Abstract
A complete centralized processing framework is proposed for human tracking using multistatic radar in the foliage-penetration environment. The configuration of the multistatic radar system is described. Primary attention is devoted to time of arrival (TOA) estimation and target localization. An improved approach that takes the geomet- rical center as the TOA estimation of the human target is given. The minimum mean square error paring (MMSEP) approach is introduced for multi-target localization in the multistatic radar system. An improved MMSEP algorithm is proposed using the maximum velocity limitation and the global nearest neighbor criterion, efficiently decreasing the computational cost of MMSEP. The experimental re- sults verify the effectiveness of the centralized processing framework.
Highlights
Human target detection and tracking have great potential in military, safety, security and entertainment applications [1]
If all sets among Rks, s = 1, 2, 3 are not empty, perform minimum mean square error paring (MMSEP) algorithm for target localization upon remaining measurements and add the positions to Pk. k = k + 1
At successive slow time intervals, it uses global nearest neighbor (GNN) criterion for target association to decrease the computational cost of target localization for all possible combinations in MMSEP
Summary
Human target detection and tracking have great potential in military, safety, security and entertainment applications [1]. Multistatic radar with a low frequency wideband transmitting signal has fine localization precision, a large covered area, and the ability to penetrate the foliage [5], [8]. If the distance between receiving antennas is small enough, the measurement association method proposed in [6] can be applied An efficient method proposed in [14] defines a list of potential targets based on a certain metric (a potential target corresponds to a combination of the measurements from each receiving channel). It avoids resolving the optimum assignment problem and identifies the maximum likelihood targets by re-.
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