Conventional wireless communication is built upon hardware-based signal processing. This enables high performance, but is inflexible as the signal-processing algorithms are “baked in” to the hardware. Software-defined radio (SDR) is an emerging solution in which more of the signal-processing logic is implemented in software instead of hardware. This allows for adaptability to spectrum conditions (e.g., jamming or congestion), changes to protocols, and software updates that improve signal-processing logic – features that are beneficial in many consumer and military applications. However, the high sampling rate $(\text{kHz}$ to MHz or faster) of many SDR applications poses significant challenges for real-time scheduling of such workloads. To manage high sampling rates on general-purpose processors, which process samples sequentially instead of in parallel, as can be done using hardware acceleration, samples must be buffered, or “batched” together, to minimize overheads and maximize locality. To address this characteristic of high-frequency signal processing, this paper presents an extension of traditional real-time scheduling models called the marginal cost model, which reflects the fact that when batching many samples, the marginal cost of processing additional samples is often much less than the cost of processing the first sample. Empirical evaluations are presented from the open source GNU Radio SDR framework to validate the marginal cost model. Experiments are then presented that demonstrate the trade-offs between batching and worst-case latency for synthetic SDR workloads. Finally, a case study is presented to demonstrate the utility of the presented model and batching techniques in real-world signal-processing applications.

For outdoor navigation, GPS provides the most widely-used means of node localization; however, the level of accuracy provided by low-cost receivers is typically insufficient for use in high-precision applications. Additionally, many of these applications do not require precise absolute Earth coordinates, but rather rely on relative positioning to infer information about the geometric configuration of the constituent nodes in a system. This paper presents a novel approach that uses GPS to derive relative location information for a scalable network of single-frequency receivers. Networked nodes share their raw satellite observations, enabling each node to localize its neighbors in a pairwise fashion as opposed to computing its own standalone position. Random and systematic errors are mitigated in novel ways, challenging long-standing beliefs that precision GPS systems require extensive stationary calibration times or complex equipment configurations. In addition to presenting the mathematical basis for our technique, a working prototype is developed, enabling experimental evaluation of several real-world test scenarios. The results of these experiments indicate sub-meter relative positioning accuracy under various conditions and in varying environments. This represents up to order of magnitude increase in precision over existing absolute positioning techniques or other unimodal GPS-based solutions.

One of the primary limitations to widescale adoption of many of today's high-precision location-aware applications is the cost associated with achieving the levels of accuracy necessary for safe and reliable operation. We have developed a relative localization solution enabling centimeter-scale positioning of a network of mobile nodes. The success of this research in the relative sense can be coupled with the known locations of a set of stationary nodes as well as the computational power of the cloud to open the door for an absolute localization service, whereby base stations at well-known locations can be used to transform relative coordinates into absolute locations. We will demonstrate the current progress of our work via live walking and driving experiments in which observers in an exhibition hall can issue commands to walkers and drivers in the field and see the results of these commands as determined by our positioning system in real-time.

In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This paper presents an approach that uses GPS to derive relative location information for multiple receivers. Nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. The system has been implemented using a network of Android phones equipped with a custom Bluetooth headset and integrated GPS chip to provide raw measurement data. Our evaluation shows that centimeter-scale tracking accuracy at an update rate of 1 Hz is possible under various conditions with the presented technique. This is more than an order of magnitude more accurate than simply taking the difference of reported absolute node coordinates or other simplistic approaches due to the presence of uncorrelated measurement errors.

In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This video demonstration presents an approach that uses GPS to derive relative location information for multiple receivers. Cooperating nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. This is achieved via creation of a new error model that incorporates GPS localization errors specific to the multiple-receiver case, development of a new, highly accurate observation model that allows for the change in range between a single satellite and two receivers to be mapped through time, and the synthesis of these two models into a novel 3D pairwise tracking algorithm that uses the local GPS node as its own reference and requires only an approximate estimate of its own absolute position to achieve centimeter-scale relative tracking precision.