We consider the problem of precise range estimation between pairs of moving vehicles using periodic broadcast messages. The transmitter and receivers are not time synchronized and one needs to explicitly account for the clock offset and clock drift in addition to the vehicle motion to obtain accurate range estimates. We develop a range estimation algorithm based on a local polynomial smoothing of the vehicle motion and experimentally verify that the performance is close to that obtained using unicast round-trip time ranging. This broadcast approach is of interest particularly in the context of dedicated short-range communications (DSRC) wherein periodic broadcast safety messages are exchanged between vehicles. We propose to exploit these broadcast messages to perform ranging. Our scheme requires additional timestamp information to be transmitted as part of the DSRC messages, and we develop a novel timestamp compression algorithm to minimize the resulting overhead. We validate our proposed algorithm on experimental data and show that it is able to achieve sub-meter ranging accuracies in vehicular scenarios.
Range estimation has been a topic of research interest for several decades starting with classical RADAR systems @cite_21 and GPS @cite_12 to more recent technologies like WiFi @cite_11 , Bluetooth @cite_7 , and ultra-wide band @cite_1 to name a few. There is still active research in the community to develop inexpensive and precise ranging technologies that can work in various environments. These are enabling applications in the vehicular space @cite_17 @cite_16 @cite_4 @cite_19 , sensor networks @cite_13 @cite_15 @cite_20 , and robotics @cite_3 @cite_24 .
@cite_4 @cite_7 @cite_21 @cite_1 @cite_17 @cite_20 @cite_3 @cite_24 @cite_19 @cite_15 @cite_16 @cite_13 @cite_12 @cite_11
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In this paper, we investigate a trade-off between the number of radar observations (or measurements) and their resolution in the context of radar range estimation. To this end, we introduce a novel estimation scheme that can deal with strongly quantized received signals, going as low as 1-bit per signal sample. We leverage for this a dithered quantized compressive sensing framework that can be applied to classic radar processing and hardware. This allows us to remove ambiguous scenarios prohibiting correct range estimation from (undithered) quantized base-band radar signal. Two range estimation algorithms are studied: Projected Back Projection (PBP) and Quantized Iterative Hard Thresholding (QIHT). The effectiveness of the reconstruction methods combined with the dithering strategy is shown through Monte Carlo simulations. Furthermore we show that: (i), in dithered quantization, the accuracy of target range estimation improves when the bit-rate (i.e., the total number of measured bits) increases, whereas the accuracy of other undithered schemes saturate in this case; and (ii), for fixed, low bit-rate scenarios, severely quantized dithered schemes exhibit better performances than their full resolution counterparts. These observations are confirmed using real measurements obtained in a controlled environment, demonstrating the feasibility of the method in real ranging applications. Millimeter-wave (mmWave) radar is widely used in vehicles for applications such as adaptive cruise control and collision avoidance. In this paper, we propose an IEEE 802.11ad-based radar for long-range radar (LRR) applications at the 60 GHz unlicensed band. We exploit the preamble of a single-carrier physical layer frame, which consists of Golay complementary sequences with good correlation properties that make it suitable for radar. This system enables a joint waveform for automotive radar and a potential mmWave vehicular communication system based on the mmWave consumer wireless local area network standard, allowing hardware reuse. To formulate an integrated framework of vehicle-to-vehicle communication and LRR, we make typical assumptions for LRR applications, incorporating the full duplex radar operation. This new feature is motivated by the recent development of systems with sufficient isolation and self-interference cancellation. We develop single- and multi-frame radar receiver algorithms for target detection as well as range and velocity estimation for both single- and multi-target scenarios. Our proposed radar processing algorithms leverage channel estimation and time–frequency synchronization techniques used in a conventional IEEE 802.11ad receiver with minimal modifications. Analysis and simulations show that in a single-target scenario, a gigabits-per-second data rate is achieved simultaneously with cm-level range accuracy and cm s-level velocity accuracy. The target vehicle is detected with a high probability (above 99.99 @math ) at a low false alarm rate of 10 @math for an equivalent isotropically radiated power of 40 dBm up to a vehicle separation distance of about 200 m. The proposed IEEE 802.11ad-based radar meets the minimum accuracy resolution requirement of range and velocity estimates for LRR applications. We demonstrate that it is possible to achieve accurate localization and tracking of a target in a randomly placed wireless sensor network composed of inexpensive components of limited accuracy. The crucial enabler for this is a reasonably accurate local coordinate system aligned with the global coordinates. We present an algorithm for creating such a coordinate system without the use of global control, globally accessible beacon signals, or accurate estimates of inter-sensor distances. The coordinate system is robust and automatically adapts to the failure or addition of sensors. Extensive theoretical analysis and simulation results are presented. Two key theoretical results are: there is a critical minimum average neighborhood size of 15 for good accuracy and there is a fundamental limit on the resolution of any coordinate system determined strictly from local communication. Our simulation results show that we can achieve position accuracy to within 20 of the radio range even when there is variation of up to 10 in the signal strength of the radios. The algorithm improves with finer quantizations of inter-sensor distance estimates: with 6 levels of quantization position errors better than 10 are achieved. Finally we show how the algorithm gracefully generalizes to target tracking tasks. This paper presents a novel indoor navigation and ranging strategy by using a monocular camera. The proposed algorithms are integrated with simultaneous localization and mapping (SLAM) with a focus on indoor aerial vehicle applications. We experimentally validate the proposed algorithms by using a fully self-contained micro aerial vehicle (MAV) with on-board image processing and SLAM capabilities. The range measurement strategy is inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals. The navigation strategy assumes an unknown, GPS-denied environment, which is representable via corner-like feature points and straight architectural lines. Experimental results show that the system is only limited by the capabilities of the camera and the availability of good corners.