Laser Range Sensors

This paper presents the design aspects of laser range finders and proximity sensors beingdeveloped at IRDE for different applications. The principle used in most of the laser rangefinders is pulse echo or time-of-flight measurement. Optical triangulation is used in proximitysensors while techniques like phase detection and interferometry are employed in instrumentsfor surveying and motion controllers where high accuracy is desired. Most of the laser rangefinders are designed for ranging non-cooperative targets

Characterization of modulated time-of-flight range image sensors

A number of full field image sensors have been developed that are capable of simultaneously measuring intensity and distance (range) for every pixel in a given scene using an indirect time-of-flight measurement technique. A light source is intensity modulated at a frequency between 10–100 MHz, and an image sensor is modulated at the same frequency, synchronously sampling light reflected from objects in the scene (homodyne detection). The time of flight is manifested as a phase shift in the illumination modulation envelope, which can be determined from the sampled data simultaneously for each pixel in the scene. This paper presents a method of characterizing the high frequency modulation response of these image sensors, using a pico-second laser pulser. The characterization results allow the optimal operating parameters, such as the modulation frequency, to be identified in order to maximize the range measurement precision for a given sensor. A number of potential sources of error exist when using these sensors, including deficiencies in the modulation waveform shape, duty cycle, or phase, resulting in contamination of the resultant range data. From the characterization data these parameters can be identified and compensated for by modifying the sensor hardware or through post processing of the acquired range measurements

Distributed Detection in Sensor Networks with Limited Range Sensors

We consider a multi-object detection problem over a sensor network (SNET) with limited range sensors. This problem complements the widely considered decentralized detection problem where all sensors observe the same object. While the necessity for global collaboration is clear in the decentralized detection problem, the benefits of collaboration with limited range sensors is unclear and has not been widely explored. In this paper we develop a distributed detection approach based on recent development of the false discovery rate (FDR). We first extend the FDR procedure and develop a transformation that exploits complete or partial knowledge of either the observed distributions at each sensor or the ensemble (mixture) distribution across all sensors. We then show that this transformation applies to multi-dimensional observations, thus extending FDR to multi-dimensional settings. We also extend FDR theory to cases where distributions under both null and positive hypotheses are uncertain. We then propose a robust distributed algorithm to perform detection. We further demonstrate scalability to large SNETs by showing that the upper bound on the communication complexity scales linearly with the number of sensors that are in the vicinity of objects and is independent of the total number of sensors. Finally, we deal with situations where the sensing model may be uncertain and establish robustness of our techniques to such uncertainties.Comment: Submitted to IEEE Transactions on Signal Processin

Dynamic range optimisation of CMOS image sensors dedicated to space applications

Nowadays, CMOS image sensors are widely considered for space applications. Their performances have been significantly enhanced with the use of CIS (CMOS Image Sensor) processes in term of dark current, quantum efficiency and conversion gain. Dynamic Range (DR) remains an important parameter for a lot of applications. Most of the dynamic range limitation of CMOS image sensors comes from the pixel. During work performed in collaboration with EADS Astrium, SUPAERO/CIMI laboratory has studied different ways to improve dynamic range and test structures have been developed to perform analysis and characterisation. A first way to improve dynamic range will be described, consisting in improving the voltage swing at the pixel output. Test vehicles and process modifications made to improve voltage swing will be depicted. We have demonstrated a voltage swing improvement more than 30%. A second way to improve dynamic range is to reduce readout noise A new readout architecture has been developed to perform a correlated double sampling readout. Strong readout noise reduction will be demonstrated by measurements performed on our test vehicle. A third way to improve dynamic range is to control conversion gain value. Indeed, in 3 TMOS pixel structure, dynamic range is related to conversion gain through reset noise which is dependant of photodiode capacitance. Decrease and increase of conversion gain have been performed with different design techniques. A good control of the conversion gain will be demonstrated with variation in the range of 0.05 to 3 of initial conversion gain

Sensor Scheduling for Energy-Efficient Target Tracking in Sensor Networks

In this paper we study the problem of tracking an object moving randomly through a network of wireless sensors. Our objective is to devise strategies for scheduling the sensors to optimize the tradeoff between tracking performance and energy consumption. We cast the scheduling problem as a Partially Observable Markov Decision Process (POMDP), where the control actions correspond to the set of sensors to activate at each time step. Using a bottom-up approach, we consider different sensing, motion and cost models with increasing levels of difficulty. At the first level, the sensing regions of the different sensors do not overlap and the target is only observed within the sensing range of an active sensor. Then, we consider sensors with overlapping sensing range such that the tracking error, and hence the actions of the different sensors, are tightly coupled. Finally, we consider scenarios wherein the target locations and sensors' observations assume values on continuous spaces. Exact solutions are generally intractable even for the simplest models due to the dimensionality of the information and action spaces. Hence, we devise approximate solution techniques, and in some cases derive lower bounds on the optimal tradeoff curves. The generated scheduling policies, albeit suboptimal, often provide close-to-optimal energy-tracking tradeoffs