraft Co. has developed a MMW radar designed specifically for the EVS application. This radar was designed to provide the range, resolution, accuracy, update rate, and safety monitoring functions required to meet the demanding Category III FAA certification requirements. The Hughes MMW radar comprises three subsystems: the processor, transceiver and antenna. The radar is currently undergoing testing. To test the radar's ability to compensate for aircraft motion and provide real-time imagery, a dynamic platform was used to simulate aircraft motion. >

One of the main advantages of millimeter wave (MMW) imaging radar systems result from the fact that their imaging performance does nearly not depend on atmospheric affects such as fog, rain and snow. That is the reason that MMW radar seems to be one of the most promising sensors for enhanced vision systems (EVS), which can aid the pilot during approach, landing and taxiing, especially under bad weather conditions. Compared to other imaging devices, MMW radar system deliver a lower image resolution and update rate, and have a worse signal to noise ratio. Moreover, the commonly proposed method of the perspective view projection in EVS applications results in some imaging errors and artifacts. These sensor specific effects should be taken into account during the presently conducted EVS research and development. To get the opportunity of studying imaging radar systems in ground based research environments, we have developed a new type of a MMW radar sensor simulator. Our approach is based on detailed terrain and/or airport databases, as they are available for normal visual simulations or VR applications. We have augmented these databases with some specific attributes which describe object surface properties with respect to MMW. Our approach benefits from the state of the art of high speed computer graphics hard- and software. It is implemented in C/C++ and uses the Open GL graphic standard and the SGI Performer database handler. It runs on every SGI graphic workstation, and achieves an image update rate of about 20 Hz, which is more than actual available radar systems deliver. One of the main advantages of our approach is, that it can be integrated easily in emergent multisensor based enhanced vision systems and it is a useful tool for EVS research and development.

Millimeter-wave (mmWave) radar offers high-resolution sensing capabilities, enabling accurate detection of closely spaced targets along with the estimation of parameters such as position, range, velocity, and angle. This work focuses on Direction of Arrival (DoA) estimation using real-time data collected from the Infineon BGT60TR13C mmWave radar sensor. Prominent DoA estimation algorithms, including Conventional Beamforming (CBF), Multiple Signal Classification (MUSIC), and Sparse Parametric Iterative Covariance-based Estimation (SPICE), were implemented and evaluated using the collected data. The performance of these algorithms was assessed based on metrics such as Mean Square Error (MSE) and output Signal-to-Noise Ratio (SNR). Experimental results demonstrate that the SPICE algorithm outperforms traditional methods in terms of both accuracy and robustness, highlighting its effectiveness for high-resolution DoA estimation in practical mmWave radar systems.

This paper presents a framework for detecting the obstacles from a rotor-wing UAV by fusing the information from millimeter-wave radar and monocular camera. In order to implement the angle measurement function of ranging radar, we designed a dual radar network system to locate the target position, and through calibration we achieved correspondence between target orientation and image area. In order to deal with the complex movements of UAV (such as yaw and pitch), this paper proposes a robust algorithm, which can calculate the accurate height of target position points in the image as well as the bottom boundary of the target area in the image, thus avoiding over segmentation of the target. Finally, the target is segmented from the background of the partition after fusion through the threshold algorithm according to the mutational gray value of the bottom boundary of the target position. Through extensive experiments, the effectiveness and efficiency of proposed algorithm have been proved and the experimental results show that the proposed method is novel, simple and feasible.

Plant protection UAVs need to be kept at a certain altitude to ensure the safety of UAV operations when performing imitation ground flight operations. Aiming at the problems of limited measuring range, weak anti-jamming ability, and high cost of UAV altimetry sensors, a UAV altimetry method based on millimeter-wave radar is proposed, and the spectrum is refined using the Zoom-FFT algorithm. The experimental results show that the millimeter wave radar can maintain stable measurement at different altitudes and in different environments, and the ranging accuracy of the Zoom-FFT algorithm is increased by 1.17% during the day and 1.31% in the night compared with the FFT algorithm.

Millimeter-wave radar is widely used in intelligent vehicle infrastructure cooperative systems (IVICS) because of its long detection distance, high integration and less affected by weather conditions. The target recognition and classification technology based on millimeter wave radar is a research hotspot. However, traditional recognition methods require a complex feature extraction process. In this paper, a millimeter-wave radar classification method based on deep learning is proposed, which uses the ability of convolutional neural networks (CNN) method to automatically extract feature data, so as to replace most of the complex processes of traditional radar signal processing chain. It solves the four classification tasks of bus, cars, electromobile and pedestrians. Taking the selected feature data as the RV spectrum, a seven layer convolution neural network is built to classify the target. Compared with traditional machine learning methods, CNN greatly reduces the workload of feature engineering, and its classification effect is better than traditional machine learning methods. Then, from the perspective of redundancy of the convolution kernels, we simplify the CNN architecture. As a result, final classification accuracy of the CNN is 93.16%. After simplifying the structure of the CNN, the computational complexity and storage consumption of the network are reduced by 74.67% and 73.71%, respectively, while ensuring that the classification accuracy does not decline.

<p>Improving our understanding of the processes governing mass loss from the cryosphere is inhibited by a lack of data at high spatial and temporal resolution. Satellite sensors can provide regional to global scale coverage of glacier processes but fail to resolve processes that occur rapidly, for example glacier calving. To observe these processes, the glaciology community must invest in new techniques that can monitor these processes adequately and fill this major research gap. Here, we will discuss the implementation of an exciting new radar system that is capable of imaging glacial terrain at a high angular resolution and during most weather conditions. The system, named AVTIS2, operates at 94 GHz (~3 mm) and offers a compromise between imaging resolution and penetration through atmospheric obscurants. AVTIS2 scans mechanically across a scene of interest in defined increments of azimuth and elevation angles and generates a 3D data cube of backscattered power. We use a point to maximum power criterion to generate point clouds and construct Digital Elevation Models (DEMs) of the terrain. Because AVTIS2 is a real aperture radar it does not require the phase stability of interferometric radars and can acquire DEMs irrespective of local environmental conditions. In this work, we have used the AVTIS2 radar to map Rhône Glacier in the Swiss Alps, representing the first ever time a millimetre wave radar has been used in this way. To improve our understanding of the performance of AVTIS2 for mapping glaciers, we have characterised the scattering properties of glacial ice at 94 GHz by calculating its Radar Cross Section (RCS). This is key to understanding the performance of AVTIS2 for mapping glaciers. This study represents the first investigation into the reflectivity of ice at millimetre wavelengths and the utility of millimetre wave radar as a surveying tool. We will report on the future application of this instrument in glaciological studies and the unique perspective it can offer.</p>

An improved algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation in bistatic MIMO radar based on fourth order cumulants is presented. Firstly, the data of receiver is reset and divided to acquire the rotational invariance property of transmitter and receiver, respectively. The fourth order cumulants matrixes in twain are constructed which are based on the basic definition of the cumulant. Then we use the propagator method (PM), which only requires linear operation but does not involve any eigendecomposition of the cumulant matrix, to estimate the DODs and DOAs, respectively. Finally, the maximum likelihood method is used to solve the pairing problem. The proposed method is effective in prohibiting the Gaussian colored noise and improves the performance of the angle estimation slightly. It does not need two-dimensional spectrum peak searching and eigenvalue decomposition on the cumulant matrix, thus the computation complexity is reduced. At the same time, it has no exceptive claim on the number of receive arrays or receive arrays. Simulation results verify the effectiveness and feasibility of the proposed method.

In bistatic multiple input multiple output (MIMO) radar, more number of detectable incident signals and a higher angle estimation performance can be obtained by using conjugate estimation of signal parameters via rotational invariance techniques (ESPRITs) with the characteristic of noncircularity. The result is achieved under the assumption that all the received signals are noncircular. When the incident signals are the coexistence of noncircular and circular signals, the conjugate ESPRIT will not valuable. Therefore, this paper proposes a method of the joint of direction of departure (DOD) and direction of arrival (DOA) estimation, which is appropriate for the coexistence of noncircular and circular signals in bistatic MIMO radar. First, the received data model of the bistatic MIMO radar is given. Second, we modify the received signal model by the use of noncircularity characteristic. Third, we derive out the equation of spatially rotational invariant containing the DOD and DOA information. Last, we solve the equation to obtain the DOD and DOA by means of total least squares technology. The proposed algorithm has the three advantages. One is that it has better angle estimation accuracy than that of method which does not use the noncircularity characteristic. Another one is that it has more number of detectable incident signals. The last one is that the more number of noncircular signals, the higher angle estimation accuracy will be. Results from numerical experiments are used to show the effectiveness of our proposed algorithm.

As device technology improves, interest in the millimeter-wave band grows. Wireless communication systems migrate to higher frequencies, millimeter-wave radars and passive sensors find new solid-state implementations that promise improved performance, and entirely new applications in the millimeter-wave band become feasible. The circuit or system designer is faced with a new and unique set of challenges and constraints to deal with in order to use this portion of the spectrum successfully. In particular, the advantages of monolithic integration become increasingly important. This thesis presents many new developments in Monolithic Millimeter-Wave Integrated Circuits (MMICs), both the chips themselves and systems that use them. It begins with an overview of the various applications of millimeter waves, including a discussion of specific projects that the author is involved in and why many of them demand a MMIC implementation. In the subsequent chapters, new MMIC chips are described in detail, as is the role they play in real-world projects. Multi-chip modules are also presented with specific attention given to the practical details of MMIC packaging and multi-chip integration. The thesis concludes with a summary of the works presented thus far and their overall impact on the field of millimeter-wave engineering.

In this paper, we investigate the problem of joint direction of departure (DoD) and direction of arrival (DoA) estimation with bistatic MIMO radar. Specifically, a weaved co-prime array (WCA) bistatic MIMO (WCA-bMIMO) radar is proposed to produce a hole-free sum-difference co-array, which enables to offer significantly increased degrees of freedom (DoFs) and enlarged array aperture. Moreover, the two-dimensional spatial smoothing method is introduced to construct a smoothed received signal, which helps to tackle the rank deduction problem rooted in the equivalent one-snapshot received signal from the sum-difference co-array. Subsequently, the joint DoD and DoA estimates can be obtained by extracting the three-way tensor model. Furthermore, co-array Cramer-Rao Bound (CRB) for the bistatic co-prime MIMO radar is provided. Finally, the superiorities of the proposed bistatic co-prime MIMO radar in accuracy and resolution are proved by numerical simulation results.