Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of these sensors can provide remarkably good target tracking performance. In this article, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings over time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models of trajectories yield respectable tracking performance.

Cooperative MIMO is a new technique that allows disjoint wireless communication nodes (e.g. wireless sensors) to form a virtual antenna array to increase bandwidth, reliability and/or transmission distance. It differs fundamentally from other MIMO communication since the signals received from each node have a relative timing and frequency offset due to the distributed nature of their transmitting antennas. Therefore, the receiver must estimate the timing and frequency for each transmitting node, in addition to the MIMO channel. In this paper, we design and implement a receiver for the cooperative MIMO problem using reconfigurable hardware. We discuss the computation required for each stage of the receiver and perform experimental study of the tradeoffs between area, power, performance and quality of results. The end result is an efficient, parameterizable, cooperative MIMO receiver implemented on several different state-of-the-art FPGAs devices.

Recent work has shown that, despite the minimal information provided by a binary proximity sensor, a network of such sensors can provide remarkably good target tracking performance. In this paper, we examine the performance of such a sensor network for tracking multiple targets. We begin with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. We provide necessary and sufficient criteria for an accurate target count in a one-dimensional setting, and provide a greedy algorithm that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings across time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. An extensive set of simulations, as well as experiments with passive infrared sensors, are reported. We conclude that, despite the combinatorial complexity of target counting, probabilistic approaches based on fairly generic models for the trajectories yield respectable tracking performance.

The authors consider a system architecture whereby orthogonal space time block codes (OSTBCs) are applied to a distributed set of wireless nodes. The utility offered is that by employing diversity we are able to greatly reduce the necessary link margin usually required to combat fast fading. A distributed set of wireless nodes are particularly well suited for this task as they are typically separated with significant inter-node distance thus having highly uncorrelated channels to a collector. In a typical application, a wireless sensor network (WSN) will have nodes that may wish to transmit data to a possibly mobile standoff collection point. In this paper, the authors discuss many practical aspects necessary for a real-world implementation including time and frequency offset estimation as well as channel tracking. As the waveform has been targeted to field programmable gate array (FPGA) hardware, we look at the relative implementation complexity of each signal processing element.

A cooperative MIMO network is considered with Ns sensors and a collector node with Mc antennas. In a practical implementation of this network, the sensor carriers have relative frequency offsets which must be estimated along with the MIMO channel. Generalized successive interference cancellation (GSIC) is proposed for this joint estimation problem. The primary operations in GSIC are correlation, FFT and cancellation. A reconfigurable hardware (FPGA) implementation of these GSIC primitives is described. A hybrid analysis/simulation for bit error rate (BER) is presented with results for GSIC using Alamouti and G4ccodes.

In this paper, we describe amplitude estimation algorithms for the Walsh traffic channels in the downlink of Interim Standard 95 (IS-95). The parameter estimate is designed to improve the performance of a successive interference canceller (SIC) by providing greater reconstruction accuracy. A feature of the IS-95 downlink that makes amplitude estimation difficult is that during lowered voice activity levels, bit repetition occurs with a corresponding drop in the symbol energy for a frame of data. As a result, the amplitude level cannot be assumed to remain constant and should be taken into account during signal reconstruction in the SIC. We explore various algorithms for estimating the nominal Walsh traffic channel amplitude level. The distinguishing characteristic of the different techniques is the type of a priori knowledge employed in the estimation. For example, one parameter that may need to be estimated is the bit repetition multiple using a rate determination algorithm.