This paper demonstrates how the fully adaptive radar framework can be applied to cloud profiling radars. A simulation based on the GEOS5 nature run dataset is introduced in which the cloud profiling radar continuously adapts its pulse repetition frequency (PRF) such that the unambiguous range is 1.2 times the cloud column height. This process maximizes the (PRF) which would in turn maximize the unambiguous velocity estimate.

Collaborative networks of small satellites will form future Earth-observing systems. Maximizing the science value of measurements from such systems will require autonomous decision making with regard to management of limited resources (i.e. power, communications, and sensor configuration). The complexity of this decision space warrants the creation of software tools to aid users in efficient modeling and simulation of collaborative remote sensing networks. In this paper, we present a new open-source software library and tool-set that has been specifically designed for simulating such networks. Details of the object-oriented C++ library are presented with results from example simulations to confirm that it is able to address this challenge. The software tools developed offer enhanced simulation capabilities to developers of future observing system simulation experiments (OSSEs) with collaborative networks of adaptive sensor platforms.

This paper describes the first application of the fully adaptive radar (FAR) framework for cognition to the process of radar imaging. A cognitive radar adapts to its surroundings based on its perceptions of the environment, offering improved performance for a multitude of radar applications. We implemented an autoregressive backprojection (ARBP) imaging technique for the circular synthetic aperture radar (SAR) video within the structure of the FAR framework, allowing the system to adapt its down-range and cross-range resolutions to keep the detected targets visually distinct. This simple demonstration paves the way for more advanced adaptive imaging scenarios in the future. Application of the technique to the GOTCHA volumetric SAR data set demonstrated its capability in a realistic scenario in the presence of clutter and limited target persistence. When applied to the GOTCHA data set, the adaptive imaging system's cumulative executive optimization cost (CEOC), which is used to quantify the overall performance, was 41.3% smaller than the constant, fine resolution case. This significant improvement in CEOC comes at the expense of occasionally failing to meet imaging performance goals as the system adjusts to changes in the environment.

The CubeSat Radiometer Radio Frequency Interference Technology Validation (CubeRRT) mission is developing a 6U CubeSat system to demonstrate radio frequency interference (RFI) detection and filtering technologies for future microwave radiometer remote sensing missions. CubeRRT will perform observations of Earth brightness temperatures from 6-40 GHz using a 1 GHz bandwidth tuned channel and will demonstrate on-board real-time RFI processing. The system is currently under development, with an expected launch date in mid-2018 followed by a one year period of on-orbit operations. CubeRRT spacecraft and radiometer instrument testing as well as the mission concept of operations are described in this paper.

In this paper we discuss the necessary technology required to enable the future of spectrum resource constrained missions. We discuss the CubeSat Radiometer Radio Frequency Interference Technology (CubeRRT) validation mission and the development of its digital backend, necessary for performing on-board RFI detection and filtering for wideband high frequency radiometry. The CubeRRT mission will validate the on-board RFI filtering technology solving technological challenges such as bandwidth, data downlink volume, and RFI types. We present a few initial results of the backend spectrometer leading to full-system integration and test.

Future observing systems simulation experiments (OSSEs) will need to be of increased sophistication to support emerging sensors that exploit parametric adaption, resource management in constrained environments, and the collaboration of distributed sensing nodes. This paper demonstrates the power of using adaptive approaches to remote sensing through a fully adaptive radar imaging example that uses experimental data. In the demonstration, the image resolution varies between 0.47 m and 1.2 m leading to a cost metric savings of 18% and 22% compared to fixed coarse and fine resolution tests. The experimental demonstration is used to inform a discussion of how future OSSEs will need to operate.

The CubeSat Radiometer Radio Frequency Interference Technology Validation (CubeRRT) mission is developing a 6U CubeSat system to demonstrate radio frequency interference (RFI) detection and filtering technologies for future microwave radiometer remote sensing missions. CubeRRT will perform observations of Earth brightness temperatures from 6-40 GHz using a 1 GHz bandwidth tuned channel and will demonstrate on-board real-time RFIS processing. The system is currently under development, with an expected launch date in mid-2018 followed by a one year period of on-orbit operations. Development of the CubeRRT spacecraft, radiometer instrument, and concepts of operation are described in this paper.

Echolocation is the ability to use sound-echoes to infer spatial information about the environment. Some blind people have developed extraordinary proficiency in echolocation using mouth-clicks. The first step of human biosonar is the transmission (mouth click) and subsequent reception of the resultant sound through the ear. Existing head-related transfer function (HRTF) data bases provide descriptions of reception of the resultant sound. For the current report, we collected a large database of click emissions with three blind people expertly trained in echolocation, which allowed us to perform unprecedented analyses. Specifically, the current report provides the first ever description of the spatial distribution (i.e. beam pattern) of human expert echolocation transmissions, as well as spectro-temporal descriptions at a level of detail not available before. Our data show that transmission levels are fairly constant within a 60° cone emanating from the mouth, but levels drop gradually at further angles, more than for speech. In terms of spectro-temporal features, our data show that emissions are consistently very brief (~3ms duration) with peak frequencies 2-4kHz, but with energy also at 10kHz. This differs from previous reports of durations 3-15ms and peak frequencies 2-8kHz, which were based on less detailed measurements. Based on our measurements we propose to model transmissions as sum of monotones modulated by a decaying exponential, with angular attenuation by a modified cardioid. We provide model parameters for each echolocator. These results are a step towards developing computational models of human biosonar. For example, in bats, spatial and spectro-temporal features of emissions have been used to derive and test model based hypotheses about behaviour. The data we present here suggest similar research opportunities within the context of human echolocation. Relatedly, the data are a basis to develop synthetic models of human echolocation that could be virtual (i.e. simulated) or real (i.e. loudspeaker, microphones), and which will help understanding the link between physical principles and human behaviour.

Passive radar (PR) systems must be able to detect the presence of a target signal many orders of magnitude weaker than the direct signal interference (DSI). Due to the continuous nature of most PR signals, this interference, rather than thermal noise, determines the sensitivity of the system. Suppression of DSI and clutter prior to range-Doppler processing is crucial for maximizing the effective dynamic range, to increase detection range and improve overall system performance. A number of time-domain adaptive filtering techniques have been proposed to mitigate the effects of DSI, with varying levels of success. As such, an investigation of the primary factors affecting suppression performance is presented, using Advanced Television Systems Committee digital television (DTV) waveforms as an example, through simulation and extensive experimental trials. A number of spectral and spatially diverse DTV signals are considered to analyze suppression performance under a wide range of realistic scenarios. In particular, the fast block least mean squares filter is shown to provide good suppression performance with low computational requirements. Results of this analysis can be used to predict PR performance and stability. Practical metrics, such as suppression runtime and ease of implementation, also serve to counsel selection of DSI mitigation algorithms for experimental systems.

The CubeSat Radiometer Radio Frequency Interference Technology Validation (CubeRRT) mission is developing a 6U CubeSat system to demonstrate radio frequency interference (RFI) detection and mitigation technologies for future microwave radiometer remote sensing missions. CubeRRT will perform observations of Earth brightness temperatures from 6-40 GHz using a 1 GHz bandwidth tuned channel, and will demonstrate on-board real-time RFI processing. The system is currently under development, with launch readiness expected in 2018 followed by a one year period of on-orbit operations. Project plans and status are reported in this paper.

Nature provides a number of examples where acoustic echolocation is the primary sensing modality, the most well known of these being the bat, whale, and dolphin. All demonstrate a remarkable ability to “see with sound.” Using echolocation, they navigate, locate, and capture prey. As species, they have not only survived but have thrived in all their individual environments, often solely reliant on echolocation. All of these creatures are inherently cognitive. They all maintain a perception of their environment through the nervous system that allows them to take actions. In this paper, we focus on the bat as an example of a cognitive system exploiting a memory-driven perception-action cycle, enabling it to navigate and interact with its environment. The key conceptual components of cognition and how it could be applied to man-made echoic sensors is introduced. This is followed by a description of how echoic flow fields, a bio-inspired technique that bats have been shown to use, fit guidance, and control problems. We then go on to explain how bats are able to reliably distinguish between different targets. A combination of the theory and examples is used to demonstrate the vast potential for advancing the capability of made in man-made systems by adopting aspects of natural echolocating cognitive dynamic systems.

Definitions of cognition almost always use the term “knowing.” Sensors probe the environment producing stimuli that are interpreted to form a perception, but “knowing” is something that takes place within the brain of a human after being presented with sensed data or some form of image. Consider radar and air traffic control. A radar sensor scans the airspace and, using the principles of echolocation, is able to detect, locate, and display aircraft on a screen. An air traffic controller views the screen and continually repositions the aircraft using strict protocols that comply with safety of life requirements. In other words, it is the air traffic controller who supplies the cognitive component necessary to achieve air safety.

An analysis of the relationship between multipath ghosts and the direct target image for radar imaging is presented. A multipath point spread function (PSF) is defined that allows for specular reflections in the local environment and can allow the ghost images to be localized. Analysis of the multipath PSF shows that certain ghosts can only be focused for the far field synthetic aperture radar case and not the full array case. Importantly, the ghosts are shown to be equivalent to direct target images taken from different observation angles. This equivalence suggests that exploiting the ghosts would improve target classification performance, and this improvement is demonstrated using experimental data and a naïve Bayesian classifer. The maximum performance gain achieved is 32%.

In this paper, we investigate the feasibility of uncooperatively and covertly detecting people moving behind walls using passive bistatic WiFi radar at standoff distances. A series of experiments was conducted which involved personnel targets moving inside a building within the coverage area of a WiFi access point. These targets were monitored from outside the building using a 2.4-GHz passive multistatic receiver, and the data were processed offline to yield range and Doppler information. The results presented show the first through-the-wall (TTW) detections of moving personnel using passive WiFi radar. The measured Doppler shifts agree with those predicted by bistatic theory. Further analysis of the data revealed that the system is limited by the signal-to-interference ratio (SIR), and not the signal-to-noise ratio. We have also shown that a new interference suppression technique based on the CLEAN algorithm can improve the SIR by approximately 19 dB. These encouraging initial findings demonstrate the potential for using passive WiFi radar as a low-cost TTW detection sensor with widespread applicability.

A through-the-wall radar image (TWRI) bears little resemblance to the equivalent optical image, making it difficult to interpret. To maximize the intelligence that may be obtained, it is desirable to automate the classification of targets in the image to support human operators. This paper presents a technique for classifying stationary targets based on the high-range resolution profile (HRRP) extracted from 3-D TWRIs. The dependence of the image on the target location is discussed using a system point spread function (PSF) approach. It is shown that the position dependence will cause a classifier to fail, unless the image to be classified is aligned to a classifier-training location. A target image alignment technique based on deconvolution of the image with the system PSF is proposed. Comparison of the aligned target images with measured images shows the alignment process introducing normalized mean squared error (NMSE) ≤ 9%. The HRRP extracted from aligned target images are classified using a naive Bayesian classifier supported by principal component analysis. The classifier is tested using a real TWRI of canonical targets behind a concrete wall and shown to obtain correct classification rates ≥ 97%.