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.

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.

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The multi-input multi-output (MIMO) communication framework is adopted for wireless sensor networks by having multiple sensors equipped with single-element antennas cooperate in transmission. A power method-based iterative algorithm is developed that computes the optimal transmit and receive eigen-filters distributively among the sensors while transferring most of the computational burden to the central collector node. Since the proposed algorithm implicitly exploits the channel state information (CSI) both at the receiver and the transmitter, it is expected that the resulting spectral efficiency is higher than what can be achieved by receive CSI-only space-time coding. This intuition is confirmed by employing a variable-rate adaptive modulation scheme for the eigen-transmission and comparing its spectral efficiency with that of orthogonal space time block codes (OSTBCs) at specific target bit error rates. The performance is also evaluated using realistic channel estimation as well as the least mean square (LMS) and recursive least square (RLS) algorithms for iterative eigencoding.

Orthogonal frequency-division multiplexing . (OFDM) has been utilized in various communication sysfems because of its ability to compensate for niultipath channels using simple low-complexity equalizers. However: the performance of OFDMsysfertts can still be compromised due to adjacent channel and cochannel interjerence. In this paper.we investigate the performance of an adaptive beanforming algorithm for an OFDM system based on the constant modulus (CM) array. We consider this blind technique for a wireless local area nehvork such as the IEEE 802.IIa standard. The algorithm exploits the constant ntodulusproperfy of the low-rare OFDM data carriers, and we show that it can also be used for the nonconstanf modulus constellations at higher data rates. We consider the case where cochannel users become active part way into the transmitted packet such that new beanformer weights should be computed to null these interfering signals.

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